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Full waveform inversion (FWI) is one of the most challenging procedures to obtain quantitative information of the subsurface. For elastic inversions, when both compressional and shear velocities have to be inverted, the algorithmic issue becomes also a computational challenge due to the high cost related to modelling elastic rather than acoustic waves. This shortcoming has been moderately mitigated by using high-performance computing to accelerate 3D elastic FWI kernels. Nevertheless, there is room in the FWI workflows for obtaining large speedups at the cost of proper grid pre-processing and data decimation techniques. In the present work, we show how by making full use of frequency-adapted grids, composite shot lists and a novel dynamic offset control strategy, we can reduce by several orders of magnitude the compute time while improving the convergence of the method in the studied cases, regardless of the forward and adjoint compute kernels used.

Computational geosciences, 2017-02, Vol.21 (1), p.31-45

Acceleration strategies for elastic full waveform inversion workflows in 2D and 3D: Near offset elastic full waveform inversion

IFOS3D is a three‐dimensional elastic full‐waveform inversion (EFWI) tool designed for high‐resolution estimation of the Earth's material properties within 3D subsurface structures. However, due to the significant computational costs associated with 3D EFWI, leveraging the computing power of a supercomputer for implementation is a logical choice. In this article, we introduce several innovative process‐level and thread‐level optimizations based on heterogeneous many‐core architectures in the new Sunway supercomputer, which is a powerful system globally. These optimizations encompass a process‐level communication overlapping strategy, thread‐level data partitioning and layout approaches, a remote memory access optimized master‐slave communication scheme, and a thread‐level data reuse and overlapping strategy. Through these optimizations, we achieve significant improvements in each iteration, with a kernel function speedup of approximately 59×$$ \times $$ and an overall program speedup of about 14×$$ \times $$. Our findings demonstrate the ability of our proposed optimization strategies to overcome the computational challenges associated with 3D EFWI, providing a promising framework for future advancements in the field of subsurface imaging.
IFOS3D is a three‐dimensional elastic full‐waveform inversion tool designed for high‐resolution estimation of the Earth's material properties within 3D subsurface structures. In this article, we introduce several innovative process‐level and thread‐level optimizations based on heterogeneous many‐core architectures in the new Sunway supercomputer. Through these optimizations, we achieve significant improvements in each iteration, with a kernel function speedup of approximately 59×$$ \times $$ and an overall program speedup of about 14×$$ \times $$.

Engineering reports (Hoboken, N.J.), 2025-01, Vol.7 (1), p.n/a

Redesigning elastic full‐waveform inversion on the new Sunway architecture

In this paper, the elastic full-waveform inversion in the frequency domain based on Green functions is investigated. The method allows to image media velocities of compressional wave and shear wave by using vertical and horizontal components. The forward problem is solved by the finite difference scheme with the perfectly matched layer in the frequency domain. The inversion is an optimization iterative process to minimize the residual between the synthetic data and the observed data on the surface. A gradient method is used to find the optimization direction, preconditioned with the diagonal part of the inverse Hessian matrix. The gradient is constructed based on Green functions, which serves to compute the gradient of the objective function with respect to model parameters effectively. The inversion is completed from low frequency to high frequency so that smaller wavelengths are progressively introduced in inversion. Numerical computations for the benchmark Overthrust model demonstrate the full-waveform inversion method in this paper can inverse media velocities of complex structures accurately.

Journal of physics. Conference series, 2023-02, Vol.2444 (1), p.12003

Frequency-domain elastic full-waveform inversion based on Green functions

Elastic full waveform inversion (EFWI) is essential for obtaining high-resolution multi-parameter models. However, the conventional EFWI may suffer from severe cycle skipping without the low-frequency components in elastic seismic data. To solve this problem, we propose a multistage phase correction-based elastic full waveform inversion method in the frequency-wavenumber domain, which we call PC-EFWI for short. Specifically, the seismic data are first split using 2-D sliding windows; for each window, the seismic data are then transformed into the frequency-wavenumber domain for PC-EFWI misfit. In addition, we introduced a phase correction factor in the PC-EFWI misfit. In this way, it is possible to reduce phase differences between measured and synthetic data to mitigate cycle skipping by adjusting the phase correction factor in different scales. Numerical examples with the 2-D Marmousi model demonstrate that the frequency-wavenumber domain PC-EFWI with multistage strategy is an excellent way to reduce the risk of EFWI cycle skipping and build satisfactory start models for the conventional EFWI.

