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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Stable carbon isotope values of syndepositional carbonate spherules and micrite record spatial and temporal changes in photosynthesis intensity

Marine and lacustrine carbonate minerals preserve carbon cycle information, and their stable carbon isotope values (δ 13 C) are frequently used to infer and reconstruct paleoenvironmental changes. However, multiple processes can influence the δ 13 C values of bulk carbonates, confounding the interpretation of these values in terms of conditions at the time of mineral precipitation. Co-existing carbonate forms may represent different environmental conditions, yet few studies have analyzed δ 13 C values of syndepositional carbonate grains of varying morphologies to investigate their origins. Here, we combine stable isotope analyses, metagenomics, and geochemical modeling to interpret δ 13 C values of syndepositional carbonate spherules (>500 μm) and fine-grained micrite (<63 μm) from a ~1600-year-long sediment record of a hypersaline lake located on the coral atoll of Kiritimati, Republic of Kiribati (1.9°N, 157.4°W). Petrographic, mineralogic, and stable isotope results suggest that both carbonate fractions precipitate in situ with minor diagenetic alterations. The δ 13 C values of spherules are high compared to the syndepositional micrite and cannot be explained by mineral differences or external perturbations, suggesting a role for local biological processes. We use geochemical modeling to test the hypothesis that the spherules form in the surface microbial mat during peak diurnal photosynthesis when the δ 13 C value of dissolved inorganic carbon is elevated. In contrast, we hypothesize that the micrite may precipitate more continuously in the water as well as in sub-surface, heterotrophic layers of the microbial mat. Both metagenome and geochemical model results support a critical role for photosynthesis in influencing carbonate δ 13 C values. The down-core spherule–micrite offset in δ 13 C values also aligns with total organic carbon values, suggesting that the difference in the δ13C values of spherules and micrite may be a more robust, inorganic indicator of variability in productivity and local biological processes through time than the δ 13 C values of individual carbonate forms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Understanding a Stratigraphic Hydrothermal Resource – Geophysical Imaging at Steptoe Valley, Nevada

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The stratigraphic reservoir in northern Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, included data which further highlighted potential resource targets in the basin. Geophysical surveys, complemented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a >100MWe power-capable reservoir is likely economically accessible using conventional well placement and stimulation techniques in the Paleozoic carbonates of the deep/central basin of northern Steptoe Valley. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the northern Steptoe resource. The geophysical tools, interpretations, lessons learned, and public data generated by this study establish an exploration methodology to inform decisions for characterization and development of northern Steptoe Valley and other stratigraphic geothermal reservoirs in the western U.S.

15 GEOTHERMAL ENERGY↗

NASA NTRS1994

Carbon isotope effects associated with aceticlastic methanogenesis

The carbon isotope effects associated with synthesis of methane from acetate have been determined for Methanosarcina barkeri 227 and for methanogenic archaea in sediments of Wintergreen Lake, Michigan. At 37 degrees C, the 13C isotope effect for the reaction acetate (methyl carbon) --> methane, as measured in replicate experiments with M. barkeri, was - 21.3% +/- 0.3%. The isotope effect at the carboxyl portion of acetate was essentially equal, indicating participation of both positions in the rate-determining step, as expected for reactions catalyzed by carbon monoxide dehydrogenase. A similar isotope effect, - 19.2% +/- 0.3% was found for this reaction in the natural community (temperature = 20 degrees C). Given these observations, it has been possible to model the flow of carbon to methane within lake sediment communities and to account for carbon isotope compositions of evolving methane. Extension of the model allows interpretation of seasonal fluctuations in 13C contents of methane in other systems.

