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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗
DOE OSTI2022

On the Feasibility of Geophysical Methods for CO 2 Monitoring in the North Dakota CarbonSAFE Project

Conference presentation at American Institute of Chemical Engineers (AIChE) Annual Meeting, Phoenix, AZ, November 13–18, 2022. An analysis of rock physics crossplots and diagnostic models suggests that the baseline seismic data may be sensitive to changes in lithology and porosity of the Broom Creek Formation and that these changes may be detectable by prestack seismic inversion.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Wellbore integrity assessment with casing-based advanced sensing

Wellbore integrity is of paramount importance to subsurface resource extraction, energy storage 1 and waste disposal. After installation, well casing and cement are subject to mechanical stress due to near-well pressure changes and fluid induced corrosion. This is exacerbated for geothermal wells where produced fluid is at high temperature and corrosive. The current state-of-the-art technologies for wellbore integrity assessments are an array of cased hole logging tools. Wireline deployed acoustic, electromagnetic and mechanical tools are all available to inspect steel casing corrosion and casing-cement bond and these tools can provide high-resolution assessment of borehole conditions. They are intrusive, however, in terms of borehole preparation and interruption to the normal operation of the wells, and not suitable for high temperature or highly deviated well deployments. In addition, these measurements are performed infrequently due to high cost, and are therefore incapable of providing frequent data to better predict borehole degradation trajectory, which can help provide early warning of potential borehole failures. For this project we are developing a suite of novel, non-invasive, casing based tools for wellbore integrity assessment, combining fast/low cost screening with higher-precision investigation. Our approach is based on monitoring the response of the casing when energized at the wellhead, thereby interrogating the casing without well intervention. Lab, field and numerical approaches are used in our study. During the early stage of the research, we focus on numerical simulations, which have shown the sensitivity of the low frequency electromagnetic (EM) signals to changes in borehole depths and have successfully tested the concept at a field site with different length well casings. Initial seismic modeling efforts have also demonstrated our capability to simulate seismic tube wave and seismic field alterations due to borehole breakage and associated fluid leakage. Further numerical, laboratory and field experiments are underway for additional technology sensitivity analysis, particularly the transient EM/Seismic reflectometry methods, data acquisition optimization, and numerical simulation improvements.

Wilt, Michael↗
DOE OSTI2022

Deep learning multiphysics network for imaging CO 2 saturation and estimating uncertainty in geological carbon storage

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade-off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO 2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO 2 saturation. The network is trained on pairs of CO 2 label models and multiphysics data so that it can directly image CO 2 saturation. Here we use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO 2 saturation images. Using realistic CO 2 label models and multiphysics data derived from the Kimberlina CO 2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single-physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO 2 saturation images. Our results also suggest that the deep learning multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring.

58 GEOSCIENCES↗
DOE OSTI2026

FY 2026 Midyear Report: Seismic Monitoring of Underground Vibration Sources Using Distributed Acoustic Sensing and Seismometers

Safeguards-relevant temporal changes in underground facilities can be observed using geophysical monitoring techniques. Seismic waves, in particular, provide valuable insights into subsurface activities and can serve as an important tool for detecting anomalous events that may indicate containment breaches at geological repositories. This midyear report summarizes ongoing efforts to automatically and rapidly detect and locate anomalous vibration signals that could be indicative of potential containment breaches. Previous work during FY25 focused on compiling continuous seismic datasets from two underground sites and developing a database of continuous waveforms and ground-truth event data derived from multiple sensing modalities. Building on this foundation, we are adapting anomaly detection and geolocation algorithms to explore methods for monitoring underground activities using two relatively low-maintenance sensing technologies: a dense surface geophone array deployed at the Pleasant Gap mine in Pennsylvania, and a three-dimensional fiber-optic cable array for distributed acoustic sensing (DAS) installed in the subsurface at the Sanford Underground Research Facility (SURF) in South Dakota. This report summarizes work conducted during the first two quarters of FY26, during which we refined a dynamic power spectral density (PSD)-based detector, applied it independently to each geophone station, and then combined the per‑station detections with density-based spatial clustering of applications with noise (DBSCAN) to cluster events and produce spatial maps over a nine‑day interval. In addition, we outline plans for a field trial at the Waste Isolation Pilot Plant (WIPP) in New Mexico to compare traditional seismic monitoring approaches with DAS techniques and to evaluate the benefits of combined data analysis. Activities during the past two quarters have included the preparation and submission of a Field Test Plan to WIPP for approval, as well as submission to headquarters for review and feedback.

