Ionospheric Detection of Explosive Events

Abstract

The ionospheric response to explosions which occur at or below the Earth's surface has been noted since the first detonations of nuclear devices during the early period of aboveground testing. Acoustic gravity waves and traveling ionospheric disturbances were detected in association with test explosions carried out by the Union of Soviet Socialist Republics in Novaya Zemlya in 1961. While research in this area has continued, the standards accepted by the Comprehensive Nuclear Test Ban Treaty for detection and confirmation of nuclear explosions have been based on (1) seismic, (2) hydroacoustic, (3) infrasound, and (4) radionuclide monitoring from ground detectors. We suggest that ionospheric sensing offers a complementary methodology that may allow for robust confirmation of explosive events. One method of ionospheric monitoring of explosive events is analysis of total electron content (TEC), available by processing data from Global Navigation Satellite System (GNSS) receivers distributed globally on land masses. Traveling ionospheric disturbances observed by their signature in TEC have been used to detect and confirm mine collapses, mine blasts, earthquakes, volcanic eruptions, and meteorite strikes as well as underground nuclear tests. While an integrated measurement like TEC is not as sensitive to small‐amplitude density perturbations as other methods, the existence of large networks of continuously operating GNSS stations makes this an intriguing new monitoring asset. We report on the current capabilities for detection of explosions via the ionosphere, the outstanding challenges, and prospects for future developments that have potential to augment the current standards for detection and confirmation of nuclear detonations. By leveraging the worldwide network of GNSS receivers, robust confirmation of impulsive events is possible. This methodology complements existing technologies approved by the Comprehensive Test Ban Treaty. The article outlines background theory, new approaches to monitoring of explosions, and challenges that remain to be addressed.

Full text

Ionospheric Detection of Explosive Events
C. Y. Huang
1
, J. F. Helmboldt
2
, J. Park
3
, T. R. Pedersen
1
, and R. Willemann
1
1
Space Vehicles Directorate, Air Force Research Laboratory, Kirtland Air Force Base, NM, USA,
2
Naval Research
Laboratory, Washington, DC, USA,
3
Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA
Abstract The ionospheric response to explosions which occur at or below the Earth's surface has been
noted since the first detonations of nuclear devices during the early period of aboveground testing.
Acoustic gravity waves and traveling ionospheric disturbances were detected in association with test
explosions carried out by the Union of Soviet Socialist Republics in Novaya Zemlya in 1961. While research
in this area has continued, the standards accepted by the Comprehensive Nuclear Test Ban Treaty for
detection and confirmation of nuclear explosions have been based on (1) seismic, (2) hydroacoustic, (3)
infrasound, and (4) radionuclide monitoring from ground detectors. We suggest that ionospheric sensing
offers a complementary methodology that may allow for robust confirmation of explosive events. One
method of ionospheric monitoring of explosive events is analysis of total electron content (TEC), available by
processing data from Global Navigation Satellite System (GNSS) receivers distributed globally on land
masses. Traveling ionospheric disturbances observed by their signature in TEC have been used to detect and
confirm mine collapses, mine blasts, earthquakes, volcanic eruptions, and meteorite strikes as well as
underground nuclear tests. While an integrated measurement like TEC is not as sensitive to small‐amplitude
density perturbations as other methods, the existence of large networks of continuously operating GNSS
stations makes this an intriguing new monitoring asset. We report on the current capabilities for detection of
explosions via the ionosphere, the outstanding challenges, and prospects for future developments that have
potential to augment the current standards for detection and confirmation of nuclear detonations. By
leveraging the worldwide network of GNSS receivers, robust confirmation of impulsive events is possible.
This methodology complements existing technologies approved by the Comprehensive Test Ban Treaty. The
article outlines background theory, new approaches to monitoring of explosions, and challenges that remain
to be addressed.
1. Introduction
Aboveground nuclear explosions have been detected by ionospheric monitors since 1958, the International
Geophysical Year. Nuclear tests were carried out in the Pacific, on Johnston Island in the North Pacific
Ocean southwest of Hawaii, in August 1958. Artificial aurora were observed at Maui, Hawaii, several
hundred kilometers NE of Johnston, and at Apia, Samoa, conjugate to Johnston (Cullington, 1958).
Electrical conductivity increased by 1–2 orders of magnitude at the 80‐to 100‐km altitude range owing to
ionization caused by energetic particles and X‐rays. This was followed by strong radio absorption
(Matsushita, 1959). The locations of Johnston Island and the observatories where ionospheric responses
were detected are shown in Figure 1. Disturbances which propagated large distances and caused changes
in ionospheric electron densities were detected as far away as Brisbane, Australia (Bowman, 1962). From
times of arrival at Brisbane, the speeds of propagation of the disturbances were deduced to range from
220 to 840 m/s, approximately the sound speed at the altitude of the detonation.
During 1961, a series of aboveground nuclear tests were carried out over both of the test sites used by the
Soviet Union: Novaya Zemlya and Semipalatinsk, Kazakhstan. Ionospheric disturbances were detected
globally and reported in a series of publications (Beynon & Jones, 1962; Dieminger & Kohl, 1962; Rose
et al., 1961, and others). The propagation of the perturbations was attributed to atmospheric waves, in
particular acoustic gravity waves (AGWs) (Hines, 1960; Obayashi, 1962), with wave periods of several
minutes. As we discuss below, AGWs couple to the ionosphere and generate traveling ionospheric
disturbances (TIDs) which were detected in association with the tests on Johnston Island and Novaya
Zemlya (Breitling et al., 1967; Hines, 1967). A study of variations in the electron density peak in the Flayer
measured by ionosondes distributed around the world indicated propagation velocities between 50 and
900 m/s (Breitling et al., 1967), corresponding to a mix of shock and acoustic waves.
©2019. American Geophysical Union.
All Rights Reserved.
This article has been contributed to by
US Government employees and their
work is in the public domain in the
USA.
REVIEW ARTICLE
10.1029/2017RG000594
Key Points:
•Detection of aboveground and
belowground explosive events is
possible by ionospheric monitoring
•Methodology leverages global GNSS
availability, but attribution and
validation are outstanding
challenges
•Ionospheric observations coupled
with numerical simulations can add
robust confirmation to traditional
methods of detection
Correspondence to:
C. Y. Huang,
afrl.rvborgmailbox@us.af.mil
Citation:
Huang, C. Y., Helmboldt, J. F., Park, J.,
Pedersen, T. R., & Willemann, R.
(2019). Ionospheric Detection of
Explosive Events. Reviews of Geophysics,
57,78–105. https://doi.org/10.1029/
2017RG000594
Received 19 JAN 2018
Accepted 15 JAN 2019
Accepted article online 21 JAN 2019
Published online 11 FEB 2019
HUANG ET AL. 78

There have been significant changes to nuclear testing and detection since the early days of nuclear tests.
Aboveground testing was banned in 1963 by the Limited Test Ban Treaty, leading to belowground tests.
Testing in underground cavities results in significantly smaller atmospheric wave amplitudes since the
shock wave does not reach the surface (Herbst et al., 1961; Latter et al., 1961), making detection more elu-
sive. Nevertheless, it has long been known that TIDs are generated by at least some crustal earthquakes with-
out ruptures that reach the surface (Calais & Minster, 1995). A second change is a large increase in the
number of ionospheric observations available on a regular basis. The main contributors to ionospheric elec-
tron density measurements are large networks of Global Navigation Satellite System (GNSS) stations, such
as the freely available International GNSS Service (http: www.igs.org/; Dow et al., 2009). From the total elec-
tron content (TEC) derived from GNSS data, perturbations in electron density corresponding to TIDs can be
extracted. This has been done successfully (Park et al., 2011; Yang et al., 2012) for the 2006 and 2009 under-
ground nuclear explosions (UNEs) carried out by North Korea.
The wide availability of GNSS data enables nearly continuous global monitoring of the ionosphere and, in
principle, global monitoring of explosive events. In addition to nuclear tests, other impulsive events (earth-
quakes, mining explosions, rocket launches, volcano eruptions, meteorite and bolide entry, and others) gen-
erate waves that couple to AGWs and TIDs (Berngardt et al., 2015; Calais & Minster, 1995; Fitzgerald &
Carlos, 1997; T. B. Jones & Spracklen, 1974). TIDs are also detected in the ionosphere for a large range of
natural phenomena including high‐latitude magnetic activity (Hocke & Schlegel, 1996; Hunsucker, 1982;
Richmond, 1978), tropospheric forcing (Forbes, 1995), and tsunamis and other ocean surface waves
(Galvan et al., 2012; Vadas et al., 2015; Wu et al., 2016). In regions where GNSS measurements are dense,
high‐resolution mapping of TIDs can be carried out (Otsuka et al., 2013; Saito et al., 1998) showing the spa-
tial structure and temporal evolution of the waves.
Figure 1. Schematic of test location on Johnston Island and locations of geomagnetic (O) and ionospheric (Δ) stations in the Pacific and on the American continent.
The geographic, magnetic, and geomagnetic equators and the 260°E meridian are shown (figure and caption reproduced from Matsushita, 1959).
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Figure 2 illustrates the basic concept of ionospheric responses to natural and manmade explosive events and
possible methods for detection. The explosions and earthquakes are shown over the green land mass, and
tsunamis in the ocean generated by earthquakes in the ocean at lower right. Explosive events generate seis-
mic waves that propagate at the Earth's surface. These are illustrated as dark circles over land. In addition,
surface waves due to the impulsive events give rise to shock and acoustic waves shown as light concentric
circles propagating upward through the lower atmosphere. When these waves intersect with the ionosphere,
gravity and acoustic waves generate TIDs illustrated as dark bands in the Fregion of the ionosphere. TIDs
create perturbations in GNSS observations, symbolized by yellow zigzags, which can be observed by recei-
vers, sounders, and radio telescope arrays on the ground. In addition, the effects of TIDs generated in the
conjugate hemisphere can be detected remotely. These new technical means combined with increasing ten-
sions between nuclear powers stimulated this review of current and future methodologies for detection of
explosive events. The remainder of this paper is organized into sections on the Comprehensive Nuclear
Test Ban Treaty (CTBT), atmospheric effects, ionospheric effects, innovative techniques, challenges,
and summary.