Remote sensing (Basel, Switzerland), 2022-12, Vol.14 (23), p.5916

Frequency-Wavenumber Domain Elastic Full Waveform Inversion with a Multistage Phase Correction

The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method.

Applied sciences, 2023-12, Vol.13 (24), p.13014

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

Ocean bottom seismometer (OBS) can record both pressure and displacement data by modern marine seismic acquisitions with four-component (4C) sensors. Elastic full-waveform inversion (EFWI) has shown to recover high-accuracy parameter models from multicomponent seismic data. However, due to limitation of the standard elastic wave equation, EFWI can hardly simulate and utilize the pressure components. To remedy this problem, we propose an elastic full-waveform inversion method based on a modified acoustic-elastic coupled (AEC) equation. Our method adopts a new misfit function to account for both 1C pressure and 3C displacement data, which can easily adjust the weight of different data components and eliminate the differences in the order of magnitude. Owing to the modified AEC equation, our method can simultaneously generate pressure and displacement records and avoid explicit implementation of the boundary condition at the seabed. Besides, we also derive a new preconditioned truncated Gauss–Newton algorithm to consider the Hessian associated with ocean bottom seismic 4C data. We analyze the multiparameter sensitivity kernels of pressure and displacement components and use two numerical experiments to demonstrate that the proposed method can provide more accurate multiparameter inversions with higher resolution and convergence rate.

Remote sensing (Basel, Switzerland), 2020-09, Vol.12 (17), p.2816

Multiparameter Elastic Full Waveform Inversion of Ocean Bottom Seismic Four-Component Data Based on A Modified Acoustic-Elastic Coupled Equation

Elastic full-waveform inversion (EFWI) is a quantitative data fitting procedure that recovers multiple subsurface parameters from multicomponent seismic data. As density is involved in addition to P- and S-wave velocities, the multiparameter EFWI suffers from more serious tradeoffs. In addition, compared with P- and S-wave velocities, the misfit function is less sensitive to density perturbation. Thus, a robust density reconstruction remains a difficult problem in multiparameter EFWI. In this paper, we develop an improved scattering-integral-based truncated Gauss-Newton method to simultaneously recover P- and S-wave velocities and density in EFWI. In this method, the inverse Gauss-Newton Hessian has been estimated by iteratively solving the Gauss-Newton equation with a matrix-free conjugate gradient algorithm. Therefore, it is able to properly handle the parameter tradeoffs. To give a detailed illustration of the tradeoffs between P- and S-wave velocities and density in EFWI, wavefield-separated sensitivity kernels and the Gauss-Newton Hessian are numerically computed, and their distribution characteristics are analyzed. Numerical experiments on a canonical inclusion model and a modified SEG/EAGE Overthrust model have demonstrated that the proposed method can effectively mitigate the tradeoff effects, and improve multiparameter gradients. Thus, a high convergence rate and an accurate density reconstruction can be achieved.

