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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Prediction of gas hydrate saturation using machine learning and optimal set of well-logs

We report resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well-specific calibration of empirical exponents in the electrical resistivity method, (ii) assumption of known pore morphology for gas hydrates in the acoustic velocity method, and (iii) presence of unknown mineralogy and bulk modulus terms in the acoustic velocity method. NMR-density porosity-derived gas hydrate saturation based on the analysis of the transverse magnetization relaxation time (T2) is considered the most precise method, but acquisition of NMR-based logs is limited at relatively recent drilled sites; additionally, its use in conventional oil and gas reservoirs is not that common due to higher cost and operational deployment limitations associated with acquiring NMR well-logs. This study proposes a new method that predicts gas hydrate saturation (S h ) for any well using porosity, bulk density, and compressional wave (P wave) velocity well-logs with neural network (or stochastic gradient descent regression) without any well-specific calibration and/or other aforementioned shortcomings of the existing methods. The method is developed by examining the underlying dependency between S h and different combinations of well-logs, chosen from 6 routine logs, with 12 different machine learning (ML) algorithms. The accuracy of the proposed method in predicting S h is ~ 84%, which is better than the accuracy of seismic and electrical resistivity methods (≤ 75%) per the results reported by three different studies. The robustness of the method in the specific case of permafrost-associated gas hydrates is demonstrated with well-log data from two wells drilled on the Alaska North Slope.

58 GEOSCIENCES↗
DOE OSTI2024

Stanford Thermal Earth Model for the Conterminous United States

Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below. A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively..

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO 2 ) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO 2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO 2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat threedimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

25 ENERGY STORAGE↗
DOE OSTI2025

Probabilistic Seismic Hazard Analysis for Iraq Based on the Updated Earthquake Catalog (1900-2021) and Ground Motion Characteristics

Onur et al. (2017) compiled the first comprehensive earthquake catalog for Iraq, covering 1900 to 2009 within 26°–40°N latitude and 36°–51°E longitude. This catalog was utilized in a probabilistic seismic hazard assessment (PSHA) by Abdulnaby et al. (2020) to aid in updating Iraq’s building code seismic provisions. Recently, we have updated the earthquake catalog for Iraq by adding earthquakes recorded from 2010 to 2021 and directly calculating moment magnitude (Mw) for about 2,800 earthquakes using the coda envelope methodology and waveform data from the Mesopotamian Seismological Network (MPSN) in Iraq.

58 GEOSCIENCES↗
DOE OSTI2022

Geological activity shapes the microbiome in deep-subsurface aquifers by advection

Subsurface environments host diverse microorganisms in fluid-filled fractures; however, little is known about how geological and hydrological processes shape the subterranean biosphere. Here, we sampled three flowing boreholes weekly for 10 mo in a 1478-m-deep fractured rock aquifer to study the role of fracture activity (defined as seismically or aseismically induced fracture aperture change) and advection on fluid-associated microbial community composition. We found that despite a largely stable deep-subsurface fluid microbiome, drastic community-level shifts occurred after events signifying physical changes in the permeable fracture network. The community-level shifts include the emergence of microbial families from undetected to over 50% relative abundance, as well as the replacement of the community in one borehole by the earlier community from a different borehole. Null-model analysis indicates that the observed spatial and temporal community turnover was primarily driven by stochastic processes (as opposed to deterministic processes). We, therefore, conclude that the observed community-level shifts resulted from the physical transport of distinct microbial communities from other fracture(s) that outpaced environmental selection. Given that geological activity is a major cause of fracture activity and that geological activity is ubiquitous across space and time on Earth, our findings suggest that advection induced by geological activity is a general mechanism shaping the microbial biogeography and diversity in deep-subsurface habitats across the globe.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Infrasound Detections of Low-Magnitude Earthquakes: Preliminary Results of the West Texas Acoustic Experiment

Infrasound observations have grown increasingly important for the monitoring of earthquakes. While large earthquakes generate infrasound that can be detected thousands of kilometers away, there are few near-field observations of infrasound generated by low-magnitude events. We describe preliminary results of the West Texas Acoustic Experiment, during which infrasound sensors collected continuous data in the Permian Basin for a six-month period spanning January—June 2023. During this time, more than 1000 earthquakes with magnitudes between 1.2 and 4.2 occurred within 50 km of the network. We used spectral analysis, array processing, and manual inspection of waveforms to evaluate arrivals of infrasound signals following 84 events with magnitudes between 2.5 and 4.2. Here, we describe eight such events and the infrasound signals associated with each. We find detections of seismic-to-acoustic infrasound signals associated with seven events. We also find strong evidence of a laterally-propagating, purely acoustic wave generated by an M2.9 earthquake.

