Crustal and upper mantle model of the Middle East based on full-waveform inversion
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Uncertainty quantification of single and multi-parameter full-waveform inversion through a variational autoencoder
This poster will be presented at IMAGE as well as for a Sandia-UT student poster session next week
Elmeliegy, Abdelrahman M↗
ML-Based Rock Properties and Seismic Volume Enhancement
This project aims to improve field-scale Carbon Capture and Storage (CCS) assessments by enhancing petrophysical and geophysical log predictions through machine learning and neural networks. In our work during EY23, we applied Conditional Variational Autoencoders (CVAEs) to predict compressional velocity (Vp) and assess CO2 saturation levels in geological formations at the Illinois Basin Decatur Project (IBDP). In another task, we improved full-waveform inversion (FWI) methods with machine-learning approaches using lithological constraints. Full-waveform inversion (FWI) of seismic data estimates the elastic properties of subsurface rocks with high spatial resolution.
Nathanail, Athanasios↗
Machine learning for seismic low-frequency extrapolation
The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.
58 GEOSCIENCES↗
Toward more-robust, AI-enabled subsurface seismic imaging for geotechnical applications
Non-invasive seismic imaging has the potential to cost-effectively evaluate large volumes of subsurface material to inform geotechnical site investigation. However, seismic imaging using full waveform inversion (FWI) requires significant computational time and is dependent on an initial starting model. As a result, FWI has not yet been widely adopted into geotechnical practice. Previous efforts, on relatively simple two-layered models, indicate that data-driven artificial intelligence (AI) models may be as effective as FWI at predicting 2D images of shear wave velocity (V s ). Furthermore, the AI model predictions can be made almost instantaneously after data acquisition and do not require an initial starting model. We examine the generality of these findings by developing a new AI model for subsurface seismic imaging, whereby we make several notable contributions. First, we architect a multimodal AI model that combines time- and frequency-domain representations of the seismic wavefield to predict a 50 m by 20 m subsurface image of V s . Second, we developed a new diverse dataset of 100,000 images with their corresponding seismic wavefields to train the AI model. Third, we propose four physics-informed data augmentations for data-driven seismic imaging. Fourth, we develop two prediction consistency tests to evaluate the model’s performance when the true subsurface is unknown. Our final model, which has been made publicly available, is capable of predicting a subsurface V s image from a single seismic wavefield with an average, mean absolute percent error (MAPE) of 24 %. The predictive model is applied to a field dataset and shown to be consistent with local geology and shear-wave refraction measurements from the same location.
Artificial intelligence↗
The Collaborative Seismic Earth Model: Generation 2
Geological interpretations, earthquake source inversions and ground motion modeling, among other applications, require models that jointly resolve crustal and mantle structure. With the second generation of the Collaborative Seismic Earth Model (CSEM2), we present a global multi-resolution tomographic Earth model that serves this purpose. The model evolves through successive regional- and global-scale refinements. While the first generation aggregated regional models, with this study, we ensure consistency between all individual submodels, resulting in a model that accurately explains wave propagation across scales. Recent regional tomographic models were incorporated, comprising continental-scale inversions for Asia and Africa, as well as regional inversions for the Western US, Central Andes, Iran, and Southeast Asia. Across all regional refinements, over 793,000 source-receiver pairs contributed. Moreover, the long-wavelength Earth model (LOWE) introduces large-scale structures outside of pre-existing local refinements. A full-waveform inversion for global anisotropic P-and S-wave speed structure over a total of 194 iterations with a minimum period of 50 s on a large data set of 1 hr of waveform data from 2,423 earthquakes and over 6 million source-receiver pairs ensures that regional updates in the crust and uppermost mantle translate into updates of deeper, global-scale structure. To test the performance of CSEM2, we evaluate waveform fits between observed and synthetic seismograms at 50 s for an independent data set on the global scale, and on the regional scale for lower periods. We accurately simulate waveforms within and across regional refinements, maintaining the original resolution of the submodels embedded in the global framework.
