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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Striped Data Server for Scalable Parallel Data Analysis

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. As a result, we have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗
DOE OSTI2017

Washington Geothermal Play Fairway Analysis Data From Potential Field Studies

A recent study which adapts play fairway analysis (PFA) methodology to assess geothermal potential was conducted at three locations (Mount Baker, Mount St. Helens seismic zone, and Wind River valley) along the Washington Cascade Range (Forson et al. 2017). Potential field (gravity and magnetic) methods which can detect subsurface contrasts in physical properties, provides a means for mapping and modeling subsurface geology and structure. As part of the WA-Cascade PFA project, we performed potential field studies by collecting high-resolution gravity and ground-magnetic data, and rock property measurements to (1) identify and constrain fault geometries (2) constrain subsurface lithologic distribution (3) study fault interactions (4) identify areas favorable to hydrothermal flow, and ultimately (5) guide future geothermal exploration at each location.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2 ) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2 , decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Striped Data Analysis Framework

Traditionally, High Energy data analysis is based on the model where data are stored in files and analyzed by running multiple analysis processes, each reading one or more of the data files. This process involves repeated data reduction step, which produces smaller files, which is time consuming and leads to data duplication. We propose an alternative approach to data storage and analysis, based on the Big Data technologies. The idea is to store each element of data once and only once in a distributed scalable database and analyze data by reading only "interesting" pieces of data from the database. To make this approach possible, we developed columnar Striped Data Representation Format as the basis of the framework. Traditional columnar approach allows for efficient analysis of complex data structures. While keeping all the benefits of columnar data representation, striped mechanism goes further by enabling efficient parallelization of computations and flexible dist ribution of data analysis. The framework includes scalable and elastic data storage, compute and user analysis backend components. The framework uses off-the shelf web services and data caching technologies as the compute/data co-location mechanism. Flexible architecture allows the framework run in the cloud using container technologies. The framework offers Python/Jupyter as the user analysis backend platform, but can also run in command line or batch mode. In the article, we will present the results of the FNAL-LDRD-2016-032 FNAL LDRD project, the design, implementation, features and performance characteristics of the Striped Data Analysis Framework.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Accurate and rapid acoustic damage characterization in complex structures using sparse sensor networks and deep learning models

Damage diagnosis in critical components is essential for ensuring the safety and reliability of operations across industries, spanning manufacturing, aerospace, and energy. Traditional acoustic nondestructive testing methods primarily focus on detecting defects through the direct scattering of single-mode incident waves from the damage, which limit their applicability to simple structures and small inspection areas. Our earlier research demonstrated that machine learning algorithms combined with sparse sensor networks can identify critical defect signatures even from multiply scattered, multi-mode acoustic signals, indicating the potential for improved defect inspection in complex, real-world structures. In this work, we demonstrate the successful implementation of this approach in a fixed sensor configuration to rapidly and accurately detect simulated defects in a geometrically complex, real-world structure, a brake rotor hub. Three different types of defects were physically simulated on the surface of the hub, and the collected data were used to train an autoencoder-based deep learning model. Two models were tested, one using single measurements and the other using multiple measurements taking advantage of the spatial distribution of the sensor network. After training, the multi-measurement model achieved 100 % accuracy in identifying, classifying, and locating unseen, unique damages. This work illustrates the potential of the proposed method for a wide range of industrial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

EXAFS investigation of the local structure in URu 2-x Fe x Si 2 : Evidence for distortions below 100 K

X-ray absorption measurements at the U L III , Ru K , and Fe K edges are reported for the hidden order (HO) material URu 2 - x Fe x Si 2 ( x = 0 , 0.05, 0.08, 0.10, 0.12, 0.15, and 0.20) as a function of x and temperature T . Furthermore, when Fe is substituted for Ru, the local structure about Fe shrinks slightly and the first neighbor Fe-Si bond length decreases by ≈ 0.05 Å . More importantly excess disorder is observed below 80–100 K (the coherence temperature T * ) in plots of the Debye-Waller factor σ 2 ( σ is the width of the pair distribution function); at low T the data deviate from the usual Einstein or correlated-Debye model plots. This excess disorder is most prominent for the Ru-Si bond, and σ 2 actually increases below 80 K. These results suggest a local orthorhombic distortion with B 1 g -like symmetry that develops below 80–100 K. A model that describes these local distortions is presented, and discussed in terms of other measurements that indicate a breaking of fourfold symmetry at low T . In addition, the square root of the difference between σ 2 ( T ) for the Ru-Si pair and a Debye fit to these data serves as an order parameter for this orthorhombic distortion, in the temperature range below 100 K. This quantity is a length related to a - b , the difference between the a and b lattice constants in the orthorhombic phase, and provides a connection between this distortion and T * . X-ray absorption near edge structure (XANES) measurements also show that there are no changes in the edge positions down to 0.1 eV for any edge as a function of x , for T in the HO regime.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Development of a Physical-Topological Model for the Response of a High-Power Vertical DMOS Transistor to the Effect of Pulsed Gamma-Radiation

