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Resulting model of the hydraulic saturation for the petrophysical joint inversion of simulated ERT and TT data.
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Many tasks in applied geosciences cannot be solved by single measurements, but require the integration of geophysical, geotechnical and hydrological methods. Numerical simulation techniques are essential both for planning and interpretation, as well as for the process understanding of modern geophysical methods. These trends encourage open, simple,...
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Citations
... The tomography pictures of seismic refraction or time groups were generated using the pygimli.physics.traveltime module of the pyGIMLi, as described by Rücker et al. (2017). The inversion approach relies on several meshes. ...
A portion of the west of Mexico City is densely populated in an abrupt topography, whose volcano-sedimentary materials increase the likelihood of landslides. We exploited the geometry of a quadrangular geophones array to apply Seismic Refraction Tomography (SRT) and Ambient Noise Tomography (ANT) methods and explore the extent of landslide-prone materials. The results show low-velocity areas (Vs < 100 m/s, being Vs group velocities) associated with materials that have lost their resistance due to the increase in pore pressure and the places where eventually, more landslides will occur (120 < Vs < 200 m/s) if mitigation work is not carried out. The most stable zones correspond to materials with velocity values greater than 250 m/s that overlap a bedrock at an average depth of 8 m. Thus, when it is not advisable to perform active source experiments, ANT can provide practical results to determine the extension of the sliding materials.
... The data were generated using publicly available software packages. For simulating electrical potential distributions we used the Python package pyGIMLi (Rücker et al., 2017). Flow and transport simulations were run using Floppy (Bakker et al., 2016), MODFLOW-2005(Harbaugh, 2005 and MT3D-USGS (Bedekar et al., 2016). ...
Quantitative estimates of hydrological state variables using electrical or electromagnetic geophysical methods are systematically biased by overlooked heterogeneity below the spatial scale resolved by the method. We generalize the high‐salinity asymptotic limit of electrical conduction in porous media at the continuous (e.g., Darcy) scale, by introducing a new petrophysical parameter, the mixing factor M, which accounts for the effect of fluid conductivity heterogeneity on the equivalent electrical conductivity tensor; it is expressed in terms of the volume‐average of the product of mean‐removed fluid conductivity and electric fields. We investigate the behavior of M for static and evolving fluid conductivity scenarios. Considering 2‐D ergodic log‐normal random fields of fluid conductivity, we demonstrate, in absence of surface conductivity, that observing the components of the M‐tensor allows univocally determining the variance and anisotropy of the field. Further, time‐series of the M‐tensor under diffusion‐limited mixing allows distinguishing between different characteristic temporal scales of diffusion, which are directly related to the initial integral scales of the salinity field. Under advective‐diffusive transport and for a pulse injection, the time‐series of M have a strong dependence on the Péclet number. Since M is defined in the absence of surface conductivity, we investigate how to correct measurements for surface conductivity effects. The parameter M provides conceptual understanding about the impact of saline heterogeneity on electrical measurements. Further work will investigate how it can be incorporated into hydrogeophysical inverse formulations and interpretative frameworks.
... This study focuses on the datasets from 2015 to 2022 with comparable measurement dates (first week of September). ERT and RST data were individually inverted using the opensource library pyGIMLi (Rücker et al. 2017) to derive the electrical resistivity and P-wave velocity distribution (Section 4.4). ...
This study investigates the ground characteristics of the high altitude (3’410 m a.s.l.) permafrost site Stockhorn in the Swiss Alps using a combination of surface and subsurface temperature, soil moisture, electrical resistivity and P-wave velocity time series data including a novel approach to explicitly quantify changes in ground ice content. This was motivated by the clear signal of permafrost degradation visible in the full available dataset at this long-term monitoring site within the PERMOS (Permafrost Monitoring Switzerland) network. First, we assess the temporal and spatial evolution of the ground ice and water content by combining and analysing all available in situ thermal (borehole and ground surface temperature), hydrological (soil moisture) and geophysical (geoelectric and seismic refraction) data over two decades (2002-2022) regarding the driving factors for the spatially different warming. Secondly, we explicitly quantify the volumetric water and ice content and their changes in the subsurface from 2015 to 2022 using a time-consistent petrophysical joint inversion scheme using the open-source library pyGIMLi. The petrophysical joint inversion scheme has been improved by constraining the rock content to be constant in time for six subsequent inversions. This allows us to obtain consistent changes in ice and water content over the monitoring period based on jointly inverted resistivity and traveltime data. All the different data show a warming trend of the permafrost. The ice content calculated from the petrophysical joint inversion has decreased by about 15 vol.% between 2015 and 2022. Changes in ice content are first observed in the lower, south-facing part of the profile. As a result, resistivity and P-wave velocity have been decreasing significantly. Permafrost temperatures measured in the boreholes have increased between 0.5 and 1°C in 20 years. Our study shows the high value of joint and quantitative analysis of datasets comprising complementary subsurface variables for long-term permafrost monitoring.
