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At least 307 records · Page 17

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NASA NTRS1984

A critical assessment of viscous models of trench topography and corner flow

Stresses for Newtonian viscous flow in a simple geometry (e.g., corner flow, bending flow) are obtained in order to study the effect of imposed velocity boundary conditions. Stress for a delta function velocity boundary condition decays as 1/R(2); for a step function velocity, stress goes as 1/R; for a discontinuity in curvature, the stress singularity is logarithmic. For corner flow, which has a discontinuity of velocity at a certain point, the corresponding stress has a 1/R singularity. However, for a more realistic circular-slab model, the stress singularity becomes logarithmic. Thus the stress distribution is very sensitive to the boundary conditions, and in evaluating the applicability of viscous models of trench topography it is essential to use realistic geometries. Topography and seismicity data from northern Hoshu, Japan, were used to construct a finite element model, with flow assumed tangent to the top of the grid, for both Newtonian and non-Newtonian flow (power law 3 rheology). Normal stresses at the top of the grid are compared to the observed trench topography and gravity anomalies. There is poor agreement. Purely viscous models of subducting slables with specified velocity boundary conditions do not predict normal stress patterns compatible with observed topography and gravity. Elasticity and plasticity appear to be important for the subduction process.

Zhang, J.↗

NASA NTRS1985

A critical assessment of viscous models of trench topography and corner flow

Zhang, J.↗

DOE OSTI2020

Semisupervised Learning for Seismic Monitoring Applications

The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. Finally, in geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.

58 GEOSCIENCES↗

NASA NTRS1992

An application of synthetic seismicity in earthquake statistics - The Middle America Trench

The way in which seismicity calculations which are based on the concept of fault segmentation incorporate the physics of faulting through static dislocation theory can improve earthquake recurrence statistics and hone the probabilities of hazard is shown. For the Middle America Trench, the spread parameters of the best-fitting lognormal or Weibull distributions (about 0.75) are much larger than the 0.21 intrinsic spread proposed in the Nishenko Buland (1987) hypothesis. Stress interaction between fault segments disrupts time or slip predictability and causes earthquake recurrence to be far more aperiodic than has been suggested.

Ward, Steven N.↗

NASA NTRS1992

Engineering design constraints of the lunar surface environment

Living and working on the lunar surface will be difficult. Design of habitats, machines, tools, and operational scenarios in order to allow maximum flexibility in human activity will require paying attention to certain constraints imposed by conditions at the surface and the characteristics of lunar material. Primary design drivers for habitat, crew health and safety, and crew equipment are: ionizing radiation, the meteoroid flux, and the thermal environment. Secondary constraints for engineering derive from: the physical and chemical properties of lunar surface materials, rock distributions and regolith thicknesses, topography, electromagnetic properties, and seismicity. Protection from ionizing radiation is essential for crew health and safety. The total dose acquired by a crew member will be the sum of the dose acquired during EVA time (when shielding will be least) plus the dose acquired during time spent in the habitat (when shielding will be maximum). Minimizing the dose acquired in the habitat extends the time allowable for EVA's before a dose limit is reached. Habitat shielding is enabling, and higher precision in predicting secondary fluxes produced in shielding material would be desirable. Means for minimizing dose during a solar flare event while on extended EVA will be essential. Early warning of the onset of flare activity (at least a half-hour is feasible) will dictate the time available to take mitigating steps. Warning capability affects design of rovers (or rover tools) and site layout. Uncertainty in solar flare timing is a design constraint that points to the need for quickly accessible or constructible safe havens.

Morrison, D. A.↗

NASA NTRS1996

Transient Creep of a Composite Lower Crust: A Polymineralic Basis for Rapidly Evolving Postseismic Deformation Modes - 2