58 GEOSCIENCES↗
Paired autoencoders for likelihood-free estimation in inverse problems
Abstract We consider the solution of nonlinear inverse problems where the forward problem is a discretization of a partial differential equation. Such problems are notoriously difficult to solve in practice and require minimizing a combination of a data-fit term and a regularization term. The main computational bottleneck of typical algorithms is the direct estimation of the data misfit. Therefore, likelihood-free approaches have become appealing alternatives. Nonetheless, difficulties in generalization and limitations in accuracy have hindered their broader utility and applicability. In this work, we use a paired autoencoder framework as a likelihood-free estimator (LFE) for inverse problems. We show that the use of such an architecture allows us to construct a solution efficiently and to overcome some known open problems when using LFEs. In particular, our framework can assess the quality of the solution and improve on it if needed. We demonstrate the viability of our approach using examples from full waveform inversion and inverse electromagnetic imaging.
Chung, Matthias (ORCID:0000000178224539)↗
The 26 December 2004 tsunami source estimated from satellite radar altimetry and seismic waves
The 26 December 2004 Indian Ocean tsunami was the first earthquake tsunami of its magnitude to occur since the advent of both digital seismometry and satellite radar altimetry. Both have independently recorded the event from different physical aspects. The seismic data has then been used to estimate the earthquake fault parameters, and a three-dimensional ocean-general-circulation-model (OGCM) coupled with the fault information has been used to simulate the satellite-observed tsunami waves. Here we show that these two datasets consistently provide the tsunami source using independent methodologies of seismic waveform inversion and ocean modeling. Cross-examining the two independent results confirms that the slip function is the most important condition controlling the tsunami strength, while the geometry and the rupture velocity of the tectonic plane determine the spatial patterns of the tsunami.
ocean general circulation model (OGCM)↗
Memory-efficient nonsmooth dynamic optimization using adaptive randomized compression
Dynamic optimization problems arise in many applications including flow control, full waveform inversion, and medical imaging. These problems are plagued by significant computational challenges. One such challenge — and the focus of this work — is the memory limitation induced by the size of the underlying dynamical system. In particular, the entire dynamic trajectory is required for derivative computation and therefore must be stored or recomputed using, e.g., checkpointing. Although recent work demonstrated the use of adaptive randomized sketching to overcome the memory challenge, that work only applies to smooth unconstrained problems, prohibiting its use for nonsmooth regularized and constrained problems. The inclusion of nonsmooth regularizers and constraints is critical as they often arise in an attempt to preserve certain physical properties or to promote sparsity. To solve these problems, we introduce a trust-region algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function that leverages randomized sketching to compress the dynamical system trajectories and adaptively adjust the sketch rank to satisfy a gradient inexactness condition. We prove convergence of this algorithm and demonstrate that it achieves substantial memory reduction on three discretized PDE-constrained optimization applications.
97 MATHEMATICS AND COMPUTING↗
Intraplate earthquakes and the state of stress in oceanic lithosphere
The dominant sources of stress relieved in oceanic intraplate earthquakes are investigated to examine the usefulness of earthquakes as indicators of stress orientation. The primary data for this investigation are the detailed source studies of 58 of the largest of these events, performed with a body-waveform inversion technique of Nabelek (1984). The relationship between the earthquakes and the intraplate stress fields was investigated by studying, the rate of seismic moment release as a function of age, the source mechanisms and tectonic associations of larger events, and the depth-dependence of various source parameters. The results indicate that the earthquake focal mechanisms are empirically reliable indicators of stress, probably reflecting the fact that an earthquake will occur most readily on a fault plane oriented in such a way that the resolved shear stress is maximized while the normal stress across the fault, is minimized.