... Several studies in the literature have made attempts to integrate ERT with geotechnical data for subsurface soil characterization (Braga et al., 1999;Cosenza et al., 2006;Stan and Stan-Kłeczek, 2014;Sudha et al., 2009). Recent strides in petrophysical reconstruction from geophysical models, aided by open-source Python codes for data inversion (Rücker et al., 2017), have paved the way for promising endeavors. ...
... lution unique (Aki and Lee, 1976;Tarantola, 2005;Aster et al., 2018). Many codes have been developed using this class of methods (Rawlinson, 2005;Rücker et al., 2017;Afanasiev et al., 2019;Wathelet et al., 2020;Komatitsch et al., 2023). However, since regularization is often chosen using ad-hoc criteria, these methods produce deliberately biased results, and valuable information can be concealed in the process (Zhdanov, 2002). ...
Bayesian inference has become an important methodology to solve inverse problems and to quantify uncertainties in their solutions. Variational inference is a method that provides probabilistic, Bayesian solutions efficiently by using optimisation. In this study we present a Python Variational Inversion Package (VIP), to solve inverse problems using variational inference methods. The package includes automatic differential variational inference (ADVI), Stein variational gradient descent (SVGD) and stochastic SVGD (sSVGD), and provides implementations of 2D travel time tomography and 2D full waveform inversion including test examples and solutions. Users can solve their own problems by supplying an appropriate forward function and a gradient calculation code. In addition, the package provides a scalable implementation which can be deployed easily on a desktop machine or using modern high performance computational facilities. The examples demonstrate that VIP is an efficient, scalable, extensible and user-friendly package, and can be used to solve a wide range of low or high dimensional inverse problems in practice.
... In each dataset of the experiment, the signal-to-noise ratio remained high even at larger offsets, therefore no stacking procedure was applied on the seismic traces to attenuate noise. The picked first arrivals were inverted through the C++/Python-based library pyGIMLi (Rücker et al., 2017), discretizing the subsoil model with an unstructured triangular mesh. PyGIMLi utilizes a shortest path algorithm (Moser, 1991;White, 1989) which models seismic energy propagation as ray. ...
A well-developed soil structure is crucial for a fertile soil and rules its influence on the ecosystem. Slight variations in the way soil components are organized and connected can greatly impact the soil mechanical and hydraulic characteristics. However, these features are challenging to measure at a scale that is relevant for agricultural management. In this study, we assess the capability of two seismic geophysical methods, i.e. Seismic Refraction Tomography and Multichannel Analysis of Surface Waves, to monitor the effects of compaction on agricultural bare soil. The purpose is to highlight the different mechanical response caused both by soil plastic deformation and soil water distribution due to increasing compaction. Results demonstrate that both geophysical techniques provide sufficient information to capture the effects of soil compaction and distinguish its significance , while traditional direct measurements, being punctual, lack sufficient spatial coverage. P-wave velocities carry a strong imprint of soil compaction, provided by seismic refraction, incorporating the information given by the multiphase medium in its entirety. On the other hand, S-wave velocity derived from Surface Waves discriminates the effect of solid matrix structure. This work, moreover, aims to pave the way for seismic methods to spatially characterize compaction at field scale, illustrating its potential and suggesting possible developments.
... Measured resistances were hereby inverted accounting for the surface topography. Comparison studies using other inversion algorithms (CRTOMO (Kemna 2000), pyGIMLi/BERT (Rücker et al. 2017), did not yield fundamentally different results. To obtain representative values for the resistivity of active and permafrost layers we used the so-called ZOI (zoneof-interest) technique (after Hilbich et al. 2022). ...
Warming permafrost has been detected worldwide and is projected to continue during the next century by many modelling studies. In mountain regions, this can lead to potentially hazardous impacts on short time-scales by an increased tendency for slope instabilities. However, time scales of permafrost thaw and the role of the ice content are less clear, especially in heterogeneous mountain terrain, where ice content can vary between zero and supersaturated conditions over small distances. Warming of permafrost near the freezing point shows therefore complex inter-annual behaviour due to latent heat effects during thawing and the influence of the snow-cover, which is governed by highly non-linear processes itself. Here, we demonstrate a preconditioning effect within near-surface layers in mountain permafrost that causes non-linear degradation and accelerates thaw. We hypothesise that a summer heat wave, as has occurred in the Central European summers 2003, 2015 and 2022, will enhance permafrost degradation if the active layer and the top of the permafrost layer are already preconditioned, i.e. have reduced latent heat content. This preconditioning can already be effectuated by a singular warm year, leading to exceptionally strong melting of the ground ice. On sloping terrain this ice-loss can be considered as irreversible, as large parts of the melted water will drain during the process, and an equivalent build-up of ice in cold years does not happen on similar time-scales as the melting. We propose a simple geophysical approach based on electrical resistivity tomography surveys that can assess the state of preconditioning in the absence of boreholes. In addition, we will show that resistivity data from a total of 124 permafrost sites in the Andes, Europe, and Antarctic adhere to a distinct power law behaviour between unfrozen and frozen states, which confirms the consistent electrical behaviour of permafrost and active layer materials over a wide range of landforms and material composition.