Postseismic horizontal strain and displacement following the June 28, 1992, Landers, California, earthquake (M(sub W) 7.3) is broad scale and cannot be explained solely by delayed afterslip located at the rupturing fault trace. Both the observed strain at Pifion Flat Observatory (PFO) and observed Global Positioning System receiver velocities evolve rapidly after the Landers-Big Bear earthquake sequence. The observed exponential decay of these motions, with timescales of 4-34 days, may reflect a soft creep rheology in the lower crust and brittle-ductile transition zone or even within the seismogenic crust itself. Here a simple model of a two-dimensional screw dislocation in a layered Maxwell viscoelastic Earth is employed in conjunction with a composite rheology to demonstrate that the short timescale transient response modes (approx. = 4-34 days) are consistent with the behavior of a biviscous lower crust. The lowest viscosity of this system is derivable from laboratory experimental data on the long-term creep of natural quarztites, and the highest viscosity is consistent with isostasy-related lower crustal flow in a continental extensional tectonic environment. The model predicts significant stress relaxation at the base of the seismogenic crust. Near the base of the seismogenic zone, and about 4 km away from the mainshock, the rate of predicted relaxation is of the order of 0.01 MPa/ d during the first 20 days of postseismic flow. Oblate spheroidal inclusions at 5% concentration levels that are both aligned and fairly flat in shape and that have a viscosity of 3-4 x 10(exp 15) Pa s are consistent with both the amplitude and decay time of horizontal crustal strain observed at PFO after the Landers mainshock. It is speculated that the structures exposed in cross sections and in seismic reflection profiles of the lower crust that have mylonitic associations are, in part, the cause of such rapid postseismic evolution in southeastern California. Unmylonitized quartz-rich rock at sufficiently elevated temperatures could also contribute to the rapid decay modes.

Ivins, Erik R.↗

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 OSTI2021

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects

Researchers at New Mexico Tech, Los Alamos National Laboratory (LANL), Sandia National Laboratory (SNL), and other collaborators are developing a methodology to improve characterization of stress in the subsurface by means of a model-based inversion of reservoir engineering data, time lapse seismic measurements, and microseismicity. Historically, various geophysical techniques have been used in efforts to understand the state of stress in the subsurface through direct and indirect imaging of stress sensitive features (faults and fractures) and observations of transient stress related observations (time variant elastic moduli and microseismicity). Standard direct and indirect techniques for seismic fault and fracture imaging suffer from detectability limits due to reliance on high data quality and multiplicity. Variations in effective elastic properties from inversion of high quality seismic (Vertical Seismic Profile (VSP)) data may be used to infer stress near the wellbore through integration with independent experimental characterization of the stress-velocity relationship. Given a sufficiently robust observation network, microseismic emissions may be inverted to characterize focal mechanisms which, together with supporting assumptions and constraints, inform estimates of in-situ stress. While each of these techniques contributes in part to the characterization of stress within limited spatial and temporal domains, no one method provides an unambiguous stress measurement or a predictive capability over a site scale spatial extent. Challenges associated with solution non-uniqueness, measurement ambiguity, and irregular sampling may be greatly minimized through combination of one or more of independent measurements within a common framework in which realistic geological, hydrodynamic, geomechanical, and seismological constituent process models may act as constraints.

02 PETROLEUM↗

DOE OSTI2023

ANN-based ground motion model for Turkey using stochastic simulation of earthquakes

SUMMARY Turkey is characterized by a high level of seismic activity attributed to its complex tectonic structure. The country has a dense network to record earthquake ground motions; however, to study previous earthquakes and to account for potential future ones, ground motion simulations are required. Ground motion simulation techniques offer an alternative means of generating region-specific time-series data for locations with limited seismic networks or regions with seismic data gaps, facilitating the study of potential catastrophic earthquakes. In this research, a local ground motion model (GMM) for Turkey is developed using region-specific simulated records, thus constructing a homogeneous data set. The simulations employ the stochastic finite-fault approach and utilize validated input-model parameters in distinct regions, namely Afyon, Erzincan, Duzce, Istanbul and Van. To overcome the limitations of linear regression-based models, artificial neural network is used to establish the form of equations and coefficients. The predictive input parameters encompass fault mechanism (FM), focal depth (FD), moment magnitude (Mw), Joyner and Boore distance (RJB) and average shear wave velocity in the top 30 m (Vs30). The data set comprises 7359 records with Mw ranging between 5.0 and 7.5 and RJB ranging from 0 to 272 km. The results are presented in terms of spectral ordinates within the period range of 0.03–2.0 s, as well as peak ground acceleration and peak ground velocity. The quantification of the GMM uncertainty is achieved through the analysis of residuals, enabling insights into inter- and intra-event uncertainties. The simulation results and the effectiveness of the model are verified by comparing the predicted values of ground motion parameters with the observed values recorded during previous events in the region. The results demonstrate the efficacy of the proposed model in simulating physical phenomena.