2. The CTBT
The CTBT was opened for signature in 1996 and was signed by 183 member states of the United Nations as of
September 2017. The CTBT is comprehensive both in the sense that it bans tests everywhere—in space, in
the atmosphere, in the oceans, and underground—and in the sense that it bans any test involving an uncon-
trolled nuclear chain reaction, down to zero yield (Dahlman et al., 2009).
The CTBT has not yet entered into force because several of the states listed in its Annex 2 have not yet ratified
the treaty. Nevertheless, the United Nations General Assembly authorized the CTBT Preparatory
Commission (PrepComm) to organize a Provisional Technical Secretariat (PTS), which is funded by contri-
butions from the CTBT States Signatory to build and operate a worldwide monitoring system.
On behalf of the PrepComm, the PTS is required by the CTBT to use equal diligence in monitoring all geo-
graphic regions of the world and to use only the technologies that are described in the CTBT. The PTS col-
lects data from the International Monitoring System (IMS)—composed of hundreds of stations around the
world that it has built or modified—and analyzes the data at its International Data Center (IDC) to
Figure 2. Schematic illustrating ionospheric response to natural and manmade explosive events. Explosions on land are
shown at bottom left and earthquakes at bottom center; tsunamis due to earthquakes are shown in the ocean at bottom
right. The surface and seismic waves on land are shown as concentric dark circles over land. The tsunami waves are
shown as dark ripples in the ocean. The disturbances generate shock and acoustic and gravity waves which are illustrated
as light circles propagating through the atmosphere. When the waves reach the ionosphere, they generate traveling
ionosphere disturbances (TIDs) shown as dark bands in the Fregion of the ionosphere. These perturb signals from GNSS
satellite and galactic radio sources. The perturbations, illustrated by yellow zigzags, can be detected on the ground by
GNSS receivers, high‐frequency sounders, and radio telescopes. Remote detection of waves generated in the conjugate
hemisphere can also be observed.
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determine the locations and sizes of the events that produced signals in the IMS data. The IDC prepares a
“screened”list from which it excludes events that are almost certainly not nuclear tests, based on criteria
prescribed in the CTBT and updated with the approval of the PrepComm. Despite the existence of screening
criteria, the CTBT does not authorize either the PTS or the PrepComm to announce conclusions on the nat-
ure of any of the events that it detects (CTBTO, 2012).
Locations of the 321 monitoring stations that will comprise the complete IMS are listed in the CTBT and
updated by the PrepComm in the event of unforeseen circumstances. A large majority of the IMS stations
—including nearly all of the seismic and hydroacoustic stations—have been completed, certified for use
in analysis by the PTS, and are sending data to the IDC.
The four monitoring technologies named in the CTBT and used by the PTS are seismic, infrasonic, hydroa-
coustic, and radionuclide. Among these, only radionuclide data provide a “smoking gun”positively confirm-
ing that an event involved an uncontrolled nuclear chain reaction. Radionuclide monitoring is not effective
if an explosion is fully contained and there is no release of gas or particulates. The effectiveness of the IMS in
detecting nuclear tests in different settings has been the topic of many studies and several comprehensive
reviews (National Research Council, 2012).
The hydroacoustic component of the IMS is very likely to detect even the smallest possible nuclear explosion
anywhere in the oceans. In addition, a nuclear test in the ocean would be detected by both the seismic and
radionuclide components of the IMS and by monitoring systems operated by several states signatory to
the CTBT.
The infrasonic component of the IMS has a threshold for detecting nuclear explosions in the atmosphere that
varies geographically and seasonally with changes in stratospheric winds. Nevertheless, infrasonic monitor-
ing is effective against realistic scenarios of nuclear testing in the atmosphere, and it is supplemented by
radionuclide monitoring, as well as satellite‐based monitoring systems operated by several countries.
There is concern that an underground nuclear test could evade detection by the CTBT Organization because,
at least in some geographic regions, the detection threshold of the IMS misses the smallest possible test. Even
if an underground test is detected, IMS data may not be sufficient to distinguish a small test from other “con-
founding”seismic events, which include naturally occurring earthquakes, induced earthquakes, and mining
explosions. Among the confounding seismic events, naturally occurring earthquakes are by far the
most numerous.
One of the criteria for distinguishing between naturally occurring earthquakes and underground nuclear
tests is the depth of the event below the Earth's surface. For technological reasons, nuclear tests typically
are conducted at depths of no more than a few hundred meters, but very nearly all naturally occurring earth-
quakes occur at depths of at least several kilometers. Indeed, depth confidently determined to be greater
than 3 km is one of the criteria used by the PTS to remove an event from its screened list. But event depth
is often poorly constrained by IMS seismic data—so much so that the depths of many earthquakes are fixed
at the surface—so many naturally occurring earthquakes remain in the screened event list (Fisk et al., 2002).
Explosions at the surface—such as from some types of mining operations—can generate infrasonic signals,
but the amplitude of infrasonic signals falls off very rapidly with depth for seismic events, including under-
ground explosions and earthquakes. Thus, the existence of a large‐amplitude infrasonic signal correlated
with a seismic event location would be a strong indication of an event at the surface or at a very shallow
depth. That is, almost all naturally occurring earthquakes are expected to produce at most very small infra-
sonic signals compared to underground explosions with a comparable seismic magnitude.
3. Atmospheric Effects
As noted in section 1, the reaction of the neutral atmosphere to nuclear explosions has been observed since
the early days of aboveground testing. During the Novaya Zemlya test of October 1961, pressure waves were
observed by microbarographs in England (Carpenter et al., 1961; Rose et al., 1961), comparable with pres-
sure waves observed after the Krakatoa volcano eruption of 27 August 1853 (R. V. Jones, 1962). The mean
velocity was approximately 310 m/s, with wavelength of about 100 km (Obayashi, 1962). The disturbance
was assumed to be atmospheric waves affected by compressional and gravitation forces.
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The basic interpretation of observations leading to the widely accepted hypothesis that the waves emitted by
explosive events are atmospheric gravity waves was given by Hines (1960). A review of the rigorous mathe-
matical basis underlying discussion of wave properties is given by Yeh and Liu (1974). While not all atmo-
spheric waves are AGWs, the bulk of the experimental observations can be explained by this hypothesis,
in particular those following aboveground nuclear testing (Hines, 1967; Row, 1967).
There are two distinct classes of waves emanating from explosive events, predominantly gravitational or pre-
dominantly acoustic. The first class restricts wave propagation to the horizontal surface with no phase pro-
pagation in the vertical direction. These surface waves are excluded from this review on the basis of wave
observations in the atmosphere which show vertical propagation. The second class of waves is described
by Hines (1960) as “internal”acoustic waves (AWs). On the basis of meteor height wind observations,
Hines deduced the properties of these waves.
The dynamic equation describing an idealized (isothermal) atmosphere as a fluid medium is
DW
Dtþ2Ω×WðÞ¼−
1
ρ∇pþgþF(1)
where Wis the velocity, D/Dtis the total derivative = ∂/∂t+(W.∇); Ωis the Coriolis force; gis the gravita-
tional force; Fincludes all other forces relevant in the upper atmosphere above 70 km, primarily viscosity
and hydromagnetic forces (Kato, 1980). This will be discussed in more detail below.
If we assume that the atmosphere is in hydrostatic equilibrium with variations only in the x(horizontal) and
z(vertical) directions, the linearized equations of motion for the gas give the dispersion relation
ω2−ωa2

þωg2
ω2
−1

ks2Cs2−k0
z
2Cs2¼0 (2)
This assumes that the wave variations in space and time can be represented by exp[i(wt −k
x
x−k
z
z)]. wand
kare the wave frequency and number, respectively; the subscript srefers to sound waves; k
z
′=k
z
+iH;His
the density scale height; C
s
is the sound speed; ω
a
is the acoustic cutoff frequency ωa¼γg
2Cs;γis the specific
heat ratio; ω
g
is the Väisälä‐Brunt or buoyancy frequency ω
g
=(γ−1)
1/2
g/C
s
. Typically, ω
a
/2π= 3.3 mHz
and ω
g
/2π= 2.9 mHz in the lower atmosphere.
There are two real propagating solutions: acoustic modes (ω>ω
a
) which are compressional waves, and grav-
ity modes (ω<ω
g
) which are dominated by buoyancy (Artru et al., 2004; Hines, 1960).
The frequency regimes are illustrated in Figure 3 which is taken from Artru et al. (2004). At left, the separa-
tion of the two modes by frequency is apparent, with the two cutoff frequencies, ω
a
and ω
g
, indicated.
Figure 3. (left panel) Domains of existence of acoustic and gravity waves as a function of frequency and angular order.
There is a clear separation in frequency between the two classes of waves, indicated by the acoustic cutoff frequency,
here ω
a
= 3.68 mHz. Seismic waves couple with upward propagating acoustic waves for frequencies higher than the
acoustic cutoff frequency. (right panel) Seismic waves. Figure and caption based on Figure 1, Artru et al. (2004).
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Seismic modes are also shown in the figure as a function of energy at right. Dispersion surfaces of the pro-
pagating internal atmospheric gravity waves, shown in Figure 9 of Hines (1960), illustrate the anisotropic
nature of the waves, with group and phase velocities generally propagating in different directions. Two
important points that arise from the dispersion relation are that (1) wave energy is transported upward with
group velocity (Yeh & Liu, 1974), and (2) phase fronts of the waves have a component that is inclined down-
ward, opposite the direction of upward energy flow (Hines, 1960). Overall, the phase fronts appear to be
tilted in the forward direction when viewed from a single observation point.
Internal atmospheric gravity waves increase in amplitude with upward propagation, in proportion to exp(γg/
2C
s2
)z, where zis the vertical coordinate. This can be understood in terms of energy flux, since atmospheric
density decreases in proportion to exp (−γg/2C
s2
)z. Countering the amplification are damping effects due to
energy dissipation. These are basically molecular viscosity and thermal conductivity. The energy dissipation
is proportional to exp(−2αdz) where αis the attenuation due to molecular viscosity and thermal conduction.
A simplified expression for the attenuation was derived by Hines (1960).