... is the relaxation time, with f c being the characteristic frequency at which the maximum of the imaginary part of the complex resistivity is observed. The ratio of the chargeability and the ρ 0 gives the normalized chargeability: We obtained the fit of the PM to our measured spectra (field and laboratory data) using routines developed by Maximilian Weigand, which are part of the Python geophysical imaging and modeling library pyGIMLi [42]. ...
Graphite, a critical raw material, prompts interest in assessing former quarries for volumetric content, driving the need for accurate prospection techniques. We explore the efficacy of spectral induced polarization (SIP) imaging at field scale for this purpose. Field measurements in a quarry with unknown graphite content underscore the need for assessment before drilling due to abrupt topography. Due to the lack of ground truth required to calibrate existing petrophysical models, we propose using SIP laboratory measurements to achieve the quantitative interpretation of the imaging results. We conducted experiments at two scales: rock plugs for material response and ground rocks of varying sizes for textural analysis. The rock plugs allow us to investigate the response of the material, while the ground samples permit us to understand changes in the SIP response for varying textural properties. Our lab work establishes power-law relationships between polarization (expressed in terms of normalized chargeability) and graphite content, as well as relaxation time and grain size. Salinity dependence is noted between chargeability, normalized chargeability, and relaxation time. Utilizing these findings, we provide a quantitative interpretation of field SIP imaging results.
... Such analyses of geophysical signals in the laboratory are essential for isolating processes and facilitating the monitoring of complex, interconnected processes. In terms of data processing and inversion, the recent development of open-source geophysical inversion libraries has opened the door to multiphysics joint inversion (Cockett et al., 2015;Rücker et al., 2017). The joint inversion of multimethod data is also key to disentangling the geophysical signals arising from various processes in the field, especially when coupled to process-based hydrological or reactive transport models (e.g., Hermans et al., 2016;Hu et al., 2020;Lesparre et al., 2020). ...
Geophysical methods have long been used in earth and environmental science for the characterization of subsurface properties. While imaging the subsurface opens the “black box” of subsurface heterogeneity, we argue here that these tools can be used in a more powerful way than characterization, which is to develop and test hypotheses. Critical zone science has opened new questions and hypotheses in the hydrologic sciences holistically around controls on water fluxes between surface, biological, and underground compartments. While groundwater flows can be monitored in boreholes, water fluxes from the atmosphere to the aquifer through the soil and the root system are more complex to study than boreholes can inform upon. Here, we focus on the successful application of various geophysical tools to explore hypotheses in critical zone hydrogeology and highlight areas where future contributions could be made. Specifically, we look at questions around subsurface structural controls on flow, the dimensionality and partitioning of those flows in the subsurface, plant water uptake, and how geophysics may be used to constrain reactive transport. We also outline areas of future research that may push the boundaries of how geophysical methods are used to quantify critical zone complexity.
... To further evaluate our model, we used the open-source pyGIMLi package (Rücker et al., 2017) to build a 2D V P profile based on our velocity-depth relations and simulate the Apollo 14 and 16 active seismic experiments. ...
Seismic methods will be useful for future lunar near‐surface characterization, and high‐fidelity elastic models will be required to aid interpretation of seismic observations. To develop an elastic lunar near‐surface model, we performed ultrasonic velocity measurements of lunar regolith simulant at low confining pressure and developed a rock physics model calibrated to these measurements. Grain contact models based on Hertz‐Mindlin theory produce accurate results at high confining pressure (i.e., several hundred meters or more burial depth) but historically fail to predict observed velocities in unconsolidated media at low pressure. Therefore, we heuristically modified existing models to fit our measured data over a range of porosities and confining pressures. To compare with Apollo 14 and 16 active seismic experiments, we used our new heuristic rock physics model to produce lunar subsurface velocity profiles. We performed ray tracing through our velocity profiles to calculate seismic traveltime, which results in good agreement with first arrivals interpreted from the Apollo experiments. Our model suggests a slightly higher velocity‐pressure dependence than inferred from in situ measurements, which may be due to porosity reduction in the lunar regolith from impact‐induced and natural vibrations.