58 GEOSCIENCES↗
DOE OSTI2021

IC reports (2021)

We requested HPC support to continue research on the seismic waves generated by impacts. We used the following codes: (1) the Hybrid Optimization Software Suite (HOSS), developed at LANL. HOSS is based on a combined Finite and Discrete Element Method (FDEM). New material models are developed for the sedimentary rocks. HOSS has been recently benchmarked to iSale and FLAG codes. SPECFEM3D is an open-source code developed since the last 90s. It won the Gordon Bell award for best performance in 2003, was finalist again in 2008 for a run at 0.16 petaflops on 149,784 cores on the ‘Jaguar’ Cray system at Oak Ridge National Laboratory. It also won the BULL Joseph Fourier supercomputing award in 2010.; SW4 is a 4th-order finite difference code developed at LLNL which is currently actively developed to handle complex 3D models and to be ported on future exascale platforms. We assessed our need to a total of 3.9M CPU-hrs for year 1 and and 3.1 M for year 2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The EGS Collab Project – Stimulations at Two Depths

The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. We have completed Experiment 1 (of 3), which examined hydraulic fracturing in a well-characterized underground fractured phyllite test bed at a depth of approximately 1.5 km at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests using ambient temperature and chilled water. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. More detailed numerical simulation was performed to answer key experimental design questions, forecast fracture propagation trajectories and extents, and analyze and evaluate results. Data are freely available from the Geothermal Data Repository. Experiment 2 examines the potential for hydraulic shearing in amphibolite at a depth of about 1.25 km at SURF. This site has a different set of stress and fracture conditions than Experiment 1. The Experiment 2 testbed consists of nine subhorizontal boreholes configured in two fans of two boreholes which surround the testbed and contain grouted-in electrical resistance tomography, seismic sensors, active seismic sources and distributed fiber sensors. A “five-spot” set of test wells that extends from a custom mined alcove includes an injection well and four production/monitoring wells. The testbed was characterized geophysically and hydrologically, and three stimulations have been performed using the Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP) tool to measure strains, and a new strain quantifying tool (downhole robotic strain analysis tool -DORSA) was deployed in a monitoring hole during stimulation. Real-time data were broadcast during stimulations to allow real-time response to arising issues.

EGS Collab, Enhanced Geothermal Systems, EGS, fiel↗
DOE OSTI2025

Event Classifications on DNE2 Main Experiment Data using a Convolutional Neural Network Ensemble

The Dynamic Networks (DN) Experiment for FY24 (DNE2) is an experiment within DN with the goal of quantitatively evaluating the effectiveness of solutions developed so far by various researchers under the Low Yield Nuclear Monitoring (LYNM) program using a shared set of metrics and datasets. A key component of this experiment is the mimicking of a signature processing pipeline, and comparing currently accepted and standard-use processing methods to more state-of-the-art processes developed under DN. In this work, we focus specifically on the Event Characterization (EC) Focus Area (FA) of the pipeline, where a seismic event’s magnitude, yield and class are identified. We use Deep Learning (DL) to classify the type of events being processed as either earthquakes (EQs) or explosions (EXs) for three iterations of experiment datasets. The model is noticeably more confident and accurate in classifying explosions than earthquakes, reflecting a known shortcoming of the model, that being of a bias towards predicting explosions over earthquakes in the west coast due to training data biases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Continuous surface-to-distributed acoustic sensor snapshots explain reactivation of individual natural fractures during an unconventional reservoir stimulation

ABSTRACT Fiber-optic sensing technologies allow petroleum engineering teams to detect hydraulic fracture interaction with boreholes during unconventional reservoir stimulation. In combination with high-repeatability seismic sources, the same distributed acoustic sensors (DASs) enable vertical seismic profiling (VSP) of the fracture evolution away from the boreholes. We discovered clear signatures of seismic scattering on activated fractures during nine days of continuous seismic monitoring of the fracturing stages at the Austin Chalk/Eagle Ford Field Laboratory. The present study applies a novel approach for quantitative analysis of the scattering events in terms of the evolution of the geometry and elastic stiffness of individual fractures. Our characterization strategy sequentially refines the fracture models: from a stack of 1D soft layers to 3D rectangular inclusions. First, we estimate the number of fracture locations and reflectivity using a modified sparse-spike deconvolution of the stacked VSP traces. The fracture set consists of five fractures spaced by 15–30 m with a reflectivity of approximately 1%. Then, we develop a scattering integral method to refine these estimates along with an inversion of the fracture top and bottom for each monitoring vintage. We find that, initially, some of the fractures are located above the monitoring fiber with the height of approximately 100 m. Then we integrate the seismic interpretation with the low-frequency DAS and pressure and microseismic monitoring to reconstruct the activation process of the fractures. Most likely, some of the natural fractures slowly grew downward to the monitoring fiber as a result of fluid injections in the stimulated well. This led to bright strain anomalies but did not trigger seismicity. The top of the fractures remained almost constant and were limited by a lithologic boundary/stress barrier. To our knowledge, this is the first time VSP data enabled tracking of the fracture evolution with such high spatial and temporal resolution, which was previously only available for crosswell surveys and at a much smaller scale.