2πηkz2ωg2=ω2¼1 (3)
where ηis the kinematic viscosity. This assumes that k
z2
≫ω
a2
/C
s2
.
From this equation, it can be seen that the effect of energy dissipation is to remove higher‐frequency waves
as they propagate upward. It is generally accepted that viscosity and thermal conductivity are approximately
equal in magnitude in the upper atmosphere (Blanc, 1985). Wave amplitudes reach a maximum value before
being damped within a short altitude range which is frequency dependent. For waves at 1 Hz, this altitude is
approximately 110 km, and for waves of 0.1 Hz, it is 150 km (Blanc, 1985). Thus, altitude provides a natural
filtering effect in detectable waves. Attenuation is particularly effective about 90 km at higher frequencies
(1–10 Hz), while a frequency window (5–10 mHz) exists for which viscous damping is inefficient (Blanc,
1985). A second effect proposed by Hines (1960) due to ion drag, also described as ohmic losses or magneto-
hydrodynamic absorption, has the effect of dissipating waves with periods longer than an hour (Francis,
1975), narrowing down the frequency range of atmospheric gravity waves which can propagate upward to
the ionosphere.
If the initial assumption of an idealized atmosphere is dropped, and thermal gradients are included, addi-
tional terms which cause reflection and/or ducting of the atmospheric waves must be considered
(Pitteway & Hines, 1965). Reflection and refraction of waves at sharp gradients in density, temperature, or
a wind shear can occur at a range of altitudes, further complicating the predictions of wave propagation
(Hines & Reddy, 1967; Yeh & Liu, 1974).
AWs and gravity waves (GWs) are further separated by their propagation characteristics. AWs propagate iso-
tropically outward, while GWs propagate primarily in the horizontal direction. There is a class of AWs that
arises when the wave frequency is comparable with the Väisälä‐Brunt frequency. Under these conditions,
the buoyancy force is comparable with the gravitational force, and the waves, while compressional, exhibit
anisotropic or oblique propagation. These waves are described as AGWs.
Different propagation characteristics of AWs and GWs allow us to distinguish between naturally occurring
internal GWs such as traveling atmospheric disturbances which typically propagate horizontally from high
to low latitudes as a result of Joule heating (Hunsucker, 1982; Richmond, 1978), from AGWs generated by
explosions that propagate obliquely outward from the explosion site (Hines, 1967).
AWs are detectable as pressure variations as is done for infrasound detection (see preceding section), routi-
nely monitored as part of the CTBT protocols. An automatic system for infrasound detection and determina-
tion of source location are described by Park, Grejner‐Grzezinska, et al. (2014). The results illustrate the
methodology for detection, as well as modeling of ray paths in order to infer the sources of wave generation.
Figure 4, taken from this study, illustrates the observations made from the dense network of stations in the
western United States in response to detonations made at known source locations, with red bars indicating
the times of detonation. The velocity of the AWs ranged from 280 to 380 m/s, and arrival times at infrasound
monitors are illustrated by green bars.
While the amount of energy radiated into the atmosphere is not large, there are relatively few competing
wave sources with compatible frequency and horizontal wavelength (Artru et al., 2004). The topic of
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HUANG ET AL. 83

seismic waves and their coupling to the ionosphere and atmosphere has been explored in detail, from
proposed observational experiments (Rapoport et al., 2004), to consideration of nonlinear and oblique
propagation (Ostrovsky, 2008), to detection of waves as precursors to earthquakes (Hayakawa et al., 2012;
Rozhnoi et al., 2007), and many other studies beyond the scope of this review. We mention these papers
to point out that ionoseismic earthquake studies have been many and detailed. As earthquakes occur
frequently and generate AGWs with similar characteristics as manmade explosions, attribution of
observed waves is a critical aspect of remote detection which will be discussed in section 6.
As another trigger of AGWs, nuclear explosions have also been studied. Since nuclear testing in the oceans,
atmosphere, or space was banned by the limited test ban treaty (see preceding section on CTBT), testing has
been carried out in underground cavities since this date. This method reduces the seismic signal by about 2
orders of magnitude relative to atmospheric testing (Herbst et al., 1961; Latter et al., 1961). Detection after
this time would be enabled by multimodal technical means described in the previous section on the
CTBT. Distinguishing between waves generated by explosions and those triggered by earthquakes became
a topic of much discussion (Argo et al., 1995). The effect of acoustic radiation by UNEs was calculated by
Rudenko and Uralov (1995) and shown to propagate upward, with only small amounts captured by the
Earth's waveguide.
As already mentioned, AWs grow in amplitude with upward propagation up to the lower ionosphere (Hines,
1960). This factor, combined with the high percentage of upward wave energy, suggests that it is plausible to
Figure 4. (a) Infrasound waveforms, generated by an event near the Dugway Testing Ground recorded at regional arrays in the western United States (01:00:00–
02:00:00 UTC on 24 March 2011). Based on the origin time of the source (red line), the expected arrival time intervals for infrasound signals (group velocities from
280 to 380 m/s) are presented as green lines. (b) The signal recorded at BGU (distance of 38.5 km from the source) illustrates the individual arrivals. The signal
duration (3 s) is denoted by red lines. (c) The signal recorded at BRP is expanded to illustrate the individual arrivals. The signalduration (7s) is denoted by red lines.
(d) The event location is plotted with the 75% (blue), 90% (orange), and 95% (brown) credibility contours (393.8, 800.9, and 1,102.4 km
2
) and the associated stations
(yellow triangles) and detection azimuths (blue dashed lines). The red triangles are stations not used in the event location. Figure and caption taken from Park,
Grejner‐Grzezinska, et al. (2014).
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monitor wave emissions from explosions remotely, as proposed by Krasnov (1999). In this paper, Krasnov
proposed sounding of the ionosphere by using a ground‐based radio transmitter and receiver which allows
approximately a 300‐km wide region of the ionosphere to be sensed.
A major difficulty in AW monitoring lies in the uncertainty in neutral winds in the atmosphere. This affects
propagation characteristics (Hines & Reddy, 1967) which become more problematic with distance from the
wave source. This effect, combined with the multiplicity of waves from other events that generate AWs, leads
to difficulty in reliable detection. The problem is illustrated in Figure 4 where coherent responses to the
initial explosion are detected relatively close to the explosion site. With increasing distance, ambiguity in
the detected signals is apparent. Monitoring AWs reliably would require dense networks of observation
points not always possible for global detection.
An alternative solution to the problem is to leverage the coupling between AGWs and the ionosphere as
we will describe below. To first approximation, AWs amplify as they propagate vertically. In a realistic
atmosphere, effects such as a nonisothermal atmosphere, background neutral winds, viscosity, ion drag,
and heat conduction complicate calculation of wave amplitudes (Yeh & Liu, 1974). Model calculations
indicate a peak in AW velocity at altitudes over 100 km (Drobzheva & Krasnov, 2003). At these altitudes,
coupling to ions can generate TIDs which are the ionospheric manifestation of AGWs originating at the
Earth's surface.
In the preceding text, the emphasis was on modification of the neutral atmosphere by propagating waves,
both acoustic and gravity. But the waves also impose perturbations on the ionosphere, leading to generation
of electric currents and electric fields associated with TIDs. Detailed theoretical formulations of the coupling
have been given by Hines (1960), Hooke (1968, 1970a, 1970b), Kirchengast et al. (1995), Kirchengast (1996),
Kato (1980), Hocke and Schlegel (1996), and others.
In simplified form, the effect of AWs on the ionosphere can be expressed by the electron continuity equation:
∂N
∂t¼Q−L−M(4)
where Nis the electron density, Qand Lare the production and loss rate, respectively, and Mis the transport
term. For the Fregion, the main perturbation due to AWs is the transport term (Hooke, 1968), with relatively
small impact on production or loss. The dominant interaction between AWs and the ionosphere occurs via
ion‐neutral collisions, all other collisional terms being small. Since the ion gyrofrequency is much higher
than both the ion‐neutral collision frequency and the wave frequency, plasma is constrained to move along
magnetic field lines. Thus, the main effect of AW perturbations in the neutral gas on ions at Fregion alti-
tudes is to transfer motion of the neutral gas parallel to the magnetic field to ions via collisions (Hooke,
1968; Yeh & Liu, 1974), thus generating TIDs. TIDs, unlike AGWs, have a velocity component parallel to
the magnetic field. Together with the plasma drift, a polarization electric field is created in association with
the TIDs. This is confirmed by observations (Huang, 2016; Martinis et al., 2011) and numerical simulations
(Huba et al., 2015).
These conclusions are accurate if the GWs are accurately described in the simplified set of linearized equa-
tions given by Hines (1960) which lead to equations (2) and (3) above. A number of theoretical investigations
explore ion‐neutral coupling in more detail. These include descriptions of the effect of diffusion (Testud &
Francois, 1971; Yeh & Liu, 1974), energy dissipation (Hines, 1960), anisotropies in TID generation
(Hooke, 1970a, 1970b), heating by GW damping (Yeh & Liu, 1974; Kirchengast, 1996), and other effects that
are necessary to reconcile theory with observations.
An application of several of these theoretical concepts was presented by Drobzheva and Krasnov (2003), who
estimated the change in electron density caused by a point explosion at the surface of the earth.
∂N
∂t¼−N0
v
Lsin2Θ

þv∂N0
∂tcosφcosΘ

(5)
Nis the electron density due to the acoustic pulse; N
0
is the background electron density, Θis the angle
between the direction of the Earth's magnetic field and the acoustic ray path, and φis the angle between
the magnetic field and the zaxis.
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The predicted change in electron density agrees approximately with observations of electron densities
obtained by high‐frequency (HF) sounders after a surface explosion. The altitude profiles of the atmospheric
constituents and temperatures are based on an empirical model. Viscosity and conductivity were based on
empirical formulas.
Analytical solutions have largely been superseded by the physics‐based models, in particular the general cir-
culation models, which usually include the full set of continuity, momentum, and temperature equations for
electrons and all ion species—see Fuller‐Rowell and Rees (1980), Roble et al. (1988), Ridley et al. (2006),
Huba et al. (2000), and references therein. Below, we describe modeling approaches, including general cir-
culation model methods, which have been carried out in order to capture the complex coupling processes
involved in ionospheric response to explosive events.
