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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantitative Evaluation of CO 2 Storage Potential in the Offshore Atlantic Lower Cretaceous Strata, Southeastern United States

The geological storage of CO 2 in the Earth’s subsurface has the potential to significantly offset greenhouse gas emissions for safe, economical, and acceptable public use. Due to legal advantages and vast resource capacity, offshore CO 2 storage provides an attractive alternative to onshore options. Although offshore Lower Cretaceous reservoirs have a vast expected storage capacity, there is a limited quantitative assessment of the offshore storage resource in the southeastern United States. This work is part of the Southeast Offshore Storage Resource Assessment (SOSRA) project, which presents a high-quality potential geological repository for CO 2 in the Mid- and South Atlantic Planning Areas. This is the first comprehensive investigation and quantitative assessment of CO 2 storage potential for the Lower Cretaceous section of the outer continental shelf that includes the Southeast Georgia Embayment and most of the Blake Plateau. An interpretation of 200,000 km of legacy industrial 2D seismic reflection profiles and geophysical well logs (i.e., TRANSCO 1005-1-1, COST GE-1, and EXXON 564-1) were utilized to create structure and thickness maps for the potential reservoirs and seals. We identified and assessed three target reservoirs isolated by seals based on their effective porosity values. The CO 2 storage capacity of these reservoirs was theoretically calculated using the DOE-NETL equation for saline formations. The prospective storage resources are estimated between 450 and 4700 Mt of CO 2 , with an offshore geological efficiency factor of dolomite between 2% and 3.6% at the formation scale.

03 NATURAL GAS↗
NASA NTRS1984

Mechanical regulation of plant growth and development

Soybean and eggplant grown and shaken in a greenhouse exhibited decreased internode length, internode diameter, leaf area, and fresh and dry weight of roots and shoots in much the same way as outdoor-exposed plants. Perhaps more important than decreased dimensions of plant parts resulting from periodic seismic treatment is the inhibition of photosynthetic productivity that accompanies this stress. Soybeam plants briefly shaken or rubbed twice daily experienced a decrease in relative as well as absolute growth rate compared to that of undisturbed controls. Growth dynamics analysis revealed that virtually all of the decline in relative growth rate (RGR) was due to a decline in net assimilation rate (NAR), but not in leaf area ratio (LAR). Lower NAR suggests that the stress-induced decrease in dry weight gain is due to a decline in photosynthetic efficiency. Possible effects on stomatal aperture was investigated by measuring rates of whole plant transpiration as a function of seismo-stress, and a transitory decrease followed by a gradual, partial recovery was detected.

Mitchell, C. A.↗
DOE OSTI2022

A Non-perturbative Approach to Computing Seismic Normal Modes in Rotating Planets

In this work, a continuous Galerkin method based approach is presented to compute the seismic normal modes of rotating planets. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core using a polynomial filtering eigensolver. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method. Our discretization utilizes fully unstructured tetrahedral meshes for both solid and fluid regions. The relevant eigenvalue problem is solved by a combination of several highly parallel and computationally efficient methods. We validate our three-dimensional results in the non-rotating case using analytical results for constant elastic balls, as well as numerical results for an isotropic Earth model from standard “radial” algorithms. We also validate the computations in the rotating case, but only in the slowly-rotating regime where perturbation theory applies, because no other independent algorithms are available in the general case. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Earth and Mars models studying the effects of heterogeneity on a large range of scales.

58 GEOSCIENCES↗
DOE OSTI2020

Southwest Wind Power R&D Test Site Development at the National Wind Technology Center (CRADA CRD-12-00472 Final Report)