Journal of geophysics and engineering, 2017-12, Vol.14 (6), p.1445-1462

Density reconstruction in multiparameter elastic full-waveform inversion

The depth velocity model is a critical element for providing seismic data processing success, as it is responsible for the times of waves’ propagation and, therefore, prescribes the location of geological objects in the resulting seismic images. Constructing a deep velocity model is the most time-consuming part of the entire seismic data processing, which usually requires interactive human intervention. This article introduces the consistently numerical method for reconstructing a depth velocity model based on the modified version of the elastic Full Waveform Inversion (FWI). The specific feature of this approach to FWI is the decomposition of the space of admissible velocity models into subspaces of propagator (macro velocity) and reflector components. In turn, the latter transforms to the data space reflectivity on the base of migration transformation. Finally, we perform minimisation in two different spaces: (1) Macro velocity as a smooth spatial function; (2) Migration transforms data space reflectivity to the spatial reflectivity. We present numerical experiments confirming less sensitiveness of the modified version of FWI to the lack of the low time frequencies in the data acquired. In our computations, we use synthetic data with valuable time frequencies from 5 Hz.

Geosciences (Basel), 2021-02, Vol.11 (2), p.76

Elastic Full-Waveform Inversion Using Migration-Based Depth Reflector Representation in the Data Domain

Full waveform inversion (FWI) is one of the most challenging procedures to obtain quantitative information of the subsurface. For elastic inversions, when both compressional and shear velocities have to be inverted, the algorithmic issue becomes also a computational challenge due to the high cost related to modelling elastic rather than acoustic waves. This shortcoming has been moderately mitigated by using high-performance computing to accelerate 3D elastic FWI kernels. Nevertheless, there is room in the FWI workflows for obtaining large speedups at the cost of proper grid pre-processing and data decimation techniques. In the present work, we show how by making full use of frequency-adapted grids, composite shot lists and a novel dynamic offset control strategy, we can reduce by several orders of magnitude the compute time while improving the convergence of the method in the studied cases, regardless of the forward and adjoint compute kernels used.
The authors thank REPSOL for the permission
to publish the present research and for funding through the AURORA project. J. Kormann also thankfully acknowledges the computer
resources, technical expertise and assistance provided by the Barcelona Supercomputing Center - Centro Nacional de Supercomputacti
´on together with the Spanish Supercomputing Network (RES) through grant FI-2014-2-0009. This project has received funding from the European Union’s Horizon 2020, research and innovation programme under the Marie Skłodowska-Curie grant agreement no.
644202. The research leading to these results has received funding from the European Union’s Horizon 2020 Programme (2014–2020)
and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E
Project (www.hpc4e.eu), grant agreement no. 689772.We further want to thank the Editor Clint N. Dawson for his help, and Andreas Fichtner and an anonymous reviewer for their comments and suggestions to improve the manuscript.
Peer Reviewed

Acceleration strategies for elastic full waveform inversion workflows in 2D and 3D

A numerical method for solving an inverse problem of the theory of seismic wave propagation, relying on the modified least square method, is proposed, substantiated, and implemented. The proposed modification is based on the decomposition of the velocity model of the geological medium into the subspaces of smoothly changing propagators and sharply changing reflectors. The modified functional exhibits strong nonlinearity relative to the macro-velocity component, since the operator M, which transforms the time reflector to the space reflector, depends on it significantly. However, the low dimensions of the subspace of the macro-velocity models make it possible to use quite effective minimizing methods successfully.

Doklady earth sciences, 2018-10, Vol.482 (2), p.1365-1369

Solving an Inverse Dynamic Seismic Problem by Multicomponent Elastic Full Waveform Inversion