4. Ionospheric Effects
As noted above, TIDs can be generated by GWs, AGWs, or AWs in different mechanisms. They are classified
into large‐scale TID (LSTID), medium‐scale TID (MSTID), and small‐scale TID (SSTID), solely based on the
velocity, duration, and the wavelength regardless of their generation mechanism. The horizontal velocities
of the LSTID, MSTID, and SSTID are 400–1,000, 100–300, and lower than 200 m/s, respectively; the dura-
tions, or wave periods, are 30 min to 3 hr, 12 min to 1 hr, and a few minutes, respectively; and the wave-
lengths of LSTID, MSTID, and SSTID are greater than 1,000, 100–1,000, and 10–100 km, respectively
(Rieger & Leitinger, 2002; van Velthoven & Spoelstra, 1992). Among the different types of waves, GW and
AGW tend to generate LSTIDs or MSTIDs, while AW generates relatively smaller scales of MSTIDs or
SSTIDs (Rieger & Leitinger, 2002).
4.1. TIDs Induced by Nature
Various types of Earth geophysical events can be the trigger of TIDs, as listed in section 1. In this section, we
briefly introduce several specific types of natural and man‐made events in a different range of altitudes from
underground, near surface, up to atmosphere.
Geomagnetic storms are one of the common geophysical events known to generate perturbations in the
ionosphere, typically LSTIDs. Hunsucker (1982) reviewed the topic and suggested that possible generation
mechanisms were Joule heating, Lorentz forcing, and energetic particle precipitation. Richmond (1978) con-
cluded that the most likely source of high‐latitude GWs is Joule heating. This is the currently accepted gen-
eration mechanism. Davis (1971) detected TIDs from several high‐latitude geomagnetic observatories,
supporting the theory that LSTIDs originate during polar substorms, and claimed to determine the location
of the source for a number of individual TIDs. Ho et al. (1998) observed two long‐lasting winter geomagnetic
storms in 1993 and 1994 using a global network of GPS receivers. In both events the TEC enhancement
occurred within 1 hr of each event, and large‐scale ionospheric structures were identified. From the event
in 1994, the clear signature of a TID with a propagation velocity of up to 460 m/s was detected. While most
geomagnetic storms are considered to be a source of LSTID, Nishioka et al. (2009) detected a “super”‐MSTID
by ground‐based GNSS observations as well as Low Earth Orbit satellites during the geomagnetic storm on
10 November 2004.
Unlike geomagnetic storms, other natural and artificial events induce smaller‐scale TIDs, either MSTIDs or
SSTIDs. Earthquakes generate seismic waves and corresponding secondary waves that propagate in the ver-
tical and horizontal directions. Yuen et al. (1969) examined seismic, atmospheric, and ionospheric data after
the Hachinohe earthquake in 1968. During Rayleigh wave propagation along the Earth's surface with the
velocity of about 3.5 km/s, upward traveling AWs are produced that cause disturbances in the atmosphere
up to heights of at least 300 km (Yuen et al., 1969). The AWs are shock acoustic waves that propagate upward
from the focal area in narrow cone of zenith angles with the sound speed at the corresponding altitudes
(Astafyeva & Afraimovich, 2006; Calais & Minster, 1995). The sound speed of upward propagation depends
on altitude and range from about 250 m/s near the surface to 800 m/s at 300‐km altitudes as shown in the
vertical profile of sound speed in Artru et al. (2004). During upward propagation, the shock acoustic wave
is amplified exponentially due to the decrease of density before interacting with the ionospheric plasma.
A number of studies observed TIDs in the ionosphere from major earthquakes such as the Sumatra‐
Andaman earthquake in 2004 (Astafyeva & Afraimovich, 2006; Choosakul et al., 2009; DasGupta et al.,
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2006), Samoa in 2009 (Galvan et al., 2012; Occhipinti et al., 2013), and the Tohoku‐Oki earthquake in 2011
[Astafyeva et al., 2011; Heki, 2011; J. Y. Liu et al., 2011).
In addition to the direct impact from the Rayleigh wave, sudden vertical displacements of the Earth's surface
near the epicenters of earthquakes also excite AWs that propagate horizontally with about 300‐m/s veloci-
ties, and vertical spreading of this energy can generate TIDs in the ionospheric Flayer (Astafyeva &
Afraimovich, 2006; Bolt, 1964; Pokhotelov et al., 1995). This type of wave is considered a seismic airwave
(Astafyeva & Afraimovich, 2006; C. H. Liu et al., 1982; J. Y. Liu et al., 2006), which generates MSTIDs.
Since the TIDs from a seismic airwave are not directly excited by the main shock of the earthquake, they
are regarded as “secondary”TIDs (Astafyeva & Afraimovich, 2006).
Tsunamis are another trigger of TIDs. Unlike the vertical propagation speed of the AW, which is the order of
300–1,000 m/s, the GW induced from a tsunami in the ocean propagates obliquely upward with a vertical
velocity of about 50 m/s and takes about a few hours for the wave to reach the ionosphere (Artru et al.,
2005). In the ionosphere, the AW generated by the tsunami wave interacts with the ionospheric plasma to
generate TIDs, which then propagate horizontally with a velocity of the order of about 200 m/s (Artru et al.,
2005; J. Y. Liu et al., 2006; Galvan et al., 2012).
In addition to the above mentioned types of event, next section describes more details on the TIDs generated
by explosive events.
4.2. TIDs From Explosive Events
Ionospheric disturbances generated by explosive events near the surface or at low altitudes have been
observed from volcanic eruptions and nuclear detonations. TIDs of several volcanic eruptions were studied
using data from ionospheric observation sensors and GNSS receiver networks. Roberts et al. (1982) detected
TIDs induced by the explosion from the volcanic eruption of Mt. Helens on 18 May 1980 at three TEC mon-
itoring stations at ranges of less than 2,000 km and at six distant stations up to 4,950 km in range. The pro-
pagation velocity of two groups of TIDs from closer and further stations were about 350 and 550 m/s,
respectively. The authors claimed that the TIDs from this volcanic eruption were analogous in that they were
induced by a GW excited by the explosive activity. Several studies investigated the ionospheric signatures
released from a series of strong eruptions at Mount Pinatubo in 1991. By analyzing the virtual height varia-
tion of the ionosphere, TEC variation, and Doppler shift from the ionospheric observation sensors, Cheng
and Huang (1992) identified TIDs with periods of around 16 to 30 min, wavelengths between 160 and
435 km, and propagation speeds ranging from 131 to 259 m/s coming from the direction of Mount
Pinatubo toward the observation sites in Taiwan. From these characteristics, the waves were interpreted
as freely propagating GWs (Cheng & Huang, 1992). Igarashi et al. (1994) also observed the TIDs from this
event at five ionospheric sounding stations in Japan and determined that the apparent horizontal velocity
of the TIDs was 290 m/s. These findings agreed with the measurements of TIDs produced by the eruption
of Mount St. Helens and traveling at velocities of 293–312 m/s in C. H. Liu et al. (1982). In more recent stu-
dies, waves released from volcanic eruptions have been interpreted as AWs based on the detected propaga-
tion speed. Heki (2006) identified the TIDs triggered by the Asama volcanic eruption in 2004. The apparent
velocity was around 1.1 km/s, coinciding with the sound velocity at ionospheric altitudes. The author deter-
mined the explosion energy of the eruption by comparing the detected TID velocity and period with a known
surface explosion releasing 2×10
14
J. Dautermann et al. (2009) measured the AW component from the
Soufrière Hills Volcano, Montserrat, in 2003. The horizontal velocity of the TID was 624 m/s, and the explo-
sion energy was estimated as 1.53×10
10
J.
TIDs are also generated by artificial events such as nuclear explosions at tropospheric altitudes, the lower
atmosphere, near the surface, and underground. The ionospheric responses due to atmospheric nuclear
explosions have also been studied since the 1960s (e.g., Kanellakos, 1967; Rose et al., 1961; Webb &
Daniels, 1964). The atmospheric nuclear explosion on 30 October 1961 in Novaya Zemlya was recorded by
various types of sensors including a seismograph, a microbarograph, a magnetograph, and a vertical inci-
dence ionosonde (Rose et al., 1961). This explosion generated AWs that were strong enough not to be atte-
nuated after travel around the world. The round‐the‐world AW was detected 27 hr after the explosion near
Novaya Zemlya at ionospheric height using rocket data, and the derived mean velocity was 420 m/s (Rose
et al., 1961). Webb and Daniels (1964) detected the ionospheric oscillation identified as being triggered by
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a sound wave from an atmospheric nuclear explosion detonated by the Soviet Union in 1962. The recorded
wave had a 30‐min period, and the corresponding group velocity was about 730 m/s. A propagation velocity
with a similar range was detected from another historical event, the Housatonic nuclear explosion, which
was a megaton‐class, low‐altitude nuclear explosion that generated an AGW propagated in all directions
from ground zero (G
0
) with a constant velocity of about 765 m/s (Albee & Kanellakos, 1968; Kanellakos,
1967). In the case of the Housatonic explosion, the spatial distribution of the observing stations suggested
that the geomagnetic field played an important role in the ionospheric responses to AGWs. As described
above, ion‐neutral collisions effectively transfer kinetic energy to the ions that are constrained to move along
the magnetic field.
Kanellakos (1967) observed the different behaviors of ionospheric fluctuation at two groups of stations that
were geospatially oriented based on the direction from G
0
and the geomagnetic equator. With the considera-
tion of the influence of the magnetic field, the properties of TIDs can be estimated as a function of time after
the explosion and distance from the explosion. This is achieved by extracting the detected location and time
of the fluctuation in the plasma frequency corresponding to the peak electron density in the ionosphere
(Albee & Kanellakos, 1968). In addition, Barry et al. (1966) applied linear acoustic theory to predict the iono-
spheric signature from a ground level explosion. By analyzing the interaction of a pressure wave disturbance
with the ionosphere, the passage of the wave should be measurable using vertical‐incidence radio phase‐
path sounding. To predict the radio phase‐path disturbance near the explosion, the authors described the
methods of (1) a calculation of the size and shape of the neutral pressure wave produced in the ionosphere
and (2) a prediction of the effect of a pressure disturbance on HF radio waves.