NASA Discipline Exobiology↗

NASA NTRS2008

K/TH in Achondrites and Interpretation of Grand Data for the Dawn Mission

The Dawn mission will explore 4 Vesta [1], a highly differentiated asteroid believed to be the parent body of the howardite, eucrite and diogenite (HED) meteorite suite [e.g. 2]. The Dawn spacecraft is equipped with a gamma-ray and neutron detector (GRaND), which will enable measurement and mapping of elemental abundances on Vesta s surface [3]. Drawing on HED geochemistry, Usui and McSween [4] proposed a linear mixing model for interpretation of GRaND data. However, the HED suite is not the only achondrite suite representing asteroidal basaltic crusts; others include the mesosiderites, angrites, NWA 011, and possibly Ibitira, each of which is thought to have a distinct parental asteroid [5]. Here we critically examine the variability of GRaND-analyzed elements, K and Th, in HED meteorites, and propose a method based on the K-Th systematics to distinguish between HED and the other differentiated achondrites. Maps of these elements might also recognize incompatible element enriched areas such as mapped locally on the Moon (KREEP) [6], and variations in K/Th ratios might indicate impact volatilization of K. We also propose a new mixing model using elements that will be most reliably measured by GRaND, including K.

Usui, T.↗

DOE OSTI2025

AK112: Full Waveform Inversion Tomography of Alaska Improves Waveform Fits While Imaging Crustal, Mantle, and Slab Structure

We report a full waveform inversion tomography model of Alaska and the surrounding regions, inferring radially anisotropic shear and isotropic compressional wavespeeds by fitting complete waveforms from 120 regional earthquakes. Our multiscale approach inverted time–frequency phase misfits (maximum period of 100 s), starting with a minimum period of 40 s and ending at 20 s in 7 stages and 112 total iterations. The model (AK112) was evaluated by computing the misfits for 36 independent validation events. We find that misfit reductions were large and equal (∼55%) for both the inversion and validation data sets, providing confidence in the model. AK112 also provides much better waveform fits compared to other reported models for the region, including an isotropic version of itself, highlighting the importance of anisotropy. The model resolves known crustal, upper mantle, and slab structure to depths of 100 km with new detail: sedimentary basins in the Alaskan Shelf, Cook Inlet, and Colville basins, among others; discontinuous lithospheric structure across major terrane boundaries; and subducting slab geometry and back‐arc volcanic sources. In addition to tectonic interpretations, the model enables full waveform simulations for long‐period earthquake ground motions and source characterization (e.g., moment tensor and finite‐fault inversion).

Rodgers, Arthur [Lawrence Livermore National Labor↗

DOE OSTI2019

Execution of an Alternative Modeling Strategy for Closure of the Rainier Mesa Corrective Action Unit at the Nevada National Security Site - 19099

The Underground Test Area (UGTA) Project of the U.S Department of Energy (DOE) is investigating radionuclide contaminant movements in groundwater derived from a decades-long period of underground nuclear testing at the Nevada National Security Site (NNSS) to support regulatory corrective action and closure requirements. The project is built around an integrated campaign of drilling and sampling, data interpretation, computer modeling, and broader monitoring activities conducted under a joint Federal Facilities Agreement and Consent Order (1996 as amended) (FFACO) between the State of Nevada, the DOE, and the U.S. Department of Defense. The FFACO strategy is applied to individual Corrective Action Units (CAUs) that roughly envelop disparate geographic testing areas at the NNSS. The Rainier Mesa/Shoshone Mountain (RMSM) CAU is a geologically and hydrologically complex system lying in a remote up-gradient section of the NNSS. It was the site of 67 underground tests conducted between 1957 and 1992, 61 at Rainier Mesa proper, and 6 at Shoshone Mountain, isolated to its south. The original strategy to close the RMSM CAU required a simulation-based investigation to define the evolution of contaminated volumes of groundwater (in 3D) under uncertainty as a means to define a surface-projected contaminant boundary and to serve as a basis for longer term monitoring activities. Early models developed under this strategy proved to be quite complex and difficult to calibrate. Additional costs required to better characterize the system were judged to be out of balance with the low risks associated with this particular CAU. As a result, an Alternative Modeling Strategy (AMS) was adopted. The AMS removes the requirement for developing ensembles of 3D simulations and replaces it with a simpler one requiring the generation of multiple 1D streamline models. These are meant to encompass a range of alternative, yet plausible release and transport scenarios as a means to bound the overall lateral extent of contaminant transport. This paper will review the modeling approach developed under the AMS and its application to the Rainier Mesa portion of the CAU. Preliminary model results made under different parametric assumptions and boundary condition scenarios will be presented. Additional refinements of the model and its further application will be discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Building kinetic models for metabolic engineering

Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this study we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Three-Dimensional Modeling of Fracture Clusters in Geothermal Reservoirs

The objective of this is to develop a 3-D numerical model for simulating mode I, II, and III (tensile, shear, and out-of-plane) propagation of multiple fractures and fracture clusters to accurately predict geothermal reservoir stimulation using the virtual multi-dimensional internal bond (VMIB). Effective development of enhanced geothermal systems can significantly benefit from improved modeling of hydraulic fracturing. In geothermal reservoirs, where the temperature can reach or exceed 350oC, thermal and poro-mechanical processes play an important role in fracture initiation and propagation. In this project hydraulic fracturing of hot subsurface rock mass will be numerically modeled by extending the virtual multiple internal bond theory and implementing it in a finite element code, WARP3D, a three-dimensional finite element code for solid mechanics. The new constitutive model along with the poro-thermoelastic computational algorithms will allow modeling the initiation and propagation of clusters of fractures, and extension of pre-existing fractures. The work will enable the industry to realistically model stimulation of geothermal reservoirs. The project addresses the Geothermal Technologies Office objective of accurately predicting geothermal reservoir stimulation (GTO technology priority item). The project goal will be attained by: (i) development of the VMIB method for application to 3D analysis of fracture clusters; (ii) development of poro- and thermoelastic material sub-routines for use in 3D finite element code WARP3D; (iii) implementation of VMIB and the new material routines in WARP3D to enable simulation of clusters of fractures while accounting for the effects of the pore pressure, thermal stress and inelastic deformation; (iv) simulation of 3D fracture propagation and coalescence and formation of clusters, and comparison with laboratory compression tests; and (v) application of the model to interpretation of injection experiments (planned by our industrial partner) with reference to the impact of the variations in injection rate and temperature, rock properties, and in-situ stress.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

A Deep Gravity model for mobility flows generation

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2004

Lava Lakes on Io: New Perspectives from Modeling

Ionian paterae are a class of volcanic feature that are characterized by irregular craters with steep walls, flat floors, and arcuate margins that may or may not exhibit nesting. Loki (310 W, 12 N) is Io's largest patera at approx.200 km in diameter (Figure 1), and may account for 15% of Io's total heat flow. Earth-based infrared data, as well as information collected using the Galileo Near-Infrared Mapping Spectrometer (NIMS) and the Photopolarimeter Radiometer (PPR) have been used to interpret Loki s eruption style. Debate continues over whether Loki s occasional (periodic or not) temperature increases are due to an overturning lava lake within the patera, or to an eruption of surface flows on the patera floor. Interpretation of model results and comparisons with active terrestrial lava lakes suggest that Loki behaves quite differently from active lava lakes on Earth, and that surface flows (rather than an overturning lava lake) are a more likely explanation of Loki's thermal brightening.

Gregg, Tracy K. P.↗

NASA NTRS1994

K'-band observations of the evil eye galaxy: Are the optical and near-infrared dust albedos identical?

New measurements of the reduction of the V-band surface brightness across the prominent dust feature in the galaxy NGC 4826 are compared with corresponding increases in the V-K' color within the context of radiative transfer models invoking both absorption and scattering. The K'-band surface brightness is found to be higher than expected from standard dust models. We interpret the difference as resulting from a high effective dust albedo at K', with a likely value in excess of 0.8, provided the near-IR extinction curve in NGC 4826 is identical to the Galactic one. The high effective albedo may result from scattering by dust with a maximum grain size at least twice as large as assumed by standard models, a conclusion already indirectly hinted at by recent studies of dust star-forming regions and reflection nebulae. At least part of the high effective albedo at K' may result from near-IR nonequilibrium continuum emission attributable to very small grains.

Witt, Adolf N.↗

DOE OSTI2023

Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems

Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens.Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn,stimulate advances in machine learning as well.