Bergman, Eric A.↗
Oceanic earthquakes and the tectonic evolution of oceanic lithosphere
The body waveform inversion method of Nabelek (1984) is used to study the centroid depths and source properties of oceanic earthquakes. The source parameters for 50 earthquakes which occurred along slowly spreading midocean ridges between 1962 and 1983 are used to examine the mechanical characteristics of the median valley, including the water depth in the epicentral region, the depth range of seismic faulting, the centroid depth and seismic moment versus spreading rate, and the seismic moment budget. The locations and source characteristics of oceanic intraplate earthquakes are discussed, including near-ridge earthquakes, lithospheric stress, and earthquakes in older oceanic lithosphere. The results suggest that the median valley form by the necking of a strong layer. The properties of near-ridge earthquakes support the hypothesis that thermal stress generated by diferential cooling of the plate can be stored and accumulated over millions of years. Earthquakes in older oceanic lithosphere are most likely to reflect stresses generated by plate driving forces.
Solomon, Sean C.↗
Design and Demonstration of the Mars Organic Molecule Analyzer (MOMA) on the ExoMars 2018 Rover
The Mars Organic Molecule Analyzer (MOMA) investigation is a key astrobiology experiment scheduled to launch on the joint ESA-Roscosmos ExoMars 2018 rover mission. MOMA will examine the chemical composition of geological samples acquired from depths of up to two meters below the martian surface, where fragile organic molecules may be protected from destructive cosmic radiation and/or oxidative chemical reactions. The heart of the MOMA mass spectrometer subsystem (i.e., MOMA-MS) is a miniaturized linear ion trap (LIT) that supports two distinct modes of operation to detect: i) volatile and semi-volatile, low-to-moderate mass organics (less than or equal to 500 Da) via pyrolysis coupled with gas chromatography mass spectrometry (pyr/GCMS); and, ii) more refractory, moderate-to-high mass compounds (up to 1000 Da) via laser desorption (LDMS) at ambient Mars pressures. Additionally, the LIT mass analyzer enables selective ion trapping via multi-frequency waveform ion excitation (e.g., stored waveform inverse Fourier transform, or SWIFT), and structural characterization of complex molecules using tandem mass spectrometry (MSIMS). A high-fidelity Engineering Test Unit (ETU) of MOMAMS, including the LIT subassembly, dual-gun electron ionization source, micropirani pressure gauge, solenoid-driven aperture valve, redundant detection chains, and control electronics, has been built and tested at NASA GSFC under relevant operational conditions (pressure, temperature, etc.). Spaceflight qualifications of individual hardware components and integrated subassemblies have been validated through vibration, shock, thermal, lifetime, and performance evaluations. The ETU serves as a pathfinder for the flight model buildup, integration and test, as the ETU meets the form, fit and function of the flight unit that will be delivered to MPS in late 2015. To date, the ETU of MOMA-MS has been shown to meet or exceed all functional requirements, including mass range, resolution, accuracy, instrumental drift, and Iimit-of-detection specifications, thereby enabling the primary science objectives of the MOMA investigation and ExoMars 2018 mission.
Mars Organic Molecule Analyzer↗
Mars Organic Molecule Analyzer (MOMA) Laser Desorption/Ionization Source Design and Performance Characterization
The Mars Organic Molecule Analyzer (MOMA), a dual-source, ion trap-based instrument capable of both pyrolysis-gas chromatography mass spectrometry (pyr/GC-MS) and laser desorption/ionization mass spectrometry (LDI-MS), is the core astrobiology investigation on the ExoMars rover. The MOMA instrument will be the first spaceflight mass analyzer to exploit the LDI technique to detect refractory organic compounds and characterize host mineralogy; this mode of analysis will be conducted at Mars ambient conditions. In order to achieve high performance in the Martian environment while keeping the instrument compact and low power, a number of innovative designs and components have been implemented for MOMA. These include a miniaturized linear ion trap (LIT), a fast actuating aperture valve with ion inlet tube, and a Microelectromechanical System (MEMS) Pirani sensor. Advanced analytical capabilities like Stored Waveform Inverse Fourier Transform (SWIFT) for selected ion ejection and tandem mass spectrometry (MS/MS) are realized in LDI-MS mode, and enable the isolation and enhancement of specific mass ranges and structural analysis, respectively. We report here the technical details of these instrument components as well as system-level analytical capabilities, and we review the applications of this technology to Mars and other high-priority targets of planetary exploration.