To characterize the neutral pressure wave from the blast, the authors consider the wave as a near‐field effect
and simplify the calculation by treating it as a linear AW. Then two types of loss of energy during the upward
propagation, spreading, and attenuation were independently considered. Then the atmospheric filtering
effect based on the attenuation characteristics was applied. From the characteristics of the neutral pressure
wave propagated to the ionosphere, the corresponding fluctuations in the local ion and electron number
density on an HF radio wave was modeled. The prediction model was confirmed by the experimental results
of a 500‐t explosion in disturbance onset time, shape, and duration (Barry et al., 1966).
Since the Partial Test‐Ban Treaty was opened for signature in 1963, atmospheric nuclear tests have been
significantly reduced while more UNEs have been carried out. As a local source of acoustic fluctuations
driving the magnetosphere and ionosphere (Mikhailov et al., 2000), UNE and corresponding TID have been
investigated for several decades. Assuming hemispherical upward propagation of AWs, Rudenko and
Uralov (1995) calculated the ionospheric effects from the acoustic radiation of a UNE and determined a
relationship between the UNE parameters (depth, explosion yield, and mechanical property of the rock)
and the vertical displacement of the ionosphere, which is produced by the shock wave above the blast site.
A model for calculating the acoustic field from an UNE presented by Krasnov and Drobzheva (2005)
showed that the ionospheric electron density perturbation from the UNE could reach 10% of the back-
ground level (see equation (5), above). The UNE‐generated TID was observed from GNSS‐based TEC
shown in Park et al. (2011) and Yang et al. (2012). Unlike naturally occurring TIDs, the event‐related
TIDs can be identified much more easily based on the characteristics of the TIDs, such as the arrival time
of the various peaks, the period, and amplitude (Kanellakos, 1967). Park et al. (2011) detected the spatial
signature of a TID induced by 2009 North Korean UNE by processing GPS data observed at multiple
Continuously Operating Reference Stations in South Korea, Japan, China, and Russia as shown in
Figure 5 taken from Figure 2 in Park et al., 2011. The spatial signature can be used as a tool for distinguish-
ing the event origin based on the propagation velocity of MSTIDs released from this type of event and the
distance to the locations where TIDs were observed by the TEC sensor array (Park et al., 2011). Yang et al.
(2012) applied wavelet coherence analysis to identify two UNEs conducted in 2006 and 2009 by North
Korea. From the integrated electron content data set obtained at dense GNSS stations in Japan, referred
to as GEONET, significant wave trains were detected. Figure 6 (taken from Yang et al., 2012) depicts the
wavelet coherent analysis for five days centered on the date of the 2009 North Korean UNE. From wavelet
coherence analysis, two types of TIDs were identified in the 2006 and 2009 UNEs. Low‐frequency distur-
bances with periods of 3–12 min and propagation velocity of 75–453 m/s were consistently found from both
events, while the high‐frequency disturbances with periods of 2–5 min and propagation velocity of 297–
1,322 m/s were identified only from the 2009 event.
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4.3. Discrimination of TIDs Between Explosive Events and Other Sources
Numerous studies investigated TIDs generated from various sources at different regions. Kirchengast (1997)
demonstrated the TIDs from different causative mechanisms based on a theoretical model. On the other
hand, Kirchengast et al. (1995) retrieved quantitative GW information from incoherent‐scatter radar mea-
surements of TIDs. These studies support the fact that the TIDs can be identified with respect to the causa-
tive mechanisms/sources. This section introduces efforts to discriminate the TIDs induced by explosive
events from the TIDs of other sources.
Roberts et al. (1982) compared the TIDs from volcanic eruptions with large‐scale explosions based on the
findings reported in the literature. The authors distinguished the characteristics of the TIDs from different
sources in terms of propagation velocity. The characteristics of volcano eruptions, which was the major topic
of the paper, was compared to other literature, and it was concluded that this type of event generated unu-
sual large acoustic perturbations with the period of 4–5 min and their propagation speed was up to 300 m/s.
The TIDs induced from the 1‐to 13‐Mt blasts showed some similarity with the TIDs from the volcanic erup-
tion, but they propagated at roughly uniform speeds of 630 and 770 m/s, while the TIDs from the Mount St.
Helens' volcanic eruptions propagated with an average velocity of 355 up to 550 m/s at the distant site
(Roberts et al., 1982).
Sweeney (1996) examined the low frequency electromagnetic (EM) signals produced by chemical explosions
during the Kucken experiments in Nevada Test Site in 1995. By comparing the characteristics of EM pulses
(EMP) with UNEs, the author found the low‐frequency EM signature of a chemical explosion has longer
duration, lower‐frequency content than nuclear explosions. In addition, the detected EM signatures were
more delayed from the detonation time compared to nuclear explosions. More recent study from the same
author claimed that the magnitude of the EMP from an UNE was about 2 orders of magnitude larger than
that from a chemical explosion and had a larger component at higher frequencies (Sweeney, 2011).
Although these studies did not provide direct evidence of ionospheric variations from the events, the
Figure 5. The locations of the underground nuclear explosions and Global Navigation Satellite System stations C1
(CHAN), C2 (CHLW), D1 (DAEJ), D2 (DOND), I1 (INJE), S1 (SUWN), S2 (SHAO), S3 (SOUL), U1 (USUD), Y1
(YANP), and Y2 (YSSK) on the coastline map around Korea, China, and Japan. The gray‐shaded signals highlight
examples of the detected traveling ionospheric disturbances' slant total electron content numerical third‐order three‐point
derivatives for stations C1, D1, D2, and I1. The bold dashed line gives the ground track for satellite PRN 26 with dots that
indicate the arrival times of the traveling ionospheric disturbances at their ionospheric pierce points. All time labels in the
figure are in UTC (taken from Figure 2 in Park et al., 2011).
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differences in frequency spectrum and time delay of the EMP from two types of events can be related to the
ionospheric perturbations for discriminating the events.
Another approach for discriminating the TIDs from a particular event is to analyze the wave property of the
TIDs. Garrison et al. (2007) and Park et al. (2011) applied cross‐correlation analysis to the TID candidate
waveforms considering the similarity between the ionospheric disturbances released from one source event
and recorded at multiple stations. Garrison et al. (2007) generated a TEC time series from GPS data collected
at 175 stations and applied a fifth‐order Butterworth band‐pass filter with range 5.6–1.7 mHz (period of 3–
10 min). By computing the cross‐correlation between the filtered time series, a coherent disturbance was
identified. Park et al. (2011) also focused on the correlation between the TID candidates to identify TIDs
Figure 6. Wavelet coherence spectrum for five days centered on day 145, which is the date of the 2009 North Korean underground nuclear explosion. The dominant
frequencies in a band from 0.0035 to 0.0078 Hz at the time of the detonation (reproduction of Figure 1 in Yang et al., 2012).
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from multiple candidate waveforms by applying cross‐correlation techniques. Using one distinctive
signature as a reference TID, the correlation coefficients (CC) were computed in the filtered TEC time
series from other GNSS stations which enabled semiautomatic processing (Park et al., 2011).
Park, Arrowsmith, et al. (2014) compared the characteristics of TIDs induced by UNEs and an earthquake.
By processing GPS data recorded at Continuously Operating Reference Stations near the selected events,
which were two UNEs by North Korea in 2006 and 2009 and the Japanese Tohoku earthquake in 2011,
the TID signatures from each event were detected. The authors analyzed the waveforms of each type of
TIDs and indicated a notable difference where the wavelength of UNE‐derived TIDs were in the range of
2–4 min while the earthquake‐derived TIDs were about 15 min. Also, the peak‐to‐peak amplitudes of the
earthquake‐derived TIDs were about twice the size of the UNE‐derived TIDs. Figure 7 shows the TID wave-
forms of the 2009 UNE, 2006 UNE, and 2011 earthquake, respectively, presented in Park, Arrowsmith,
et al. (2014).
This study provides numerical results showing the CC between the waveforms across three events. These
results demonstrate significant similarity between the TIDs from two UNEs with a CC of 0.89 and dissimi-
larity between the TIDs from the UNEs and the earthquake with a CC below 0.1. However, the paper also
emphasized that more various types of earthquakes with different magnitudes, tectonic settings, and tsu-
nami effects should be taken into account for better understanding of the discrimination between two types
of events.
Yang et al. (2012) detected TIDs from 2006 and 2009 North Korean UNEs by applying a wavelet analysis to
enhance a cross‐correlation technique from Garrison et al. (2007) on GNSS derived TEC observations. By
taking the Morlet wavelet for wavelet coherence analysis, two UNEs in 2006 and 2009 were detected and
characterized in terms of the period of the disturbances and the propagation velocity of TIDs. From the
2009 event, long‐period (3–12 min) and short‐period (2–5 min) disturbances were detected and their propa-
gation velocities were in the range of 61–174 m/s and higher speed up to 1,322 m/s, respectively. From the
2006 event, only long‐period (3–12 min) disturbances with a propagation velocity in the range of 135–
453 m/s were detected.
Figure 7. Slant total electron content derivatives from station INJE for the 2009 UNE (top), station PAJU for the 2006
UNE (middle), and station 0800 for the 2011 Tohoku earthquake (bottom). The red waveforms mark the traveling iono-
spheric disturbance, and the shaded regions highlight the 4‐min windows of signal values used in the correlation analysis
(reproduced from Figure 8 in Park, Arrowsmith, et al. (2014)). UNE = underground nuclear explosion.
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5. Innovative Techniques
5.1. Passive High‐Frequency/Very High Frequency Sounding
While the GNSS analysis techniques described above create the possibility of a new global ionospheric mon-
itoring scheme, the fact that they rely on integrated TEC measurements is somewhat problematic for mon-
itoring far from the site of a low‐altitude/surface explosion. While infrasound waves can be ducted around
the world, the top of this duct is at approximately 120‐km altitude, squarely within the Eregion.
Integrated TEC measurements are much more sensitive to fluctuations within the Fregion where the bulk
of the ions reside. Thus, a very “quiet”Fregion is required for GNSS‐based detections of E
region disturbances.