Unconventional oil and gas resources are poised to maintain a significant presence in the global energy landscape for the foreseeable future. Nevertheless, our comprehension of unconventional reservoirs remains limited and has not fully harnessed advancements in seismic imaging technologies. This limitation arises from the substantial development costs associated with unconventional reservoirs and the misleading simplicity of their geological structures, which often lead to overly simplistic assumptions regarding their uniformity. The introduction of distributed acoustic sensing (DAS) technology has paved the way for various geophysical applications in these reservoirs through fiber optic deployment in horizontal wells. These applications primarily focus on characterizing fractures induced by hydraulic stimulation but fall short of providing a holistic characterization of the entire reservoir.In this context, I propose harnessing the cost-effective yet invaluable DAS-recorded seismic waves generated by perforation shots within an elastic full-waveform inversion (FWI) framework. This approach aims to estimate high-resolution elastic properties of the reservoir formation, which can subsequently serve multiple purposes, including mapping heterogeneities, assessing potential hazards, refining reservoir models for resource evaluation, and monitoring stimulation and production operations.This dissertation addresses two principal challenges in pursuit of this goal. Firstly, the representation of perforation shots as seismic sources, a prerequisite for FWI, remains undefined. I develop a source model based on a superposition of three mechanisms and derive the corresponding moment tensor representation. A workflow for estimating source parameters from field DAS data is established, with the moment tensor serving as an indicator of a perforation shot's effectiveness in creating micro-cracks in the surrounding rock. Secondly, the presence of seismic sources within the layer of interest introduces significant artifacts and footprints in the FWI results, particularly susceptible to source-related errors. To mitigate these issues, I propose a simple and effective method for footprint removal while robustly updating the model in their vicinity. This method leverages illumination redundancy and extends the model along the sources. Each source updates one component of the extended model, with a regularization term ensuring mutual consistency among these components, except within their respective footprints. After demonstrating the method's resilience in the presence of source-related errors, I apply it to a field DAS dataset, successfully identifying meter-scale reservoir anomalies indicative of lower pore pressure or denser shale regions. I further adapt this method to assess the long-term stimulation effects, revealing only a mild decrease in shear wave speed.Finally, I extend my investigations to encompass source and elastic parameter estimation in a three-dimensional geometry using cross-well DAS/perforation data. The results highlight the prevalence of explosion and tensile crack mechanisms as dominant source mechanisms and emphasize significant lateral variations in elastic parameters, further underscoring the lateral heterogeneity of unconventional reservoirs.

Seismic Source and Elastic Full-Waveform Inversion Using Distributed Acoustic Sensing and Perforation Shots in Unconventional Reservoirs

Full Waveform Inversion (FWI) plays a vital role in reconstructing geophysical structures. The Uncertainty Quantification regarding the inversion results is equally important but has been missing out in most of the current geophysical inversions. Mathematically, uncertainty quantification is involved with the inverse Hessian (or the posterior covariance matrix), which is prohibitive in computation and storage for practical geophysical FWI problems. L-BFGS populates as the most efficient Gauss-Newton method; however, in this study, we empower it with the new possibility of accessing the inverse Hessian for uncertainty quantification in FWI. To facilitate the inverse-Hessian retrieval, we put together BFGS (essentially, full-history L-BFGS) with randomized singular value decomposition towards a low-rank approximation of the Hessian inverse. That the rank number equals the number of iterations makes this solution efficient and memory-affordable even for large-scale inversions. Also, based on the adjoint method, we formulate different diagonal Hessian initials as preconditioners and compare their performances in elastic FWI. We highlight our methods with the elastic Marmousi benchmark, demonstrating the applicability of preconditioned BFGS in large-scale FWI and uncertainty quantification.

arXiv.org, 2021-08

Preconditioned BFGS-based Uncertainty Quantification in elastic Full Waveform Inversion

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

Energies (Basel), 2023-08, Vol.16 (15), p.5822

Seismic Characterization of the Blue Mountain Geothermal Field

Strong-scattering medium can usually form a good sealing medium for oil and gas resources. However, conventional elastic full-waveform inversion (EFWI) methods are difficult to build reliable velocity models under the condition of lacking low-frequency information. The elastic direct envelope inversion (EDEI) method has been proven to be able to model large-scale Vp and Vs structures of strong-scattering media. The successive use of EDEI and EFWI can obtain fine structures of the strong scatterers and their shielding areas. However, the inversion effects of inner velocity and bottom boundaries of strong scatterers by the existing methods need to be improved. In this paper, we propose the elastic direct envelope inversion with anisotropic total variation constraint (EDEI-ATV). The anisotropic total variation (ATV) constraint has the advantage of making the velocity more uniform inside the layer and sharper on boundaries, which can be used to improve the inversion results of EDEI. During the iterations, the ATV constraint is directly applied to the update of Vp and Vs, and the alternately iterative algorithm can achieve good results. After obtaining reliable large-scale Vp and Vs structures, the EFWI with anisotropic total variation constraint (EFWI-ATV) is performed to obtain high-precision Vp and Vs structures. Numerical examples verify the effectiveness of the proposed method.