Southwest Windpower, Inc. (SWWP) has been designing and distributing small wind turbines for more than 22 years and is the recognized global leader in the design, manufacturing and distribution of small wind systems (400-3000 watts). The company has been a pioneer in the development of wind technology and has built and shipped more than 170,000 wind turbines to over 120 countries worldwide. Headquartered in Flagstaff AZ, SWWP has sales representatives in over 88 countries. Applications for SWWP systems include residential homes, commercial properties, micro grids, remote cabins, telecom transmitters, offshore platforms, water pumping and sailboats. In addition, U.S. Department of Defense uses SWWP’s products in Forward Operating Bases (FOB’s), USGS uses them for remote monitoring of glacier movements and the CIA uses them to provide power seismic monitoring. Today, SWWP plans development of new technologies to address distributed energy market needs and become even more aggressive in international markets. This strategy includes extensive Research, Development, Demonstration & Deployment (RDD&D) activities to improve existing production line into more simple, reliable, and cost-effective wind turbine systems. Important part of such activities is the field testing of the new systems, which should provide: (i) validation of the reliable operation of hardware and software; (ii) validation of numerical models of the system and its components; (iii) field comparison of various control and optimization strategies; (iv) customized reliability testing; (v) customized monitoring and analysis testing; and (vi) IEC certification testing (vii) filed demonstration of system performance and reliability. Such field testing facility should encompass multiple towers and appropriate data acquisition systems. SWWP is exploring to move part of the engineering department to Colorado, and execute its RDD&D strategies in this new location. Partnership with NREL will provide great insights into SWWP’s RDD&D processes and more efficient commercialization by utilizing NWTC as the field testing site, and working close with NREL personnel. NWTC is ideally suited to strain the design. NREL is very experienced with customized and IEC testing. During this project, Southwest Windpower aims to improve the value proposition of distributed renewable energy, and make it a competitive choice in the energy markets. As of today, more then 170,000 small wind turbines, manufactured in the U.S., are installed in more then 180 countries. With the improved value proposition, Southwest Windpower wants to create and address new market needs, substantially increase its sales domestically and internationally, which should have positive impact on the creation of American jobs, competitiveness of U.S. economy, export or U.S. products, and the use of renewable energy.

17 WIND ENERGY↗
DOE OSTI2021

Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, here we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

58 GEOSCIENCES↗
DOE OSTI2020

Automatic microseismic event picking via unsupervised machine learning

SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

Chen, Yangkang↗
DOE OSTI2026

Regional Earthquake Ground Motion Simulations for Southern California With EQSIM: Insights From the 2008 Chino Hills, 2024 Highland Park, and 2021 Carson Earthquakes

This study presents physics-based, 3D simulations using the EQSIM framework for several earthquakes in the Los Angeles region. The primary objective was to assess the ability of deterministic physics-based ground motion simulations to reproduce the observed motions from historical events. The selected events included the mathematical equation M w 5.4 2008 Chino Hills, the mathematical equation M w 4.4 2024 Highland Park, and the mathematical equation M w 4.3 2021 Carson events. The simulated motions were evaluated by comparing the recorded and simulated seismograms, as well as the Fourier amplitude spectra, across multiple seismic stations. The SCEC 3D velocity model, CVM-S4.26.M01, was used to represent the regional geology, and ground motion simulations were carried out with a resolution of up to 5 Hz. The results indicate that the simulated motions captured the recorded motions up to approximately 4 Hz. While careful iterations regarding source parameters and corner frequencies were required, and, for the case of the Highland Park event, some of the near-source stations had relatively low accuracy, the present study established a positive step toward the utilization of physics-based simulations in practical applications. The computational efficiencies exhibited by EQSIM, especially on GPU clusters, further supported this assertion, as wall-clock times of simulations involving more than 10 billion grid points were as low as mathematical equation minutes. This permits ensemble simulations for a considered scenario event so that modeling uncertainties (e.g., source and geology) can be bracketed.

EQSIM↗
DOE OSTI2025

FracML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage

Poster on “FRACML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The accurate characterization of subsurface fracture networks is essential for the secure operation of carbon capture, utilization, and storage (CCUS) projects. A thorough understanding of the spatial distribution of subsurface faults and fractures is crucial for predicting CO2 plume evolution and minimizing risks such as potential leakage into overlying formations or induced seismicity. In this context, robust fracture network quantification plays a pivotal role in reservoir management, providing the data necessary to fine-tune operational parameters, and ensure the environmental and economic viability of CCUS projects. As part of the U.S. Department of Energy’s SMART (Science-informed Machine Learning for Accelerating Real-time Decisions in Subsurface Applications) initiative, we focused on the development and application of a machine learning-based tool (FRACML) designed to quantify and map fracture networks using real-world (non-synthetic) data from an active CO2 injection site. Our objective is to demonstrate the utility of this tool in improving operational efficiency and safety across CCUS sites.

artifical intelligence / machine learning (AI/ML)↗
NASA NTRS1990

Improving stability and strength characteristics of framed structures with nonlinear behavior