Glubokovskikh, Stanislav↗
DOE OSTI2026

Big Data Meets Geothermal Exploration (CRADA Final Report)

As part of the Cyclotron Road program, Zanskar Geothermal & Minerals, Inc. investigated the application of micro-earthquake and ambient noise seismology methods to imaging and characterizing the structural characteristics and hydrothermal flux of subsurface faults. Significant advances in what could be resolved were enabled by two major developments in seismology: 1) the availability of large-n arrays of low-cost seismometers, and 2) the availability of increased computational power and semi-automated data reduction algorithms. In tandem, these advances may improve the signal-to-noise ratio and spatial precision of the data collected and enable higher-resolution characterization of subsurface fracture systems and their spatio-temporal evolution. These tools supported efforts to reduce dry-hole risk and to improve wellfield productivity for geothermal resource development. In particular, two applications of these advances were evaluated: 1) fracture-seismic imaging, which was used to detect ambient emissions from fluid-filled fractures, and 2) reservoir tomography, which used information about travel paths, source locations, and source parameters of micro-earthquakes to identify areas of enhanced permeability. Integration of these methods provided guidance for siting wells and served as prior constraints for reservoir models, informing forecasts of power potential and production and injection strategies aimed at minimizing temperature decline and improving overall resource productivity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Exploring Uncertainty in Moment Estimation for Small Earthquakes in Southern Nevada Using the Coda Envelope Method

Compiling source parameter estimates for small earthquakes is important both for our understanding of earthquake physics and for accurately assessing earthquake hazard. Reliable source parameter estimates are difficult to achieve for small earthquakes, in part due to our inability to accurately model the relevant physical processes at high frequencies. The coda envelope methodology developed by Mayeda and Walter (1996) and Mayeda et al. (2003) can mitigate this concern and estimate the moment of small earthquakes by determining the parameters that control the shape of the S-wave coda envelope while eliminating path effects by minimizing the scatter between seismic stations. Here, we use an open-source implementation of this technique called the Coda Calibration Tool (CCT; Barno, 2017) to calculate CCT-based moment magnitude estimates of small earthquakes (M L 0–3) in the Rock Valley, Nevada, region within the Nevada National Security Site. The Rock Valley data set is of particular interest because it allows us to explore the changes in uncertainties of the coda calibration method with earthquake size and depth. We found that a consistent linear relationship exists between the local magnitude M L and our coda-derived M w estimates for earthquakes as small as M L 0–3, but that current CCT workflows do not accurately characterize very shallow events. We also demonstrate that the epistemic uncertainty in the apparent stress value assumed by the CCT algorithm can influence magnitude estimates of small earthquakes. In conclusion, these results provide valuable insight into the seismicity of this region, and inform future analysis and modeling efforts for nuclear monitoring and seismic hazard.

58 GEOSCIENCES↗
DOE OSTI2023

A case study of denoising microseismic data in the Farnsworth, TX CO 2 EOR site

As geologic carbon sequestration projects begin to be funded with a higher degree of frequency, microseismic monitoring will become more necessary to establish caprock integrity and induced seismicity risk. The Farnsworth, TX site, which hosts enhanced oil recovery operations, provides an ideal laboratory to test essential components of microseismic monitoring such as surface station placement, denoising, and different autopicker methods. We find that despite careful optimization, the surface station placement at the Farnsworth site was insufficient given the high level of background noise from the industrial operations at the oil field. Thus, we use only borehole geophone data in our processing. Two denoising techniques were examined: the DeepDenoiser and the continuous wavelet transform. The continuous wavelet transform was shown to be a valuable tool in converting between raw waveforms to processed denoised waveforms for microseismic monitoring. In the case of the noisy waveforms, only ten detections are found, compared with 90 in the denoised data for a two-hour window. The DeepDenoiser suffered from the fact that the training data was regional earthquake data. In addition, the PhaseNet machine-learning autopicker was applied to both the noisy and the denoised data, and this algorithm detected thousands of more arrivals in the data denoised with the continuous wavelet transform technique.