Remote sensing (Basel, Switzerland), 2023-02, Vol.15 (3), p.746

Strong-Scattering Multiparameter Reconstruction Based on Elastic Direct Envelope Inversion and Full-Waveform Inversion with Anisotropic Total Variation Constraint

Seismic characterization of landslides offers the potential for developing high-resolution models on subsurface shear-wave velocity profile. However, seismic methods based on reflection processing are challenging to apply in such scenarios as a consequence of the disturbance to the often well-defined structural and stratigraphic layering by the landslide process itself. We evaluate the use of alternative seismic characterization methods based on elastic full waveform inversion (E-FWI) to probe the subsurface of a landslide complex in Majes, southern Peru, where recent agricultural development and irrigation activities have altered the hydrology and groundwater table and are thought to have contributed to increased regional landslide activities that present continuing sustainability community development challenges. We apply E-FWI to a 2D near-surface seismic data set for the purpose of better understanding the subsurface in the vicinity of a recent landslide location. We use seismic first-arrival travel-time tomography to generate the inputs required for E-FWI to generate the final high-resolution 2D compressional- and shear-wave (P- and S-wave) velocity models. At distances greater than 140 m from the cliff, the inverted models show a predominantly vertically stratified velocity structure with a low-velocity near-surface layer between 5–15 m depth. At distances closer than 140 m from the cliff, though, the models exhibit significantly reduced shear-wave velocities, stronger heterogeneity, and localized shorter wavelength structure in the top 20 m. These observations are consistent with those expected for a recent landslide complex; however, follow-on geotechnical analysis is required to confirm these assertions. Overall, the E-FWI seismic approach may be helpful for future landslide characterization projects and, when augmented with additional geophysical and geotechnical analyses, may allow for improved understanding of the hydrogeophysical properties associated with suspected ground-water-driven landslide activity.

Sustainability, 2023-09, Vol.15 (18), p.13574

Seismic Characterization of a Landslide Complex: A Case History from Majes, Peru

Full waveform inversion (FWI) strongly depends on an accurate starting model to succeed. This is particularly true in the elastic regime: The cycle-skipping phenomenon is more severe in elastic FWI compared to acoustic FWI, due to the short S-wave wavelength. In this paper, we extend our work on extrapolated FWI (EFWI) by proposing to synthesize the low frequencies of multi-component elastic seismic records, and use those "artificial" low frequencies to seed the frequency sweep of elastic FWI. Our solution involves deep learning: we separately train the same convolutional neural network (CNN) on two training datasets, one with vertical components and one with horizontal components of particle velocities, to extrapolate the low frequencies of elastic data. The architecture of this CNN is designed with a large receptive field, by either large convolutional kernels or dilated convolution. Numerical examples on the Marmousi2 model show that the 2-4Hz low frequency data extrapolated from band-limited data above 4Hz provide good starting models for elastic FWI of P-wave and S-wave velocities. Additionally, we study the generalization ability of the proposed neural network over different physical models. For elastic test data, collecting the training dataset by elastic simulation shows better extrapolation accuracy than acoustic simulation, i.e., a smaller generalization gap.

arXiv.org, 2020-12

Deep learning for low frequency extrapolation of multicomponent data in elastic full waveform inversion