Xiang Li↗
3D seismic imaging of a fracture damage zone controlling reservoir compartmentalization at the Raft River EGS using multi-azimuth walkaway VSP
Accurate imaging of steeply dipping fracture zones in crystalline enhanced geothermal systems (EGS) is critical for constraining permeability architecture and guiding stimulation design. However, such structures remain poorly resolved by conventional surface seismic methods. We present a fully three-dimensional (3D) elastic-waveform inversion-migration workflow applied to multi-azimuth walkaway vertical seismic profiling (VSP) data acquired in a deviated borehole at the Raft River EGS. The workflow integrates first-arrival traveltime tomography, multi-scale elastic waveform inversion (EWI), and elastic least-squares reverse-time migration (ELSRTM) to recover high-resolution compressional-and shear-wave velocity models and to image structural discontinuities in the crystalline basement. The results reveal a laterally continuous low-velocity anomaly, with shear-wave velocity reductions of 25-30%, consistent with fractureinduced mechanical weakening. Two steeply dipping discontinuities bound a 50-80 m wide fracture damage zone. Independent constraints from microseismic clustering and geochemical compartmentalization corroborate the geometry and structural significance of this feature. Synthetic modeling further confirms that structures of this scale are resolvable (~30 m). These findings indicate that the Narrows structure is a distributed fracture damage zone rather than a discrete fault plane. By resolving fracture-zone geometry at the tens-of-meters scale using a single borehole, this workflow provides a practical and transferable approach for improving structural characterization, reducing uncertainty in permeability architecture, and supporting reservoir modeling and stimulation design in fractured crystalline EGS reservoirs.
58 GEOSCIENCES↗
3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification
Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.
Um, Evan Schankee [Lawrence Berkeley National Labo↗
Earthquake source mechanisms and transform fault tectonics in the Gulf of California
The source parameters of 19 earthquakes in the Gulf of California were determined from an inversion of long-period P and SH waveforms. The inversion procedure is described and the estimated precision of the derived source parameters is examined, with particular attention given to source complexity, the resolution of slip vector azimuth, and the resolution of centroid depth. The implications of these earthquake source characteristics for the tectonic evolution of the gulf are discussed.
Goff, John A.↗
GLAD-M35: a joint P and S global tomographic model with uncertainty quantification
We present our third and final generation joint P and S global adjoint tomography (GLAD) model, GLAD-M35, and quantify its uncertainty based on a low-rank approximation of the inverse Hessian. Starting from our second-generation model, GLAD-M25, we added 680 new earthquakes to the database for a total of 2160 events. New P-wave categories are included to compensate for the imbalance between P- and S-wave measurements, and we enhanced the window selection algorithm to include more major-arc phases, providing better constraints on the structure of the deep mantle and more than doubling the number of measurement windows to 40 million. Two stages of a Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton inversion were performed, each comprising five iterations. With this BFGS update history, we determine the model’s standard deviation and resolution length through randomized singular value decomposition.
58 GEOSCIENCES↗
Estimating an airborne dipole source using 3-D wavefield simulations and seismic receivers on the ground
Acoustic signals can couple to the ground, providing an opportunity to use seismic stations to investigate airborne sources. The study of Bishop et al. used wavefield simulations in a fluid-solid medium to quantify the role of topography on the seismic (ground) recordings of a monopole source in the air. We build upon this study by linking wavefield forward modelling with the source estimation code MTUQ, which can accommodate point forces or moment tensors in a solid medium, as well as sources in the air (or water) if they are enabled by the forward-modelling solver. We perform a series of synthetic numerical experiments to demonstrate that a dipole airborne source can be estimated using ground-based receivers, even within the presence of realistic topography. We investigate the influence of receiver coverage, topography, and assumed source location on the estimated results. The established capabilities raise the prospects for future efforts to estimate dipole sources in 3-D models that include heterogeneity in the air and the earth in addition to topography.
58 GEOSCIENCES↗