In this paper an optimal design procedure is introduced to improve the overall performance of nonlinear framed structures. The design methodology presented here is a multiple-objective optimization procedure whose objective functions involve the buckling eigenvalues and eigenvectors of the structure. A constant volume with bounds on the design variables is used in conjunction with an optimality criterion approach. The method provides a general tool for solving complex design problems and generally leads to structures with better limit strength and stability. Many algorithms have been developed to improve the limit strength of structures. In most applications geometrically linear analysis is employed with the consequence that overall strength of the design is overestimated. Directly optimizing the limit load of the structure would require a full nonlinear analysis at each iteration which would be prohibitively expensive. The objective of this paper is to develop an algorithm that can improve the limit-load of geometrically nonlinear framed structures while avoiding the nonlinear analysis. One of the novelties of the new design methodology is its ability to efficiently model and design structures under multiple loading conditions. These loading conditions can be different factored loads or any kind of loads that can be applied to the structure simultaneously or independently. Attention is focused on optimal design of space framed structures. Three-dimensional design problems are more complicated to carry out, but they yield insight into real behavior of the structure and can help avoiding some of the problems that might appear in planar design procedure such as the need for out-of-plane buckling constraint. Although researchers in the field of structural engineering generally agree that optimum design of three-dimension building frames especially in the seismic regions would be beneficial, methods have been slow to emerge. Most of the research in this area has dealt with the optimization of truss and plane frame structures.

Pezeshk, Shahram↗
DOE OSTI2020

Coupled Time-Lapse Full-Waveform Inversion for Subsurface Flow Problems Using Intrusive Automatic Differentiation

We describe a novel framework for estimating subsurface properties, such as rock permeability and porosity, from time-lapse observed seismic data by coupling full-waveform inversion (FWI), subsurface flow processes, and rock physics models. For the inverse modeling, we handle the back propagation of gradients by an intrusive automatic differentiation strategy that offers three levels of user control: (1) At the wave physics level, we adopted the discrete adjoint method in order to use our existing high-performance FWI code; (2) at the rock physics level, we used built-in automatic differentiation operators from the TensorFlow backend; (3) at the flow physics level, we implemented customized partial differential equation (PDE) operators for the multiphase flow equations. The three-level coupled inversion strategy strikes a good balance between computational efficiency and programming efforts, and when the gradients are chained together, it constitutes a coupled inverse system. Our numerical experiments demonstrate that the three-level coupled inverse problem is superior in terms of accuracy to a traditional decoupled inversion strategy. Additionally, our method is able to simultaneously invert for parameters in empirical relationships such as the rock physics models. Our proposed inverted model can be used for reservoir performance prediction and reservoir management/optimization purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Efficient Generalized Boundary Detection Using a Sliding Information Distance

In this work, we present a general machine learning algorithm for boundary detection within general signals based on an efficient, accurate, and robust approximation of the universal normalized information distance. Our approach uses an adaptive sliding information distance (SLID) combined with a wavelet-based approach for peak identification to locate the boundaries. Special emphasis is placed on developing an adaptive formulation of SLID to handle general signals with multiple unknown and/or drifting section lengths. Although specialized algorithms may outperform SLID when domain knowledge is available, these algorithms are limited to specific applications and do not generalize. SLID excels in these cases. We demonstrate the versatility and efficacy of SLID on a variety of signal types, including synthetically generated sequences of tokens, binary executables for reverse engineering applications, and time series of seismic events.