58 GEOSCIENCES↗
DOE OSTI2020

Assessment of Survey Results from Advanced Reactor Industry Domain Experts on Nonlinear Soil-Structure Interaction Analysis Software Verification and Validation

The seismic load case has a significant impact on the design and cost of nuclear power plants. Given the need to substantially reduce cost, advanced reactor designers are looking to leverage numerical tools that enable seismic analysis of an integrated vessel/support/structure/soil system. Modern nonlinear analysis tools provide a solution, capturing dynamic coupling between components (including soil-structure interaction (SSI)) concurrent with nonlinear behavior in one or more parts of the system. Although these methods have a rich history of technical development and implementation, software quality assurance (SQA) following nuclear industry standards remains a significant burden for those wishing to adopt such methods for advanced reactor design and licensing. To reduce the SQA burden, this project will develop the guidance for SQA verification and validation (V&V) of coupled nonlinear SSI analysis tools, to include a test matrix of software features and test problems to support commercial grade dedication (CGD). The guidance is intended to be technology-neutral: supporting designers of all advanced reactors. To maximize the value of the project to reactor designers, the project team performed focused surveys of and interviews with advanced reactor designers. Additionally, the project surveyed members of the broader industry involved in reactor design and licensing. The aggregated industry feedback consists of written survey responses, live polling responses, focused interviews, and informal feedback provided after outreach presentations, collectively referred to as “survey results”. Herein, the survey results are reported, reviewed, and assessed to inform follow-on project activities. The survey results confirmed the familiarity of the industry with the coupled nonlinear SSI analysis. They also affirm the project need based upon the expressed intent to (1) incorporate nonlinear features and (2) pursue the coupled nonlinear SSI analysis to reduce seismic demands and construction costs. All reactor designers indicated their intent to incorporate two or more nonlinear features, and all reactor designer respondents opined that the coupled nonlinear SSI analysis would allow for the optimization of the reactor design and construction. However, the programmatic challenges, whether real or perceived, present a significant barrier to reactor designers. The barrier most commonly identified by the reactor designer population was regulatory risk, with 70% citing this as a reason not to pursue the approach. The perceived regulatory risk identified by the reactor designers underscores the importance of regulator engagement and dialogue in this project. Additionally, half of the reactor designers identified cost and lack of guidance as a deterrent. The survey results also provide insights to tailor specific aspects of the guidance document and test matrix. The project plans to prepare both a formal referenceable guidance document and a collaborative, web-based test matrix and problem set. The project will focus on more complete test problem definitions for the prioritized nonlinear features over shallower problem descriptions for a larger set of features. The nonlinearities prioritized as high based upon survey feedback include fluid-structure interaction and seismic isolation and energy dissipation devices. The nonlinearities prioritized as intermediate include nonlinear geomaterials, nonlinear concrete, nonlinear steel, and interface/contact nonlinearity. Deep embedment and the associated nonlinear phenomena are assigned the lowest priority based upon survey feedback.

42 ENGINEERING↗
DOE OSTI2025

Exploring microstructures and anisotropies of serpentinites

Serpentine minerals have received a lot of attention because of their unique crystal structures, their wide occurrence in orogenic belts and their potential role in contributing seismic anisotropy in subducting slabs. Several studies have investigated crystal preferred orientation (CPO) in high temperature antigorite serpentinites from Japan, the Alps, Spain, Cuba and Tibet, documenting significant crystal alignment. However, only a limited number of lower grade serpentines have been explored to date. Mainly because of submicroscopic microstructural heterogeneities CPO cannot be measured with conventional methods such as optical microscopy and EBSD. In this study 15 serpentinites from different tectonic settings in California, the Central Alps and Northern Spain have been investigated, mainly with high energy synchrotron X-ray diffraction, to quantify bulk crystal alignment. We find that CPO is strong on sheared surfaces of fractured blocks and secondary veins but the bulk of most serpentinite samples, except high-grade recrystallized antigorite serpentinite, show only weak crystal alignment. Correspondingly calculated seismic anisotropy based on CPO is not very significant. This is supported by very heterogeneous microstructures as documented with SEM and TEM analyses.