Because of this, monitoring the bottom‐side ionosphere via HF sounding may be a more ideal approach. In
fact, many historical detections of explosion‐induced ionospheric disturbances were made this way (e.g.,
Barry et al., 1966; Fitzgerald & Carlos, 1997; Jacobson et al., 1986; Krasnov, 1999). However, there is no net-
work of HF sounders with anywhere close to the (nearly) global coverage of continuously operating GNSS
receivers. This is largely due to the relatively inexpensive nature of GNSS receiver stations compared to
HF sounding/radar systems. The cost difference derives from the fact that the transmitters for GNSS recei-
vers are free to the users, that is, the GNSS satellites are transmitters of opportunity (ToO), and the fact that a
GNSS receiver system has a much smaller physical footprint. However, several investigators have shown the
utility of HF/MF (medium‐frequency) systems that use terrestrial ToOs to sound the ionosphere and char-
acterize disturbances (e.g., Chilcote et al., 2015; Helmboldt et al., 2013). These methods, coupled with
new, relatively low‐cost, electrically short antenna technology (see, e.g., Hicks et al., 2012) enable
portable/relocatable monitoring systems. Because MF systems are still broadcasting all over the world
(e.g., AM radio), and HF systems are often still used for timing (e.g., WWV in the United States; BPM in
China), a relatively low‐cost worldwide network of HF/MF receivers could be fielded to specifically target
the bottom‐side ionosphere and act as a supplement to any GNSS‐base system.
Because the top of the thermospheric infrasound duct is at ~110‐to 120‐km altitude, such HF systems
could sound regions of the bottom‐side ionosphere that are hundreds of kilometers away from any
potential sites of interest and watch for the impact of ducted infrasound waves. Additionally, since many
high‐power HF/MF ToOs support ionospheric propagation over distances of thousands of kilometers, the
receiver systems could likewise be thousands of kilometers from the ionospheric regions to be monitored.
Thus, a relatively low‐density network of such receivers could be employed worldwide and could enable
monitoring over denied regions such as the middle of the Pacific Ocean (e.g., using the WWVH transmit-
ters in Hawaii).
In addition to human‐made ToOs, the sky is also populated with naturally occurring radio sources such as
supernova remnants, active galactic nuclei, quasars, and radio galaxies. Many such sources have radio‐
frequency spectra that increase rapidly with lower frequency, making them useful high‐frequency/very high
frequency (HF/VHF) beacons. The signals from such sources are very noise‐like and therefore cannot be
used as beacons in the same way as, for example, GPS signals. However, if multiple high‐gain antennas
are pointed toward the same cosmic radio source, cross‐correlating the (complex) signals between each pair
of antennas, or “baseline,”yields a very strongly peaked signal at a predictable time difference of arrival.
Gradients in TEC between the two antennas' lines of sight cause this time difference of arrival to be slightly
off from the predicted value, making the phase of the cross‐correlation quite sensitive to the local
TEC gradient.
This cross‐correlation of cosmic radio signals is the basis of modern radio‐frequency (RF) interferometry.
Within the radio astronomy community, interferometers are used with aperture synthesis techniques to
generate high angular resolution images of cosmic sources. Depending on the frequency being used, this
imaging process can be severely impacted by fluctuations in the local TEC gradient. This is generally true
below L‐band (~1,400 MHz), but is especially true in the HF/VHF regime (~10–300 MHz) because the
phase error is proportional to the TEC gradient between the two antennas and to the observing wavelength.
To first order, this causes the source to appear to move on the sky, blurring it out in the image plane the
same way tropospheric fluctuations do with optical images. Relatively large fluctuations on scales compar-
able to the size of the interferometer can cause a total loss of coherence across the array, making
imaging impossible.
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For many decades, ionospheric fluctuations were the main limiting factor to the size of HF/VHF interferom-
eters, which in turn severely limited their angular resolution. However, innovative techniques developed in
the 1980s and 1990s broke this ionospheric barrier, allowing for a kind of renaissance for low‐frequency RF
interferometry (see Cornwell & Fomalont, 1999; Cotton et al., 2004; Intema, 2009; Kassim et al., 2007).
Consequently, a trove of new interferometers operating in the HF and VHF regimes have been and are being
developed, including the low‐band system on the Karl G. Jansky Very Large Array (VLA) in New Mexico, the
Low Frequency Array (LOFAR) in the Netherlands/Europe, the Murchison Widefield Array (MWA) in
Australia, the Long Wavelength Array (LWA) in New Mexico and California, and the Low Frequency
Aperture Array of the Square Kilometer Array in Australia.
It was recognized early on that the ionospheric calibration methods that make high angular resolution
synthesis imaging possible could also be used as high‐precision probes of ionospheric structure. In particu-
lar, interferometers like the VLA that also operate at higher frequencies require extremely stable, atomic
clock‐based timing systems that allow for very precise measurements of fluctuations in baseline phases.
When observing a bright cosmic source, this amounts to a TEC gradient precision on the order of 10
−5
–
10
−4
TECU/km for the VLA low‐band systems (330 and/or 74 MHz; Helmboldt et al., 2012a) and for the
LOFAR Low Band Array (10–80 MHz; Mevius et al., 2016).
Different methods to utilize the high TEC gradient precision afforded by RF interferometers have been
developed to study the ionosphere, each exploiting the strengths of different array types/configurations.
Pioneering studies were performed with the VLA by Jacobson and Erickson (1992a, 1992b) using baseline
phases to find, track, and characterize TIDs. Jacobson and Erickson (1992b) also found that the TEC gradient
precision was so good that they were detecting density fluctuations among flux tubes within the plasma-
sphere. Helmboldt et al. (2012b) and Helmboldt and Intema (2014) expanded upon these results, developing
spectral analysis methods to more generically characterize observed ionospheric fluctuations with high pre-
cision. These techniques were applied to archival VLA data at 1,400 MHz to successfully detect the impact of
a TID resulting from one of the last underground nuclear tests conducted in Nevada in 1992 (Park
et al., 2013).
With the development of methods to monitor several (~20–50) moderately bright sources over the relatively
wide 74‐MHz field of view of the VLA, Cohen and Röttgering (2009) and Helmboldt, Lane, and Cotton
(2012) were able to perform larger‐scale studies of both wavelike and turbulent ionospheric fluctuations.
With the extremely wide field of view of the MWA, Loi, Murphy, et al. (2015), Loi, Trott, et al. (2015), and
Loi et al. (2016) expanded upon these methods to monitor and track hundreds of cosmic radio sources at
once, allowing for a kind of imaging of ionospheric disturbances. This led to some of the first direct evidence
of high altitude (~600–700 km) plasma troughs in the topside ionosphere, which were directly imaged with
the MWA (Loi, Murphy, et al., 2015; Loi et al., 2016).
While the novelty of these methods makes them intriguing, their usefulness for explosion monitoring may
not be immediately evident. The key is not only the high precision of the measurements but also the simple
fact that interferometers are directly sensitive to the gradient in TEC rather than the absolute TEC. This
naturally biases interferometric observations toward smaller‐scale ionospheric fluctuations. For instance,
to first order, a TID is temporally and spatially a plane wave. The spatial gradient of a plane wave is just
the wave itself multiplied by the wavenumber, and so smaller‐scale waves, which also tend to have smaller
TEC amplitudes, are magnified to the interferometer. This is the spatial equivalent of the TEC time differen-
tial method employed by Park et al. (2011) to isolate the small time‐scale explosion signature within GPS‐
based TEC time series. VLA observations concurrent with GPS and/or optical airglow imager data have
demonstrated that the VLA‐based TEC gradient measurements pick up smaller‐scale structures not evident
to the other sensors (Coker et al., 2009; Dymond et al., 2011; Helmboldt et al., 2012b; Park et al., 2013).
Figure 8 shows examples of small‐amplitude TEC gradient fluctuations following two separate explosion
tests conducted at the Energetic Materials Research and Testing Center (EMRTC) near Socorro, NM.
These were measured with the VLA using 340‐MHz observations of the bright cosmic sources Cygnus A
(10 March 2015) and Perseus A (25 July 2017) in the upper and lower panels, respectively. Cygnus A is sig-
nificantly brighter than Perseus A, and it therefore enabled TEC gradient measurements with an order of
magnitude better precision (3 × 10
−5
TECU/km vs. 4 × 10
−4
; see Figure 5). In both cases, a significant dis-
turbance is apparent 5–8 min after the blast after bandpass filtering the time series. The peak oscillation
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frequency for the 10 March disturbance is somewhat lower than that for the 25 July event (0.10 vs. 0.14 Hz).
It is also apparent that the lower‐yield event from 10 March produced a much smaller TEC gradient
amplitude, so much so that it was only possible to detect it with the extremely high precision enabled by
Cygnus A. In both cases, the arrival times are consistent with infrasound ray tracing calculations,
illustrating the potential for this method to monitor for moderate‐yield and/or well‐buried explosions.
In addition, the ability of these measurements to sense density fluctuations in the topside ionosphere (Loi,
Murphy, et al., 2015) and into the plasmasphere (Hoogeveen & Jacobson, 1997, Helmboldt et al., 2015) cre-
ates the possibility for long distance monitoring of large areas of interest. While AWs and/or GWs have no
direct impact on the plasmasphere (the neutral density there is essentially zero), the polarized electric fields
associated with the TIDs they generate do affect this region. Both observations and simulations have shown
that TID‐related electric fields map along magnetic field lines all the way to the magnetic conjugate point,
creating mirror TIDs the opposite hemisphere (Martinis et al., 2011; Otsuka et al., 2004; Shiokawa et al.,
2005) and perturbing plasmaspheric flux tube densities along the way (Huba et al., 2015). Thus, by monitor-
ing for plasmaspheric disturbances along an interferometer's line of sight, one can effectively monitor for
any fluctuations occurring in the ionosphere below, which are potentially thousands of kilometers away
from the interferometer itself. Additionally, the corotating nature of the plasmasphere allows for plasma-
spheric fluctuations to be geo‐located via their orientation (Hoogeveen & Jacobson, 1997) and/or speed
(Helmboldt et al., 2015), allowing one to map these along magnetic field lines to specific regions within
the ionosphere below.