58 GEOSCIENCES↗

NASA NTRS1974

Radiative contribution to the thermal conductivity of fibrous insulations

An approach is shown for using a simple two-flux model to interpret infrared transmission data for a variety of reuseable surface insulations materials and to calculate the radiation transmission. A description is given of preliminary experiments on mullite and silica-based materials. The calculated parameters are compared with the measured values of the total thermal conductivity, as determined on guarded hot plate equipment. It is pointed out that for many samples the newly developed four-flux model must be utilized because the scattering properties of the fibers are often dependent on the wavelength of the radiation.

Linford, R. M. F.↗

NASA NTRS2013

An Outburst Scenario for the X-ray Spectral Variability in 3C 111

We present a combined Suzaku and Swift BAT broad-band E=0.6-200 keV spectral analysis of three 3C 111 observations obtained in 2010. The data are well described with an absorbed power-law continuum and a weak (R approximately equal to 0.2) cold reflection component from distant material. We constrain the continuum cutoff at E(sub C) approximately equal to 150- 200 keV, which is in accordance with X-ray Comptonization corona models and supports claims that the jet emission is only dominant at much higher energies. Fe XXVI Ly alpha emission and absorption lines are also present in the first and second observations, respectively. The modelling and interpretation of the emission line is complex and we explore three possibilities. If originating from ionized disc reflection, this should be emitted at r(sub in) greater than or equal to 50 r(sub g) or, in the lamp-post configuration, the illuminating source should be at a height of h greater than or equal to 30 r(sub g) over the black hole. Alternatively, the line could be modeled with a hot collisionally ionized plasma with temperature kT = 22.0(sup +6.1)(sub −3.2) keV or a photo-ionized plasma with logXi=4.52(sup +0.10)(sub −0.16) erg per second cm, and column density N(sub H) greater than 3×10(sup 23) per square centimeter. However, the first and second scenarios are less favored on statistical and physical grounds, respectively. The blue-shifted absorption line in the second observation can be modelled as an ultra-fast outflow (UFO) with ionization parameter logXi=4.47(sup +0.76) (sub −0.04) erg per second cm, column density NH=(5.3(sup +1.8)(sub −1.3))×10(sup 22) per square centimeter and outflow velocity v(sub out) = 0.104+/-0.006c. Interestingly, the parameters of the photoionized emission model remarkably match those of the absorbing UFO, supporting the possibility that the same material could be responsible for both emission and absorption. We suggest an outburst scenario in which an accretion disc wind, initially lying out of the line of sight and observed in emission, then crosses our view to the source and it is observed in absorption as a mildly-relativistic UFO.

Outburst↗

DOE OSTI2023

Explaining machine-learning models for gamma-ray detection and identification

As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Definitions, methods, and applications in interpretable machine learning

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Identifying Critical Infrastructure in Imagery Data Using Explainable Convolutional Neural Networks

To date, no method utilizing satellite imagery exists for detailing the locations and functions of critical infrastructure across the United States, making response to natural disasters and other events challenging due to complex infrastructural interdependencies. This paper presents a repeatable, transferable, and explainable method for critical infrastructure analysis and implementation of a robust model for critical infrastructure detection in satellite imagery. This model consists of a DenseNet-161 convolutional neural network, pretrained with the ImageNet database. The model was provided additional training with a custom dataset, containing nine infrastructure classes. The resultant analysis achieved an overall accuracy of 90%, with the highest accuracy for airports (97%), hydroelectric dams (96%), solar farms (94%), substations (91%), potable water tanks (93%), and hospitals (93%). Critical infrastructure types with relatively low accuracy are likely influenced by data commonality between similar infrastructure components for petroleum terminals (86%), water treatment plants (78%), and natural gas generation (78%). Local interpretable model-agnostic explanations (LIME) was integrated into the overall modeling pipeline to establish trust for users in critical infrastructure applications. The results demonstrate the effectiveness of a convolutional neural network approach for critical infrastructure identification, with higher than 90% accuracy in identifying six of the critical infrastructure facility types.

97 MATHEMATICS AND COMPUTING↗