A method for developing models of semiconductor devices with two-dimensional nonuniform concentration profiles of donors and acceptors in working regions of a semiconductor device structure is for the first time proposed based on a complex of physical-topological modeling of charge carrier transport and process simulation of the forming processes of the device structure. The application of process simulation is due to the need to correctly define the parameters of the semiconductor device structure, which are used as initial data to calculate the electron transport according to the physical-topological model. The parameters of production processes of ion implantation, diffusion, and lithography, which were refined during process simulation, are determined for a high-power metal—oxide—semiconductor (MOS) transistor forming by the double diffusion method in accordance with known electrical characteristics and measured geometrical sizes of the structure. This results in two-dimensional distribution profiles of donors and acceptors in p–n junctions necessary to calculate the transistor breakdown under the effect of pulsed γ radiation. Breakdown processes are modeled with the help of the physical-topological model based on the Poisson and continuity equations as well as expressions for the diffusion and drift current densities in the transistor. Accounting for carriers formed at the instant of γ irradiation is implemented by the introduction of the dependence of the generation coefficient of electron–hole pairs on the radiation-dose power. The results of calculations correlate well with the experimental data, which makes it possible to give a conclusion regarding the adequacy of the proposed complex model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cloud Atlas: Unraveling the Vertical Cloud Structure with the Time-series Spectrophotometry of an Unusually Red Brown Dwarf

Rotational modulations of emission spectra in brown dwarf and exoplanet atmospheres show that clouds are often distributed non-uniformly in these ultracool atmospheres. The spatial heterogeneity in cloud distribution demonstrates the impact of atmospheric dynamics on cloud formation and evolution. In this study, we update the Hubble Space Telescope (HST) time-series data analysis of the previously reported rotational modulations of WISEP J004701+680352—an unusually red late-L brown dwarf with a spectrum similar to that of the directly imaged planet HR 8799e. We construct a self-consistent spatially heterogeneous cloud model to explain the HST and the Spitzer time-series observations, as well as the time-averaged spectra of WISE 0047. Additionally, in the heterogeneous cloud model, a cloud thickness variation of around one pressure scale height explains the wavelength dependence in the HST near-IR spectral variability. By including disequilibrium CO/CH 4 chemistry, our models also reproduce the redder $J-{K}_{{\rm{s}}}$ color of WISE 0047 compared to that of field brown dwarfs. We discuss the impact of vertical cloud structure on atmospheric profile and estimate the minimum eddy diffusivity coefficient for other objects with redder colors. Our data analysis and forward modeling results demonstrate that time-series spectrophotometry with a broad wavelength coverage is a powerful tool for constraining heterogeneous atmospheric structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Sparse thermal data for cellular automata modeling of grain structure in additive manufacturing

Grain growth in the wake of the melt pool formed during alloy-based additive manufacturing (AM) is complex and multifaceted, depending on parameters governing heat transport, fluid flow, and solidification itself. Cellular automata (CA) models have proven effective in providing computationally efficient and physically sound predictions of grain structure for several AM problems, but their efficiency is tied to the performance of heat transport models. CA models use only a small portion of the problem's temperature data (near the moving melt pool boundary), and much of the CA calculations do not affect the final result due to re-melting of material. Coupling of and communication between heat transport and solidification models, and eliminating operations irrelevant towards final grain structure prediction, will be necessary for using these methods for efficient simulation of large parts. In this work, we introduce a procedure of decoupling the CA from temperature field simulation, using files of relevant temperature data written by the heat transport model. This approach is validated against the standard coupling approach using data obtained through the computational fluid dynamics software OpenFOAM. Negligible differences are seen in grain size, volume, and texture distributions for multilayer simulation of test problems, while the quantity of temperature data for these test problems was reduced by four orders of magnitude (from 100s of GB to 10s of MB) and the code performance sped up by a factor of around 50. Furthermore, variability in microstructure as a function of cell size, substrate, time step, and nucleation parameters is studied, and it is found that cell sizes less than or equal to 1.67 μm and sufficiently small time steps yield Αstatistically equivalent microstructures. Finally, a potential use case for this CA approach—the layer-wise convergence in grain structure starting from extremes in initial grain size—is examined. This approach's ability to simulate expected trends in nucleation and epitaxial grain growth for large regions of microstructure, simulated independently of heat transport models themselves, should prove useful for investigation of various microstructure uncertainties and prediction of part-scale experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Full spectrum optical constant interface to the Materials Project