58 GEOSCIENCES↗
DOE OSTI2026

Phase Picking Beyond Local Distances: Where Waveform Filtering Still Matters for Deep Learning Models

Waveform filtering is a standard step in traditional seismic phase picking but often receives little attention in deep learning workflows, where models are typically trained on raw or minimally processed waveforms. Although this strategy performs well for local events, we show that performance can degrade substantially at regional distances. To address this limitation, we introduce two ways to incorporate multiband-filtered waveforms into deep learning phase pickers. The stacking approach concatenates filtered inputs along the channel dimension, while the branching approach processes each frequency band through a dedicated network branch before feature fusion. Both approaches can substantially improve performance across epicentral distances of 0° to 20°, but their effectiveness depends strongly on the selected frequency bands. Tests with multiple filter banks show that filter-bank design should be treated as part of model optimization rather than as a fixed preprocessing choice. Grad-CAM analysis of the branching model indicates that band importance varies among waveform samples and across training realizations, with only a weak overall preference for the 0.25 to 0.5 Hz band. These results show that no single filter band is consistently optimal and demonstrate that explicit feature engineering remains valuable for robust deep learning-based seismic phase picking.

58 GEOSCIENCES↗
DOE OSTI2023

Transforming microseismic clouds into near real-time visualization of the growing hydraulic fracture

SUMMARY Microseismic observations during unconventional reservoir stimulation are typically seen as a proxy for clusters of hydraulic fractures and the extent of the stimulated reservoir. Such straightforward interpretation is often misleading and fails to provide a physically reasonable image of the fracturing process. This paper demonstrates the application of a physics-based machine learning algorithm which enables a rapid and accurate fracture mapping from the microseismic data. Our training and validation data set relies on a history-matched geomechanical modelling workflow implemented in GEOS software for the Hydraulic Fracturing Test Site 1 (HFTS-1) project. For this study we augmented the simulated fracture growth through geostatistical modelling of induced seismicity, so that the synthetic microseismic catalogue matches the main statistical properties of the field observations. We formulated the problem of mapping the actual fracture in the clutter of events to parallel common video segmentation workflows: several past video frames (microseismic density snapshots) are passed through a deep convolutional network to classify whether a given voxel is associated with a fracture or intact rock. We found that for accurate fracture mapping, the network’s input and architecture must be augmented to incorporate the fluid injection parameters (pressure, rate, concentration of proppant, and location of the perforation within the cluster). The error rate for the network reached as little as 10 per cent of the fracture area, while a conventional microseismic interpretation approach yielded ∼300 per cent. Our approach also yields must faster predictions than conventional methods (minutes instead of weeks), and could enable engineers to make rapid decisions regarding engineering parameters (pumping rate, viscosity) in real time during stimulation.

58 GEOSCIENCES↗
DOE OSTI2022

Automatic Waveform Quality Control for Surface Waves Using Machine Learning

Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, K-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.

58 GEOSCIENCES↗
DOE OSTI2025

Characterization of the degradation of gamma-irradiated elastomers using Raman spectroscopy