58 GEOSCIENCES↗
DOE OSTI2021

Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments

Rocks under stress deform by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention-based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.

58 GEOSCIENCES↗
DOE OSTI2021

Regulatory Treatment of Low Frequency External Events under a Risk-Informed Performance-Based Licensing Pathway

Significant efforts are underway to develop and implement a risk-informed performance-based licensing framework for advanced reactors built on the establishment of an affirmative safety case. Such approaches offer the benefits of increased flexibility regarding key design and licensing decisions based on a detailed assessment and understanding of plant risk. These methods include the comprehensive analysis of scenarios that have historically been addressed through conservative and deterministic licensing analysis approaches, such as the evaluation of event sequences at frequencies of occurrence beyond the design basis. While the detailed analysis of low frequency events provides insights that can help justify alternative treatments to past conservatism, the findings are dependent on the quality and confidence associated with the analyses. The assessment of external hazards presents a unique challenge, as their potential frequency of occurrence, especially of large magnitude events, is inherently uncertain given the long return periods in question. The current project seeks to further explore the regulatory treatment of low frequency external events as part of a risk-informed performance-based framework. The goals are to identify the benefits and challenges of such approaches for advanced reactor vendors and to aid in the development of consistent and appropriate methodologies. As part of this effort, the current report summarizes initial project findings, with a focus on the evaluation of the seismic hazard. This includes an exploration of the implementation of a risk-informed performance-based approach through an example analysis. Based on the identified challenges associated with the probabilistic treatment of low frequency event sequences, additional performance-based methods were explored as complementary tools for demonstrating regulatory compliance. The report highlights key challenges and opportunities associated with the methods and outlines recommendations and future activities to assist with their implementation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification

Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity model, which suppresses noise in the gradients and mitigates local minima. Furthermore, the velocity model generated by neural networks is input to the same partial differential equation (PDE) solvers used in conventional FWI. The gradients of the neural networks and PDEs are calculated using automatic differentiation, which back propagates gradients through the acoustic PDEs and neural network layers to update the weights of the generative neural network. Experiments on 1D velocity models, the Marmousi model, and the 2004 BP model determine that NNFWI can mitigate local minima, especially for imaging high-contrast features such as salt bodies, and it significantly improves the inversion in the presence of noise. Adding dropout layers to the neural network model also allows analyzing the uncertainty of the inversion results through Monte Carlo dropout. NNFWI opens a new pathway to combine deep learning and FWI for exploiting the characteristics of deep neural networks and the high accuracy of PDE solvers. Because NNFWI does not require extra training data and optimization loops, it provides an attractive and straightforward alternative to conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2024

Temporal and spatial characterization of a thermogenic, fault-controlled gas hydrate system, Woolsey Mound, Gulf of Mexico

Woolsey Mound, located at Mississippi Canyon Lease Block 118 (MC118), is the site of the Gulf of Mexico hydrate research consortium’s seafloor observatory, where gas hydrates outcrop at the seafloor. The presence of gas hydrates in the mound is confirmed directly by coring and indirectly by 3D seismic reflection data. Craters, pockmarks, chemosynthetic communities, and authigenic carbonates populate the seafloor at Woolsey Mound. Each crater is characterized by a network of shallow crestal faults that connect the hydrate mound to the underlying allochthonous salt body. We characterize the temporal and spatial evolution of gas hydrates at Woolsey Mound under natural perturbations using four collocated 3D seismic reflection data sets that span over 14 years. Data acquisition differences embedded in the data sets arising from variation in geometry, sample rate, and phase are minimized using the “cross-equalization” method. Our results indicate that hydrate formation and dissociation vary temporally and spatially in close connection to the shallow crestal faults. Evidence of gas hydrate dissociation is observed over a period of three years (2000–2003), where major dissociation occurred along the southern portion of the crestal fault in the southeast crater. The dissociation is less prominent in the southwest crater. Evidence of methane venting is observed between 2000 and 2010, which is mostly concentrated in the southeast crater. The residual amplitude anomalies observed between 2000 and 2014 in the mound are mostly positive, implying that the methane venting had increased significantly. The positive anomalies are correlated with the methane seepage recorded in 2011. Our results indicate the evolution of a fault-controlled gas hydrate system in the northern Gulf of Mexico, which would aid in assessing its impact on the seafloor.

Geochemistry & Geophysics↗