Optical constants characterize the interaction of materials with light and are important properties in material design. Here we present a Python-based Corvus workflow for simulations of full spectrum optical constants from the visible and ultraviolet to hard x-ray wavelengths based on the real-space Green’s function code FEFF10 and structural data from the Materials Project (MP). The Corvus workflow manager and its associated tools provide an interface to FEFF10 and the MP database. The workflow parallelizes the FEFF computations of optical constants over all absorption edges for each material in the MP database specified by a unique MP-ID. The workflow tools determine the distribution of computational resources needed for that case. Similarly, the optical constants for selected sets of materials can be computed in a single-shot. Additionally, to illustrate the approach, we present results for several elemental solids in the periodic table, as well as a sample compound, and compare our predictions with experimental results. In addition, we provide a database of calculated results for all elements for which there is a stable elemental solid at standard conditions available in the Materials Project database. As in x-ray absorption spectra, these results are interpreted in terms of an atomic-like background and fine-structure contributions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Probing disorder in pyrochlore oxides using in situ synchrotron diffraction from levitated solids–A thermodynamic perspective

Abstract Pyrochlore, an ordered derivative of the defect fluorite structure, shows complex disordering behavior as a function of composition, temperature, pressure, and radiation damage. We propose a thermodynamic model to calculate the disordering enthalpies for several RE 2 Zr 2 O 7 (RE = Sm, Eu, Gd) pyrochlores from experimental site distribution data obtained by in situ high temperature synchrotron X-ray diffraction. Site occupancies show a gradual increase in disorder on both cation and anion sublattices with increasing temperature and even greater disorder is achieved close to the phase transition to defect fluorite. The enthalpy associated with cation disorder depends on the radius of the rare earth ion, while the enthalpy of oxygen disordering is relatively constant for different compositions. The experimental data support trends predicted by ab initio calculations, but the obtained enthalpies of disordering are less endothermic than the predicted values. Thermal expansion coefficients are in the range (8.6–10.8) × 10 −6 K −1 . These new experimental determinations of defect formation energies are important for understanding the stability of pyrochlore oxides and their disordering mechanisms, which are essential in the context of their potential applications in nuclear waste management and other technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exploiting dissipative reactions to perform in-beam γ-ray spectroscopy of the neutron-deficient isotopes 38,39 Ca

The neutron-deficient Ca isotopes continue to attract attention due to their importance for testing isospin symmetry and their relevance in capture reactions of interest for nova nucleosynthesis and the shape of light curves in Type I x-ray bursts. To date, spectroscopic information on 38,39 Ca is largely limited to data on lower-spin excited states. Here, we report in-beam γ-ray spectroscopy of complementary higher-spin, complex-structure states in 39 Ca populated in fast-beam-induced, momentum-dissipative processes leading to neutron pickup onto excited configurations of the projectile, 9 Be( 38 Ca*, 39 Ca + γ)X. Such a dissipative reaction was recently characterized for the case of inelastic scattering of 38 Ca off 9 Be, 9 Be( 38 Ca, 38 Ca + γ)X. Additional data and discussion on the nuclear structure of 38 Ca is also presented. Furthermore, an explanation for the more-complex-structure states, populated with small cross sections in one-nucleon knockout reactions, and observed in the tails of their longitudinal momentum distributions, is also offered.

20 ≤ A ≤ 38↗
DOE OSTI2021

Urca Cooling in Neutron Star Crusts and Oceans: Effects of Nuclear Excitations

We report the excited-state structure of atomic nuclei can modify nuclear processes in stellar environments. In this Letter, we study the influence of nuclear excitations on Urca cooling (repeated back-and-forth β decay and electron capture in a pair of nuclear isotopes) in the crust and ocean of neutron stars. We provide for the first time an expression for Urca process neutrino luminosity which accounts for a thermal Boltzmann distribution of excited states in both members of an Urca pair. We use our new formula with state-of-the-art nuclear structure inputs to compute neutrino luminosities of candidate Urca cooling pairs. Our nuclear inputs consist of the latest experimental data supplemented with calculations using the projected shell model. We show that, in contrast to previous results that only consider the ground states of both nuclei in the pair, our calculated neutrino luminosities for different Urca pairs vary sensitively with the environment temperature and can be radically different from those obtained in the one-transition approximation. We find that nuclear excitations can lead to an enhancement in total Urca neutrino luminosities in the accreted neutron star crust by about 5 times as compared with the previous Urca results, which is expected to cause significant observational effects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.