Karimzadeh, Shaghayegh (ORCID:0000000337531676)↗

DOE OSTI2020

Effects of composition and pressure on electronic states of iron in bridgmanite

Electronic states of iron in the lower mantle's dominant mineral, (Mg,Fe,Al)(Fe,Al,Si)O 3 bridgmanite, control physical properties of the mantle including density, elasticity, and electrical and thermal conductivity. However, the determination of electronic states of iron has been controversial, in part due to different interpretations of Mössbauer spectroscopy results used to identify spin state, valence state, and site occupancy of iron. We applied energy-domain Mössbauer spectroscopy to a set of four bridgmanite samples spanning a wide range of compositions: 10–50% Fe/total cations, 0–25% Al/total cations, 12–100% Fe 3+ /total Fe. Measurements performed in the diamond-anvil cell at pressures up to 76 GPa below and above the high to low spin transition in Fe 3+ provide a Mössbauer reference library for bridgmanite and demonstrate the effects of pressure and composition on electronic states of iron. Results indicate that although the spin transition in Fe 3+ in the bridgmanite B-site occurs as predicted, it does not strongly affect the observed quadrupole splitting of 1.4 mm/s, and only decreases center shift for this site to 0 mm/s at ~70 GPa. Thus center shift can easily distinguish Fe 3+ from Fe 2+ at high pressure, which exhibits two distinct Mössbauer sites with center shift ~1 mm/s and quadrupole splitting 2.4–3.1 and 3.9 mm/s at ~70 GPa. Correct quantification of Fe 3+ /total Fe in bridgmanite is required to constrain the effects of composition and redox states in experimental measurements of seismic properties of bridgmanite. Furthermore, in Fe-rich, mixed-valence bridgmanite at deep-mantle-relevant pressures, up to ~20% of the Fe may be a Fe 2.5+ charge transfer component, which should enhance electrical and thermal conductivity in Fe-rich heterogeneities at the base of Earth's mantle.

58 GEOSCIENCES↗

DOE OSTI2020

Validation of (not-historical) large-event near-fault ground-motion simulations for use in civil engineering applications

Ground-motion simulations generated from physics-based wave propagation models are gaining increasing interest in the engineering community for their potential to inform the performance-based design and assessment of infrastructure residing in active seismic areas. A key prerequisite before the ground-motion simulations can be used with confidence for application in engineering domains is their comprehensive and rigorous investigation and validation. This article provides a four-step methodology and acceptance criteria to assess the reliability of simulated ground motions of not historical events, which includes (1) the selection of a population of real records consistent with the simulated scenarios, (2) the comparison of the distribution of Intensity Measures (IMs) from the simulated records, real records, and Ground-Motion Prediction Equations (GMPEs), (3) the comparison of the distribution of simple proxies for building response, and (4) the comparison of the distribution of Engineering Demand Parameters (EDPs) for a realistic model of a structure. Specific focus is laid on near-field ground motions (<10km) from large earthquakes (M w 7), for which the database of real records for potential use in engineering applications is severely limited. The methodology is demonstrated through comparison of (2490) near-field synthetic records with 5 Hz resolution generated from the Pitarka et al (2019) kinematic rupture model with a population of (38) pulse-like near-field real records from multiple events and, when applicable, with NGA-W2 GMPEs. Lastly, the proposed procedure provides an effective method for informing and advancing the science needed to generate realistic ground-motion simulations, and for building confidence in their use in engineering domains.

42 ENGINEERING↗

DOE OSTI2021

Report on the LLNL Global Full-waveform Inversion Workflow and Progress

LLNL has developed the SPiRaL global seismic tomography model based upon millions of body wave travel times and surface wave dispersion curves in the period range of 25-200 seconds (Simmons et al., 2021). The motivation to construct the SPiRaL model (which stands for S-, P-, Rayleigh, and Love waves) is based upon the guidance provided by a 3-D model workshop held in Berkeley in 2007 and documented in a paper by Zucca et al. (2009). It was concluded in Zucca et al. (2009) that a “practical and obtainable” model is one that predicts body wave travel times for event location while also predicting surface wave velocities for moment tensor estimation. We have largely followed that guidance which has led to the construction of SPiRaL. Looking forward, we now seek to improve the global model through full waveform inversion (FWI). This necessitates the development/customization of an efficient and flexible modeling workflow to automate the process using LLNL’s supercomputing platforms. This report briefly outlines the work that being performed at LLNL to develop the necessary tools and framework to update the SPiRaL global model with full waveform information.