42 ENGINEERING↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

SUBTASK 1.6 – BASIN ELECTRIC CARBON STORAGE RESEARCH PROJECT: NOVEL MONITORING TECHNIQUES

The Energy & Environmental Research Center (EERC) conducted baseline activities associated with an applied research project at Basin Electric Power Cooperative’s (Basin’s) carbon capture and storage (CCS) site in Beulah, North Dakota, to establish novel carbon storage-monitoring techniques as commercial methods under Cooperative Agreement No. DE-FE0024233, Subtask 1.6. The following report summarizes the baseline activities performed and briefly describes the subsequent (operational monitoring) activities that have been proposed to the U.S. Department of Energy (DOE) as part of the overall project to develop and demonstrate novel monitoring techniques at North America’s largest permitted CCS operation. Dakota Gasification Company (DGC), a wholly owned subsidiary of Basin, owns and operates the Great Plains Synfuels Plant (GPSP) approximately 5 miles northwest of the town of Beulah, North Dakota (Figure 1). In 2023, DGC received approval from the North Dakota Industrial Commission (NDIC) to develop a storage facility on-site for injecting a stream of carbon dioxide (CO2) captured from GPSP. DGC will transport the captured CO2 stream with approximately 6.8 miles of transmission lines that extend north of GPSP and inject >1 million tonnes (MMt) of CO2 annually (>1 MMt/yr) over a 12-year period with up to six underground injection control (UIC) Class VI-compliant injection wells completed in the Broom Creek Formation, a predominantly sandstone reservoir and saline aquifer underlying GPSP. The Broom Creek Formation lies approximately 5900 feet (ft) below ground surface (bgs) at GPSP. The commercial scale (i.e., >1 MMt/yr) of DGC’s permitted carbon storage project is ideal for developing and testing the novel monitoring techniques included within Subtask 1.6. The goals of this project are to demonstrate 1) the cost-effectiveness of novel monitoring technologies included as part of this research, 2) technology capability for tracking the CO2 plume and/or associated pressure response in the subsurface and monitoring out-of-zone migration, and 3) compliance with UIC Class VI program requirements. The research activities proposed for the overall project include 1) design of an automated, integrated, modular (AIM) monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance studies; 4) time-lapse monitoring with seismic methods; 5) advanced wellbore-monitoring methods; 6) deployment of an AIM monitoring network; 7) EM monitoring of CO2 with real-time data processing; 8) continued seasonal drone-based surveillance studies; 9) seismic monitoring with passive and active surveys; and 10) wellbore monitoring with nuclear magnetic resonance (NMR) for near-surface characterization. Completion of Activities 1.0–5.0 (baseline activities) are described in this report. Upon authorization of funding by DOE, the EERC will initiate Activities 6.0– 10.0 (operational monitoring activities). Current state-of-the-art (SOA) carbon storage-monitoring techniques require countless labor hours dedicated to the acquisition of data. Once data are gathered, these SOA techniques often rely on commercial facilities to process raw data from the field. However, it is anticipated that next-generation monitoring techniques, such as those being demonstrated, will lower acquisition footprints, be less operationally intensive, and improve data acquisition efficiencies. These new techniques are more conducive to the application of machine learning, artificial intelligence, and automation, thus providing a pathway for integration into active control systems, informing site operability, and improving the integration of data for future CCS projects across the United States. Additionally, reclaimed and active mining lands are present within the project site, creating a unique opportunity to demonstrate the effectiveness of remote sensing and surface-based geophysics monitoring techniques at similar project sites that may include disturbed, unconsolidated, or actively excavated near-surface environments. The efforts included in the overall project will produce necessary designs, learnings, and data acquired during the baseline and operational monitoring periods that are necessary for time-lapse demonstration and validation of the described monitoring techniques. In addition, it is anticipated that the monitoring technologies included in this study will be compliant with UIC Class VI requirements to enable the potential for implementation at other CCS sites across the United States.

42 ENGINEERING↗
DOE OSTI2024

2-D seismic wave propagation using the distributional finite-difference method: further developments and potential for global seismology

SUMMARY We present a time-domain distributional finite-difference scheme based on the Lebedev staggered grid for the numerical simulation of wave propagation in acoustic and elastic media. The central aspect of the proposed method is the representation of the stresses and displacements with different sets of B-splines functions organized according to the staggered grid. The distributional finite-difference approach allows domain-decomposition, heterogeneity of the medium, curvilinear mesh, anisotropy, non-conformal interfaces, discontinuous grid and fluid–solid interfaces. Numerical examples show that the proposed scheme is suitable to model wave propagation through the Earth, where sharp interfaces separate large, relatively homogeneous layers. A few domains or elements are sufficient to represent the Earth’s internal structure without relying on advanced meshing techniques. We compare seismograms obtained with the proposed scheme and the spectral element method, and we show that our approach offers superior accuracy, reduced memory usage, and comparable efficiency.

Geochemistry & Geophysics↗
DOE OSTI2020

New Architecture to Support Integration and Processing of Seismic Data from Heterogeneous Sources

The Geophysical Monitoring Program (GMP) at Lawrence Livermore National Lab (LLNL) maintains a database and supporting infrastructure for geophysical data used in support of the Nuclear Detonation Detection mission. This database includes data from multiple sources, many of which do not distribute data to the public or for which there is no automated means of access. For example, Figure 1 shows (left) the distribution of waveform data in our database by source. The Incorporated Research Institutions for Seismology Data Management Center (IRISDMC) is our major source of waveform data and those data may be retrieved at will using the Federated Digital Seismograph Networks FDSN web Application Programming Interface (API). However, the next 6 most important sources of waveform data have no or only limited automated access to waveforms. As Figure 1 (right) shows, it is very common for waveform records associate with an event in our database to come from two or more sources, and in some cases data come from 10 sources. This diversity of data sources drives our need for efficient and correct integration of metadata, parametric data, and waveform data.

58 GEOSCIENCES↗
DOE OSTI2023

An autoencoder compression approach for accelerating large-scale inverse problems

Abstract Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the PDE solution introduce an enormous number of parameters and require large-scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time-dependent PDEs, the adjoint method often employed to compute gradients and higher order derivatives efficiently requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high-dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior showing how the DIAS method can be affordably extended to large-scale problems without the need for checkpointing and large memory.

Mathematics↗
DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