Mehta, Kshitij↗
DOE OSTI2023

Joint Inversion of Surface Electrical Resistivity Tomography and Seismic Refraction Data between the 200 Areas

Geologic stratigraphy on the Hanford Site influences groundwater and contaminant migration through the aquifer system and the vadose zone. The current geologic framework model (GFM) relies heavily on a sparse distribution of borehole data in some locations to map geologic contacts and hydrologic properties in the subsurface. Non-invasive geophysical methods such as electrical resistivity tomography (ERT), transient electromagnetic surveying, and seismic imaging are being used at Hanford to map subsurface structure in areas with limited well observations. This is to develop and mature the capability of geophysical methods to aid in GFM refinement, to identify regions of subsurface complexity, and for optimal well siting. A joint inversion of co-located seismic refraction and ERT data was carried out for data collected on a ~2.3-km profile between the 200 Areas on the Hanford Site. While ERT and seismic refraction images have sensitivity to overlapping physical properties (porosity, moisture content, lithology), the resolution and physics used to acquire each of these datasets are different and therefore information can be different or mutually complementary. Performing a joint inversion provides a reasonable option for a coherent, coupled interpretation for mutually complementary datasets. Between the 200 Areas, there are few boreholes to interpret the geologic framework model, and these data sets were obtained to provide a first line of evidence toward identifying stratigraphic structure. The seismic refraction and ERT data were independently inverted during fiscal year 2022 and broadly showed a two-layer structure with a trough-like feature that is ~1 km wide and upwards of 150 m deep. The depth of the trough feature was greater in the ERT image compared to the seismic image, which indicated a maximum depth of approximately 110 m. The objective of the joint inversion described in this report was to invert the seismic refraction and ERT data together while constraining the ERT image to be structurally similar to the seismic refraction image. The approach was applied using the geophysical inverse modeling program E4D, which has the capability to invert first-arrival times from seismic refraction data and ERT resistances using a “cross-gradient” constraint. The application of cross-gradient constraints with different weights produces ERT models that show a high degree of similarity within the upper 100 m (above ~120 m elevation). None of the ERT models show an improved structural similarity to the seismic result; therefore, it is recommended that further attempts to jointly interpret these models focus on petrophysics and image resolution. Petrophysical measurements of core samples would improve knowledge of what drives the ERT response in this region and, along with downhole geophysical measurements, could be used to “ground truth” the surface-based geophysical results. Image resolution studies would provide insight into which regions of the inverted images are reliable and which regions are poorly constrained.

58 GEOSCIENCES↗
DOE OSTI2017

HIFI Spectroscopy of H{sub 2}O Submillimeter Lines in Nuclei of Actively Star-forming Galaxies

We present a systematic survey of multiple velocity-resolved H{sub 2}O spectra using Herschel/Heterodyne Instrument for the Far Infrared (HIFI) toward nine nearby actively star-forming galaxies. The ground-state and low-excitation lines (E {sub up} ≤ 130 K) show profiles with emission and absorption blended together, while absorption-free medium-excitation lines (130 K ≤ E {sub up} ≤ 350 K) typically display line shapes similar to CO. We analyze the HIFI observation together with archival SPIRE/PACS H{sub 2}O data using a state-of-the-art 3D radiative transfer code that includes the interaction between continuum and line emission. The water excitation models are combined with information on the dust and CO spectral line energy distribution to determine the physical structure of the interstellar medium (ISM). We identify two ISM components that are common to all galaxies: a warm (T{sub dust}∼40--70 K), dense (n(H)∼10{sup 5}--10{sup 6} cm{sup −3}) phase that dominates the emission of medium-excitation H{sub 2}O lines. This gas phase also dominates the far-IR emission and the CO intensities for J{sub up}>8. In addition, a cold (T{sub dust}∼20--30 K), dense (n(H)∼10{sup 4}--10{sup 5} cm{sup −3}), more extended phase is present. It outputs the emission in the low-excitation H{sub 2}O lines and typically also produces the prominent line absorption features. For the two ULIRGs in our sample (Arp 220 and Mrk 231) an even hotter and more compact (R {sub s} ≤ 100 pc) region is present, which is possibly linked to AGN activity. We find that collisions dominate the water excitation in the cold gas and for lines with E{sub up}⩽300 K and E{sub up}⩽800 K in the warm and hot component, respectively. Higher-energy levels are mainly excited by IR pumping.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Brief communication: Decadal changes in topography, surface water and subsurface structure across an Arctic coastal tundra site

Abstract. In ice-rich polygonal tundra, spatiotemporal heterogeneity in ground-ice melt reshapes topography, impacting infrastructure, water and carbon cycles. This study evaluates changes in topography and subsurface structure at a coastal Arctic site by comparing data from two surveys conducted a decade apart. Each survey includes electrical resistivity tomography, active layer thickness, photogrammetry, and topographic data. Results reveal subsidence and decrease in permafrost table elevation with varying intensity and spatial distribution across polygons, alongside diverse thermal-hydrological responses, such as thermokarst pool formation in high-centered-polygons and more even subsidence in flat-centered-polygons. The study also underscores the value and limitations of sporadic surveys.

Bachman, Jonathan A↗