This report presents key findings from Raman spectroscopic analysis of gamma-irradiated rubber samples extracted from a laminated lead-damped rubber (LDR) seismic isolation device. The samples were exposed to gamma radiation from a 60Co source in a Foss Therapy Services gamma irradiator, reaching absorbed doses up to 1600 kGy. A distinct threshold near 400 kGy was identified, beyond which significant spectral changes were observed. Two Raman peaks - at approximately 425 cm-1 and 2440 cm-1 - were tracked as a function of dose using Gaussian fitting. The 425 cm-1 peak, attributed to sulfur–sulfur (S–S) bond stretching (resulting from vulcanization of the rubber), exhibited a dose-dependent upshift, indicating radiation-induced crosslinking within the sulfur-based polymer network. Conversely, the 2440 cm-1 peak, likely associated with vibrational modes of additives or impurities, showed a downward shift with increasing dose, suggesting chain scission and degradation of non-rubber constituents. These results provide first-of-a-kind insights into the microstructural evolution of elastomers under high-dose gamma irradiation and establish a preliminary dose threshold for significant degradation. Future work will incorporate multi-modal characterization—including Fourier Transform Infrared (FTIR) spectroscopy, scanning electrom microscopy (SEM) of the rubber surface morphology, thermogravimetric analysis (TGA) to determine changes in thermal stability, and mechanical testing—to correlate molecular-level changes with macroscopic performance of these elastomers as damping media in seismic isolation devices. These findings are expected to provide regulatory guidance and design criteria for qualifying low-damping rubber seismic isolators in advanced nuclear reactor applications.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

The Value of Information From Horizontal Distributed Acoustic Sensing Compared to Multicomponent Geophones Via Machine Learning

Abstract Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. The value of information (VOI) metric is used to objectively quantify and compare the value of two types of seismic receiver data via a machine learning approach. The demonstrated VOI methodology is novel by including spatial models from seismic data and obtaining the information statistics from machine learning. Our two-dimensional numerical experiments compare images created from sparsely spaced (80 m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. We used a three-fold cross validation of a U-Net convolutional neural networks to achieve average classification statistics. The results suggest that when horizontal sources are utilized, geophones and DAS identify reflectors and non-reflectors at roughly the same rate. The average F1 score for horizontal DAS is 0.939 and 0.931 for geophones. For images created from a vertical source, DAS performed marginally better (F1 = 0.919) than geophones (F1 = 0.877). Our transferrable methodology can provide guidance on which acquisition scenarios can improve images of important structures in the subsurface and present an efficient method for obtaining reliability statistics from high-dimensional, spatial data.

Energy & Fuels↗
DOE OSTI2026

How earthquakes organize stress

Stress is not uniform in the Earth. Therefore, we must use natural experiments to measure the distribution of stresses and related quantities, rather than single values. For instance, dynamic triggering shows that faults are uniformly distributed over their loading cycles in Southern California. The probability that a fault ruptures across a barrier measures the in situ energy distribution. Fault roughness reflects the distribution of strength. These natural experiments produce observable distributions that are surprisingly consistent and suggest some degree of self-organization in the Earth’s crust. Once established, the functional form of the distributions can be used to track changes in response to earthquakes as well as to distinguish fundamentally different fault systems. Transient fault locking before stress release in laboratory experiments can be interpreted as a consequence of self-organization of fault stress. The robust self-organization of multiple variables in earthquake systems suggests that the most consequential mechanical outcome of earthquakes may be the redistribution of stress and the strain energy associated with it. The low friction on a fault during seismic slip as inferred by temperature measurements of the Tohoku earthquake is consistent with dissipation playing a secondary role to this redistribution process. Through stress redistribution and interaction, subduction zone faults tend to synchronize, perhaps due to their geometric simplicity, while the continental system of Southern California cannot synchronize, perhaps due to the complexity of the fault network. Earthquakes organize stress in the crust and produce a suite of well-defined, consistent distributions.

earthquakes↗
DOE OSTI2024

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency‐based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep‐learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short‐time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. In this report we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN‐based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground‐truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real‐world data shows that both the CNN and MWCNN models outperform standard band‐pass filtering (BPF; average improvement in signal‐to‐noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

58 GEOSCIENCES↗
DOE OSTI2022

Erratum to: Data Fusion to Support Integrated Nuclear Detonation Detection [Slides]

The original document (LA-UR-22-29547) contained minor equation errors that approximate correct equations that couple the multi-sensor, serial system detector thresholds for a seismic Rayleigh wave detector and an acoustic energy detector. Those errors appeared on slides 62-67. This erratum associates the following slides with the erroneous slides. The numbers of the erroneous slides are marked at the upper right in small text in orange. A result of those errors over-predict the performance of the two-sensor serial network, that is, the former document provides an optimistic estimate of system performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Single Channel Infrasound Detection Using Machine Learning