Finally, the advent of new technologies that use electrically short, active antennas makes relocatable arrays
for monitoring purposes much more feasible (see, e.g., Hicks et al., 2012). While interferometers like the
VLA that use large parabolic antennas are fixed to one location and extremely expensive to reproduce else-
where, dipole‐based phased arrays are much more economical and relocatable. Several arrays already utilize
this technology in fixed configurations (LOFAR, MWA, and LWA), and relocatable systems based on these
are being developed (Lind et al., 2015). A system designed specifically for ionospheric monitoring would also
benefit from the fact that only the brightest cosmic sources need be observed, which greatly reduces the
required collecting area/number of antennas. Such a relocatable system can be located a few hundred kilo-
meters from the region of the ionosphere to be monitored and requires a relatively large footprint (several
Figure 8. The TEC gradient as a function of time relative to the detonation times for two EMRTC explosion tests. The
plots show the gradients projected along the vector pointing from the EMRTC site to the approximate ionospheric
pierce point location. The date/time and yield of each event are given above each panel. In both cases, the time series were
bandpass filtered to highlight the detected disturbances. The frequency ranges used for the bandpass filters are printed
within each panel. The 1 sigma uncertainties in TEC gradient are also given within each panel. The error in the time series
within the upper panel is an order of magnitude lower because a brighter source was observed (Cygnus A vs. Perseus A).
TEC = total electron content; EMRTC = Energetic Materials Research and Testing Center; VLA = Very Large Array.
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antennas with baselines of ~1 km or more). Therefore, this type of plat-
form may be better suited to targeted monitoring campaigns rather than
a global monitoring network.
In summary, while pioneering work has clearly demonstrated the unique
utility of interferometric observations of cosmic beacons for ionospheric
remote sensing, we have really only scratched the surface. By building
on these successes, economical/relocatable systems may be developed
for ionospheric disturbance monitoring that could supplement larger‐
scale GNSS‐based systems.
5.2. Numerical Modeling
As shown in previous sections, TIDs are generated by a wide range of phy-
sical processes. Models have been developed to capture the interactions
which lead to TIDs. In attempting to separate TIDs from impulsive events
(detonations, earthquakes, and rocket launches) from TIDs which are
generated by environmental processes (upward forcing from the tropo-
sphere and magnetic activity), models can play an important role.
While physics‐based models of the ionospheric impacts of nuclear test
explosions are generally not available to the academic community, a num-
ber of models to specify and predict TIDs have been developed and pub-
lished. These range from the empirical (Seker et al., 2009) and
semiempirical (Fedorenko et al., 2013) to physics‐based (Shiokawa et al.,
2005; Zettergren & Snively, 2013; Huba et al., 2015), to specific to a parti-
cular observational data set (Kirchengast et al., 1995). We will focus on
models with particular relevance to TIDs from impulsive sources.
Models serve three major purposes in this area of research: (1) They can be
used to confirm attribution of the sources of TIDs; (2) the combination of
models and observations via data assimilation enables more accurate spe-
cification and forecast of ionospheric response; and (3) accurate modeling
can be used to specify potentially affected spatial regions when direct
observations are absent or not possible.
In some versions of explosion simulations the focus is placed on the gen-
eration of AWs, treating the disturbance as pressure pulses (Albee &
Kanellakos, 1968; Bowling et al., 2013; Fedorenko et al., 2013) which
can be reduced to geometric rays. This approach has the advantage of
computational simplicity which yields surprisingly good results when
compared with observations of TID time delays measured using acoustic
detectors or by GPS receivers measuring TEC. A variation of this ray‐tracing approach is to combine empiri-
cal data with a physics‐based model (Marchand & Berthelier, 2008) in order to explore the ionospheric
response to the generation of acoustic modes by explosive events. In this study, an idealized AW generated
by an earthquake is used as input to SAMI2 (Huba et al., 2000), a two‐dimensional global, physics‐based
model of the ionosphere which describes ion motion in a coordinate system based on the magnetic field line
geometry. In this model, the continuity and momentum equations for electrons and each ion species are
solved. Neutrals are specified by the MSISE86 model (Hedin, 1987), and neutral winds by the Horizontal
Wind Model (Hedin, 1991). The coupling of an idealized neutral density perturbation launched at 0706
UT at approximately 250‐km altitude to the ionosphere results in an electron density perturbation as shown
in Figure 9. The relative density perturbation, defined as the difference between the density estimated with
and without the impulse, divided by the undisturbed density, can be seen as a single maximum in Figure 9
(top) at 0712 UT, shortly after the perturbation is applied. At 0812 UT in the simulation, the effect is observed
at locations along the magnetic flux tube. The propagation along the magnetic field toward its conjugate
point in the other hemisphere suggests that detection may be possible at large distances from the initial gen-
eration of the wave.
Figure 9. Two‐dimensional profiles of the relative density perturbation
(top) at 07:12 UT, shortly after the passage of the acoustic impulse, and
(bottom) at 08:12 UT. The relative density perturbation is the difference
between the density calculated with and without the impulse, divided by the
unperturbed density. The center of the impulse reaches the altitude of
250 km at 07:00 UT (reproduced from Figure 2 of Marchand & Berthelier,
2008).
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The next generation of this model, SAMI3 includes a wedge model (Huba et al., 2008) which is focused on a
narrower sector in local time, MSTIDs have been modeled self‐consistently using this model (Duly et al.,
2014), and qualitative results compare well with observations. Conjugate effects which have been reported
in observational studies (Burke, Martinis, et al., 2016; Burke, Pfaff, et al., 2016; Martinis et al., 2011;
Otsuka et al., 2004; Shiokawa et al., 2005) appear naturally in the model (Huba et al., 2015) as mentioned
in the preceding section. This is also a feature of models of AWs generated in the troposphere (Zettergren
& Snively, 2013).
A different approach is taken by the Whole Atmosphere Community Climate Model (WACCM) (H.‐L. Liu
et al., 2010) which was developed to improve simulations of coupling of atmospheric forces to the iono-
sphere. There are several versions of WACCM which have been tailored to address specific physical pro-
cesses (Beres et al., 2005; Verronen et al., 2016). WACCM‐X (H.‐L. Liu et al., 2010) extends the original
model to include the thermosphere where AGWs propagate. The major advance in WACCM was to provide
realistic simulation of upward forcing of the upper atmosphere by the lower atmosphere. GWs are amplified
by tropospheric processes and are generally regarded as the causes of atmospheric tides and planetary waves
(Forbes, 1995).
WACCM‐X has recently been coupled to SAMI3 which now allows upward forcing from the troposphere to
be simulated in the ionosphere by coupling of the perturbations in WACCM‐X to the lower boundary of
SAMI3 (McDonald et al., 2015). The variability in the ionosphere due to forcing from the stratosphere
during a sudden stratospheric warming was more accurately reproduced by inclusion of realistic upward
propagating GWs. Other examples of WACCM coupling to other models are described by Siskind et al.
(2017) and Pedatella et al. (2016). Further developments along these lines are being carried out, including
assimilation of low‐altitude weather patterns, and validation with observations at low latitudes (McDonald
et al., 2016).
Meng et al. (2015) use a different physics‐based model, the Global Ionosphere Thermosphere Model (GITM)
(Ridley et al., 2006) to simulate the GWs generated by a tsunami, specifically the vertical displacement of the
ocean surface which is modeled as a combination of sine waves. The actual tsunami wave field is presumably
more complex, but may be treated as a linear superposition of the simple wave used as input in this simula-
tion. Perturbations in neutral density, velocity and temperature are derived for GWs (Fritts & Alexander,
2003: Vadas & Fritts, 2005; Vadas & Nicolls, 2012) and applied at the lower boundary. By restricting the
range of frequencies, upward propagation is forced.
The characteristics of the tsunami in the Pacific triggered by the Tohoku earthquake were used as input to
the Wave‐Perturbation GITM (WP‐GITM). Meng et al. (2015) simulated three waves of amplitudes 8 cm,
10 cm, and 9 cm, respectively, moving with constant speed of 125 m/s, stopping at the West Coast of the
United States. The simulated responses in TEC are shown in Figure 10 where the perturbations in TEC
are shown in color, and the ionospheric pierce points (IPPs) are shown as black lines, numbered 1 to 16
for the GPS receivers used in validation of the simulation. Red asterisks mark the locations of the IPPs at
the times listed in the bottom, left, of each panels. Comparison of the observed TIDs with the model indicate
some agreement when the waves intersect with the IPP traces nearly perpendicularly. When the angle of
interaction is small, that is, nearly parallel incidence, the waves are of small amplitude in both receiver
and model. Other discrepancies may be due to the assumptions used the model (simple representation of tsu-
nami wave, constant tsunami wave speed). However, the model captures many of the features characteristic
of the phenomenon as can be seen in Figure 10. By varying the input, this approach may be adaptable to
other types of impulsive events.
TIDs launched by tsunamis generated by the Tohoku earthquake in March 2011 have been modeled using
physics‐based general circulation models which include self‐consistent ion‐neutral coupling (Azeem et al.,
2017; Meng et al., 2015). Observations of TIDs were made by the dense network of GPS receivers in the
United States. Figure 11 shows the perturbations in TEC for four times between 1530 and 1920 UT on 11
March 2011 (Crowley et al., 2016). The four snapshots show the TIDs as large swaths extending across much
of the continental United States. Azeem et al. (2017) used a physics‐based ionospheric model to investigate
horizontal winds. The model results showed nearly uniform winds which confirmed that the TIDs signa-
tures in Figure 11 were not due to variations in the neutral winds but were triggered by the tsunami. The
angle of propagation agrees with the propagation of the tsunami observed near the West Coast of the
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United States. Of interest is the variation in the waves due to dissipation as they propagated across the
United States, distant from their source which is presumably the Pacific Ocean.