Elastic full-waveform inversion (EFWI) is a process used to estimate subsurface properties by fitting seismic data while satisfying wave propagation physics. The problem is formulated as a least-squares data fitting minimization problem with two sets of constraints: Partial-differential equation (PDE) constraints governing elastic wave propagation and physical model constraints implementing prior information. The alternating direction method of multipliers is used to solve the problem, resulting in an iterative algorithm with well-conditioned subproblems. Although wavefield reconstruction is the most challenging part of the iteration, sparse linear algebra techniques can be used for moderate-sized problems and frequency domain formulations. The Hessian matrix is blocky with diagonal blocks, making model updates fast. Gradient ascent is used to update Lagrange multipliers by summing PDE violations. Various numerical examples are used to investigate algorithmic components, including model parameterizations, physical model constraints, the role of the Hessian matrix in suppressing interparameter cross-talk, computational efficiency with the source sketching method, and the effect of noise and near-surface effects.

arXiv.org, 2024-04

Robust elastic full-waveform inversion using an alternating direction method of multipliers with reconstructed wavefields

Full-Waveform Inversion (FWI) is a nonlinear iterative seismic imaging technique that, by reducing the misfit between recorded and predicted seismic waveforms, can produce detailed estimates of subsurface geophysical properties. Nevertheless, the strong nonlinearity of FWI can trap the optimization in local minima. This issue arises due to factors such as improper initial values, the absence of low frequencies in the measurements, noise, and other related considerations. To address this challenge and with the advent of advanced machine-learning techniques, data-driven methods, such as deep learning, have attracted significantly increasing attention in the geophysical community. Furthermore, the elastic wave equation should be included in FWI to represent elastic effects accurately. The intersection of data-driven techniques and elastic scattering theories presents opportunities and challenges. In this paper, by using the knowledge of elastic scattering (physics of the problem) and integrating it with machine learning techniques, we propose methods for the solution of time-harmonic FWI to enhance accuracy compared to pure data-driven and physics-based approaches. Moreover, to address uncertainty quantification, by modifying the structure of the Variational Autoencoder, we introduce a probabilistic deep learning method based on the physics of the problem that enables us to explore the uncertainties of the solution. According to the limited availability of datasets in this field and to assess the performance and accuracy of the proposed methods, we create a comprehensive dataset close to reality and conduct a comparative analysis of the presented approaches to it.

arXiv.org, 2024-11

Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification

Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/

arXiv.org, 2023-09

(\mathbf{\mathbb{E}^{FWI}}\): Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

The increased emission of greenhouse gases into the atmosphere, causing climate changes, leads to a strong requirement of renewable energy resources. However, they are intermittent and need buffer storage to bridge the time gap between production and public demands. The injection of gas (e.g. compressed air or hydrogen) in sealed underground structures like salt caverns is one approach to solve this problem. Possible risks related to cavern storage are gas leakages from the injection tube into the surrounding sediments, material failure in salt rock surrounding the cavern during irregular operation and in the most extreme case a partial collapse of the cavern. For the early detection of these problems, a geophysical monitoring strategy is required. The objective of this paper was to map possible leakage paths outside of the salt structures and local failures within the cavern walls by the localization of crack-induced microseismic events. Classical methods require arrival time picking and phase identification. An alternative approach is elastic reverse-time modelling (RTMOD), where the recorded microseismic events are numerically backpropagated from the receiver positions into the elastic underground model. The resulting seismic wavefield focuses at the location of the event, which can be subsequently imaged by estimating the maximum of the seismic energy at each underground point. However, the success of this approach highly depends on the used elastic background model. In case of complex salt bodies, the strong velocity contrast between the salt and the surrounding sediments is a major problem. Therefore, we propose a combined monitoring approach, consisting of a seismic full waveform inversion of active source reflection seismic data to accurately image the background velocity model and subsequent RTMOD for the microseismic event localization. Accuracy and sensitivity with respect to the acquisition geometry and random noise will be demonstrated using a complex benchmark model. Furthermore, the localization accuracy is discussed for three different scenarios covering the detection of a partial cavern collapse, a gas leakage and the occurrence of cracks within the cavern wall due to extreme loading conditions during irregular operation.