Infrasound, low frequency sound less than 20 Hz, is generated by both natural and anthropogenic sources. Infrasound sensors measure pressure fluctuations only in the vertical plane and are single channel. However, the most robust infrasound signal detection methods rely on stations with multiple sensors (arrays), despite the fact that these are sparse. Automated methods developed for seismic data, such as short-term average to long-term average ratio (STA/LTA), often have a high false alarm rate when applied to infrasound data. Leveraging single channel infrasound stations has the potential to decrease signal detection limits, though this cannot be done without a reliable detection method. Therefore, this report presents initial results using (1) a convolutional neural network (CNN) to detect infrasound signals and (2) unsupervised learning to gain insight into source type.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

InversionNet3D: Efficient and Scalable Learning for 3-D Full-Waveform Inversion

Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has shown some encouraging results in obtaining 2-D velocity maps. However, due to high computational complexity and large memory consumption, the reconstruction of 3-D high-resolution velocity maps via deep networks is still a great challenge. Here, in this article, we present InversionNet3D (InvNet3D), an efficient and scalable encoder–decoder network for 3-D FWI. The proposed method employs group convolution in the encoder to establish an effective hierarchy for learning information from multiple sources while cutting down unnecessary parameters and operations at the same time. The introduction of invertible layers further reduces the memory consumption of intermediate features during training and, thus, enables the development of deeper networks with more layers and higher capacity as required by different application scenarios. Experiments on the 3-D Kimberlina dataset demonstrate that InvNet3D achieves state-of-the-art reconstruction performance with lower computational cost and lower memory footprint compared to the baseline.

58 GEOSCIENCES↗
DOE OSTI2020

Near-Field Imaging of Shallow Chemical Explosions in Granite Using Change Detection Methods with Surface and Borehole Seismic Data

Explosions detonated in geologic media damage it in various ways via processes that include vaporization, fracturing, crushing of interstitial pores, etc. Seismic waves interact with the altered media in ways that could be important to the discrimination, characterization, and location of the explosions. As part of the Source Physics Experiment, we acquired multiple pre- and post-explosion near-field seismic datasets and analyzed changes to seismic P-wave velocity. Our results indicate that the first explosion detonated in an intact media can cause fracturing and, consequently, a decrease in P-wave velocity. After the first explosion, subsequent detonations in the pre-damaged media have limited discernible effects. We hypothesize this is due to the stress-relief provided by a now pre-existing network of fractures into which gasses produced by the explosion migrate. We also see an overall increase in velocity of the damaged region over time, either due to a slow healing process or closing of the fractures by subsequent explosions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

A New Simplified Discrete Fracture Model for Shearing of Intersecting Fractures and Faults

Abstract Shearing of fractures and faults is important because it can result in permeability change or even induce seismicity—both are keys for efficient and safe energy recovery and storage in Earth systems. Quantitative analysis of shearing of intersecting fractures and faults is challenging because it can involve dynamic frictional contacts that are complicated by deformation of the rock matrix. To predict the shearing of intersecting fractures/faults, we attempt to answer the question of how intersections impact the shearing of a fracture network and whether we can simplify the description as compared to classical discrete fracture network (DFN) models. To answer these questions, we conducted a series of numerical simulations on scenarios for variable numbers of intersecting fractures. All these examples yield consistent results: the results of using DFNs are consistent with those of using hypothetical major paths. This leads to a new model, which we name simplified discrete fracture network model , to analyze shearing of intersecting fractures/faults using major path(s). We found that the intersections of fractures do not fundamentally change the shearing of two intersecting fractures if the intersecting angles are small. Furthermore, increasing the number of fractures/faults may relax the stress as more fractures/faults become available for shearing and distributing the stress. The simplified DFN model, which can capture efficiently the shearing behavior of each major paths from a large number of intersecting fractures/faults, will be a promising conceptual model that is complementary to existing equivalent continuum and discrete fracture models to analyze shearing of intersecting fractures/faults.

58 GEOSCIENCES↗