Azeem et al. (2017) carry out a wavelet analysis of the TIDs which illustrates the variations in wave period
with longitude for selected latitudes, as shown in Figure 12. The waves display a linear correlation with long-
itude as they propagate from west to east, with wave period increasingly linearly with longitude. This is an
indication of the dispersive nature of the TID, for which the wave period is correlated with the angle that the
AGW makes with the horizontal. Given a spectrum of GWs with differing periods, the propagation angle
that an AGW makes with the horizontal (ξ) is directly dependent on the intrinsic period of the AGW (τ) such
that τ=τ
B
/sin (ξ) where τ
B
is the buoyancy frequency (Azeem et al., 2017). Longer‐period waves propagate
more horizontally, and short‐period waves propagate more vertically, giving rise to a shift in period
with longitude.
Using the relationship between wave period and angle with the horizontal cited above, Azeem et al. (2017)
found a range of horizontal wavelengths between 190 and 730 km, a consistent angle of 121.8° east of north
as the propagation angle, and phase speeds of 180–220 m/s near the West Coast of the United States, and
220–260 m/s at about 118°W longitude. In addition, analysis using the International Reference
Ionosphere and NRLMSIS‐000 model (Picone et al., 2002), the AGWs were deduced to penetrate the iono-
sphere to at least 230‐to 290‐km altitude, the approximate altitude of the Fpeak. At higher altitudes, mole-
cular viscosity and thermal conductivity have the effect of damping the waves (Hickey et al., 2009). The
analysis illustrates that wavelet decomposition can be useful in determination of source characteristics of
the observed TIDs.
A similar approach is taken by Haaser et al. (2017) in another analysis of the waves generated during the
Tohoku 2011 earthquake. Using a combination of band‐pass filtering of the TEC signals, wavelet
Figure 10. Simulated TEC perturbations over the map at 15:35 UT, 16:35 UT, 17:05 UT, and 17:35 UT. The color contours show the magnitude of the TEC perturba-
tions in TECU (total electron content unit, 1 TECU = 10
16
el/m
2
). The black curves represent the trajectories of 16 ionospheric pierce points. The red asterisks mark
the locations of ionospheric pierce points at the given time (reproduced from Figure 3 of Meng et al., 2015). TEC = total electron content.
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decomposition and cross‐correlation of the wavelets, the authors demonstrate that wavelengths, wave speed
and direction of propagation of waves in the acoustic and gravity frequency ranges can be separated. The
frequency analysis techniques enhance attribution of the TEC observations and facilitate separation of the
impulsive event from naturally occurring TIDs which form a near‐constant background.
A further modeling technique which can be applied to TIDs from impulsive events is data assimilation com-
bined with empirical or physics‐based models. These assimilative schemes have been applied to HF propa-
gation (Mitchell et al., 2017; Nickisch et al., 2016) in the presence of TIDs, but can equally be used to specify
and forecast TIDs. By assimilating data, dynamic conditions can be simulated with greater fidelity.
New numerical methods continue to advance the accuracy and extent of ionospheric modeling. In the spe-
cific area of manmade impulsive events, modeling is still relatively untested, but tools developed to treat nat-
ural explosive phenomena may prove useful in extending the simulations to characterize and forecast the
effect of manmade explosions on the ionosphere.
5.3. Space‐Based GNSS Observations
A relatively new development in extraction of TIDs from GNSS TEC measurements is the application of the
technique to spaceborne GNSS receivers. There are additional challenges in this approach since the plat-
forms from which the observations are made are in motion relative to both the sources of the wave emissions
and the GNSS satellite measuring TEC. However, a successful event study was carried out by Sun et al.
(2016), who used the GPS Radio Occultation experiment on the FORMOSAT‐3/COSMIC satellite to detect
TIDS generated by the Nepal earthquake in 2015. The space‐based observations were validated with
ground‐based GNSS measurements.
There are two advantages in spaceborne GNSS receivers. First, there is no ground motion at the receiver
location which can interfere with the analysis of TEC perturbations. Second, spaceborne receivers can access
large regions in space, including locations where ground‐based GNSS is not generally available.
Figure 11. Two‐dimensional maps of total electron content perturbation at (a) 15:30 UT, (b) 16:40 UT, (c) 17:10 UT, and (d) 19:20 UT. These maps show planar
traveling ionospheric disturbance wavefronts over the West Coast of the United States (reproduced from Figure 3 of Crowley et al., 2015).
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6. Challenges
As already mentioned, TIDs are generated by a wide range of sources. The primary challenge to ionospheric
monitoring of explosive events is reliable attribution of ionospheric perturbations. Upward forcing from the
troposphere generates GWs and AGWs which are routinely observed in the ionosphere (Forbes, 1995). These
are detected under all conditions and have their own climatology. Forcing of the ionosphere‐thermosphere
system by the solar wind causes energy to enter the high‐latitude ionosphere which generates TIDs. These
are characteristically observed to propagate away from the high‐latitude region (Hunsucker, 1982;
Richmond, 1978). A wide spectrum of impulsive events (earthquakes, mining explosions, volcano eruptions,
and rocket launches) also generate TIDs. Of these the most widely recurring are earthquakes. All of these
sources of naturally occurring TIDs provide a background against which waves generated by explosive
events must be extracted. In cases of relatively small explosions, the signal‐to‐noise ratio is close to 1 or even
less than unity. Data analytics based on machine learning may be necessary to extract the relevant waves
against the high‐noise background.
There has been little attempt to determine source characteristics or climatology of the wide range of TIDs, as
opposed to specific events or classes of event. In particular, it is unclear if TIDs generated by explosions can
be separated from the large background of naturally occurring and other manmade waves. We have
described above the characteristics of manmade versus naturally occurring waves, which indicate that wave
speeds, frequencies, and spectral coherency analysis may provide a basis for wave attribution. To date, no
systematic large‐scale analysis has been attempted. In order to evaluate the feasibility of ionospheric mon-
itoring in conjunction with seismic detection, comparative studies need to be carried out using the substan-
tial data and modeling tools now available.
An initial effort to monitor TIDs from tsunamis using GNSS‐based receivers has been described by
Savastano et al. (2017). In the study, a wavelet analysis of the variations in TEC was carried out, and con-
firmation of tsunami‐generated TIDs was made based on combination of observed wave speed and direc-
tion of propagation, combined with modeling. The authors conclude that real‐time confirmation is
Figure 12. Normalized wavelet spectrum of the traveling ionospheric disturbance period as a function of longitude for
various latitudes (reproduced from Figure 5 of Azeem et al., 2017).
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probably not yet possible, but postevent confirmation can be done. Similar efforts to classify TIDs based
on characteristic frequencies, wavelet coherence and propagation have not been attempted on any
significant scale.
Complicating the methodology of wave extraction from GNSS TEC measurements are the technical glitches
that can occur. Cycle slips due to loss of lock between receiver and satellite, low signal to noise, instrumental
problems with satellite emitter, and software failure can disrupt the measurement. In some regions such as
over oceans, or land masses with low population density, GNSS receivers are sparsely distributed. Under
these conditions, there may be insufficient data to confirm events with certainty. But until we have a quan-
titative assessment of ionospheric response as a function of the size of the event and distance to source loca-
tion, the usefulness of the technique cannot be determined.
7. Summary
We have reviewed current understanding of the ionospheric response to impulsive events, both manmade
and environmental. Currently this topic has high societal relevance because of potential violations of the
CTBT which was established to ban all nuclear testing. The CTBT includes seismic, infrasonic, hydroacous-
tic, and radionuclide monitoring. The addition of ionospheric monitoring could eliminate some false posi-
tives and thus enable more robust confirmation of explosive testing.
Earthquakes, volcano eruptions, tsunamis, industrial explosions, mine collapses, and bomb detonations
both above and below ground all generate a wide range of AWs that can be detected at varying distances
from the source. Reliable detection of GWs, AWs, and AGWs in the neutral atmosphere is complicated by
the high background of these waves which occur routinely. Extraction of signal is further complicated by
variations in atmospheric winds and temperature which are not monitored routinely.
Currently, the main characteristics used to determine if an observed TID has been generated by an impulsive
event are the following:
1. Wave period. The waves associated with impulsive events have periods of several minutes, from 3 to 30 in
published case studies, with higher wave periods observed for large aboveground nuclear detonations.
These large explosive events have wave spectra which extend to lower frequencies than less energetic
impulses.
2. Group velocity. The outward propagation speeds that have been observed range from 300 (volcanic erup-
tions) to 1000 m/s (nuclear detonations). Vertical propagation velocities of TIDs generated tsunamis are
around 50 m/s.
3. Wave spectrum. Cross‐correlation (CC) of the waveforms from two UNEs show a relatively high CC of
0.89 compared with the CC of 0.1 between the UNE and an earthquake.
4. Time delay. Many of the events which have been analyzed rely on the time delay and inferred velocity
of the waves based on the timing of the impulsive event. Sharp onset in wave activity is generally a
signature of arrival of the signal at the observing site. If the estimated wave group velocity and period
fall into the normal range for TIDs, it is concluded that the waves were generated by the explosive
event.
The ionospheric signatures of AWs and AGWs are TIDs, which have been detected widely for some time. A
range of TIDs is generated, corresponding to the same events which launch AWs and AGWs in the atmo-
sphere. A significant new aspect of TID monitoring is extraction of TID signatures via processing of TEC
from GNSS, a global network of satellites and ground receivers. As the TIDs represent perturbations in
TEC, the absolute values of TEC are not important, thus avoiding the difficulties in estimation of receiver
bias. TEC from the global network of GNSS receivers is routinely available at the International GNSS
Service website. The methodology has been tested in a limited number of event studies, using ground and
space‐based GNSS receivers. A more comprehensive test, comparing signals from seismic monitors with
observed TIDs has not been attempted.
Innovative techniques have been introduced recently. RF interferometry measurements respond to gradi-
ents in TEC, and not to the absolute values of TEC. This allows small TEC variations to be resolved which
have not detectable by other methods, greatly expanding the possibility of detection of small
underground explosions.
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In addition to innovative measurement techniques, physics‐based modeling of TIDs has advanced signifi-
cantly. By coupling ionospheric models to models which represent transport of wave energy generated in
the lower atmosphere, the entire system can be specified. This allows more robust attribution of the observed
waves. Further, data assimilation techniques can improve accuracy in specification and forecasting under
dynamic conditions. Use of sophisticated models can assist in interpretation of sparsely monitored signals
in areas where detection is difficult.
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helmboldt@nrl.navy.mil). No other
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