58 GEOSCIENCES↗

NASA NTRS1984

The distribution of earthquakes with depth and stress in subducting slabs

The global variation of Benioff zone seismicity with depth and the orientation of stress axes of deep and intermediate earthquakes is explained using numerical models of subducting slabs. Models that match the seismicity and stress require a barrier to flow at the 670 km seismic discontinuity. The barrier may be a viscosity increase of at least an order of magnitude or a chemical discontinuity. Instantaneous flow is subparallel to the slabs for models with a viscosity increase but contorted for models with a chemical barrier. Log N (number of earthquakes) decreases linearly to 250-300 km depth and increases thereafter. Stress magnitude in the models shows the same pattern, in accord with experiments showing N proportional to e(k-sigma), with k a constant and sigma the stress magnitude. The models predict downdip compression in the slabs at depths below 300-400 km, as observed for earthquake stress axes.

Vassiliou, M. S.↗

DOE OSTI2021

Thermal and Deposition Modeling of the Canister Deposition Field Demonstration

This report provides initial thermal and deposition modeling results of the spent nuclear fuel (SNF) storage system planned for use in the Canister Deposition Field Demonstration (CDFD). The goal of the CDFD testing is to collect deposition measurements on the surface of the dry shielded canister (DSC) to aid in chloride-induced stress corrosion crack (CISCC) research. Ideally this testing will occur in a marine coastal environment but at the time of this report no official site has been selected. The CDFD will use the NUHOMS ® Advanced Horizontal Storage Module – High Seismic (AHSM-HS) with a 32PTH2 canister. The SNF assemblies will be replaced with electrical heaters. Canisters are currently being outfitted with these heaters and tested at Sandia National Laboratory (SNL). Thermal modeling was conducted to ensure the electrical heaters were representative of the temperature distribution found within a storage system with SNF. Deposition models were built to evaluate contaminant deposition on SNF canisters. The CDFD testing will help to validate these models, which will then be used to plan and inform on-site test programs and predictive models for the timing and occurrence of canister CISCC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness

In this article, full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasingly studied with various neural network architectures to improve accuracy and computational efficiency. Nevertheless, the applicability of pre-trained neural networks is severely restricted by potential discrepancies between the source function used in the field survey and the one utilized during training. Here, we develop a Fourier-enhanced deep operator network (Fourier-DeepONet) for FWI with the generalization of seismic sources, including the frequencies and locations of sources. Specifically, we employ the Fourier neural operator as the decoder of DeepONet, and we utilize source parameters as one input of Fourier-DeepONet, facilitating the resolution of FWI with variable sources. To test Fourier-DeepONet, we develop three new and realistic FWI benchmark datasets (FWI-F, FWI-L, and FWI-FL) with varying source frequencies, locations, or both. Our experiments demonstrate that compared with existing data-driven FWI methods, Fourier-DeepONet obtains more accurate predictions of subsurface structures in a wide range of source parameters. Moreover, the proposed Fourier-DeepONet exhibits superior robustness when handling data with Gaussian noise or missing traces and sources with Gaussian noise, paving the way for more reliable and accurate subsurface imaging across diverse real conditions.

42 ENGINEERING↗

DOE OSTI2021

Progressive transfer learning for low-frequency data prediction in full waveform inversion

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. Here, the synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

02 PETROLEUM↗

NASA NTRS1978

A comparison of two types of velocity models for the lunar crust: Smooth continuous and stepwise layered

The data from the Apollo-14 and Apollo-16 Active Seismic Experiments were reanalyzed and show that a power-law velocity variation with depth is consistent with both the traveltimes and amplitudes of the first arrivals for source-to-geophone separations up to 32m. The data were improved by removing spurious glithches, flickering and stacking. While this improved the signal-to-noise ratios, it was not possible to measure the arrivals beyond 32m. The physical evidence that the shallow lunar regolith is made up of fine particles adds weight to the 1/6-power velocity model. The 1/6-power law predicts the traveltime t(x), varies with separation, x, as t(x) = t sub 0 (x/x sub 0) to the 5/6 power and, using a first-order theory, the amplitude, A(x), varies as A(x) = A sub 0 (x/x sub 0) to the (13-m)/12, M 1; the layer-velocity model predicts t(x) = t sub 0 (x/xsub 0) and A(x) = A sub 0 (x/x sub 0) to the 2nd power.

Gangi, A. F.↗

DOE OSTI2020

Imaging of a fluid injection process using geophysical data — A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts.

58 GEOSCIENCES↗