Remarks on the micro-earthquake detection problem: Refining the outcome using stochastic modeling

Abstract

Methodologies dealing with the detection of micro-earthquakes and the accurate estimation of body waves' arrival time constitute, during the last decades, a topic of ongoing research. The extraction and efficient analysis of the useful information from the continuous recordings is of great importance, since it is a prerequisite for reliable interpretations. Small magnitude seismic events, either naturally-occuring or induced, have been increasingly used in a wide range of industrial fields, with applications ranging from hydrocarbon and geothermal reservoir exploration, to passive seismic tomography surveys. A great number of algorithms have been proposed and applied up to now for seismic event detection, exploiting specific properties of the seismic signals both in time and in frequency domain, with the energy-based detectors (STA/LTA) to be the most commonly used, due to their simplicity and the low computational cost they require. A significant obstacle emerging at seismological identification problems lies on the fact that such processes usually suffer from a number of false alarms, which is significantly increased in extremely noisy environments. For that scope, we propose a "Decision-Making" mechanism, independent of the applied detection algorithm, which controls the results obtained during the detection process by minimizing false detections and providing the best possible outcome for further analysis. The specific scenario is based on the comparison among autoregressive models estimated on isolated seismic noise recordings, as well as on the detected intervals that resulted during the event identification procedure. A number of examples, associated with the implementation of the proposed scenario on real data, is presented with the scope of evaluating its performance. Several issues concerning the isolation of the seismic noise from the raw data, the estimation of the autoregressive models, the choice of the orders of the stochastic models etc., are discussed.

Full text

EGU2020-9095
https://doi.org/10.5194/egusphere-egu2020-9095
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Remarks on the micro-earthquake detection problem: Refining the
outcome using stochastic modeling
Athanasios Lois1, Fotis Kopsaftopoulos2, Dimitrios Giannopoulos1,3, Katerina Polychronopoulou1,
and Nikos Martakis1
1Seismotech S.A., Research and Development, Marousi, Greece (lois@seismotech.gr)
2Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace, and Nuclear Engineering, Troy, NY, USA
3Laboratory of Geophysics & Seismology, Department of Natural Resources & Environment, Hellenic Mediterranean
University, Chania, Greece
Methodologies dealing with the detection of micro-earthquakes and the accurate estimation of
body waves’ arrival time constitute, during the last decades, a topic of ongoing research. The
extraction and efficient analysis of the useful information from the continuous recordings is of
great importance, since it is a prerequisite for reliable interpretations. Small magnitude seismic
events, either naturally-occuring or induced, have been increasingly used in a wide range of
industrial fields, with applications ranging from hydrocarbon and geothermal reservoir
exploration, to passive seismic tomography surveys.
A great number of algorithms have been proposed and applied up to now for seismic event
detection, exploiting specific properties of the seismic signals both in time and in frequency
domain, with the energy-based detectors (STA/LTA) to be the most commonly used, due to their
simplicity and the low computational cost they require. A significant obstacle emerging at
seismological identification problems lies on the fact that such processes usually suffer from a
number of false alarms, which is significantly increased in extremely noisy environments.
For that scope, we propose a “Decision-Making” mechanism, independent of the applied detection
algorithm, which controls the results obtained during the detection process by minimizing false
detections and providing the best possible outcome for further analysis. The specific scenario is
based on the comparison among autoregressive models estimated on isolated seismic noise
recordings, as well as on the detected intervals that resulted during the event identification
procedure. A number of examples, associated with the implementation of the proposed scenario
on real data, is presented with the scope of evaluating its performance. Several issues concerning
the isolation of the seismic noise from the raw data, the estimation of the autoregressive models,
the choice of the orders of the stochastic models etc., are discussed.
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