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DOE OSTI2023

The Kimberlina synthetic multiphysics dataset for CO 2 monitoring investigations

Abstract We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO 2 reservoir model based on a potential CO 2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO 2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO 2 , and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO 2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO 2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.

58 GEOSCIENCES↗

DOE OSTI2024

Deep residual networks for crystallography trained on synthetic data

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Heliostat optical error inspection with polarimetric imaging drone

On a Concentrated Solar Power (CSP) field, optical errors have significant impacts on the collection efficiency of heliostats. Fast, cost-effective, labor-efficient, and non-intrusive autonomous field inspection remains a challenge. Approaches using imaging drone, i.e., Unmanned Aerial Vehicle (UAV) system integrated with high resolution visible imaging sensors, have been developed to address these challenges; however, these approaches are often limited by insufficient imaging contrast. Here, in this study, we report a polarimetry-based method with a polarization imaging system integrated on UAV to enhance imaging contrast for in-situ detection of heliostat mirrors without interrupting field operation. We developed an optical model for skylight polarization pattern to simulate the polarization images of heliostat mirrors and obtained optimized waypoints for polarimetric imaging drone flight path to capture images with enhanced contrast. The polarimetric imaging-based method improved the success rate of edge detections in scenarios which were challenging for mirror edge detection with conventional imaging sensors. We have performed field tests to achieve significantly enhanced heliostat edge detection success rate and investigate the feasibility of integrating polarimetric imaging method with existing imaging-based heliostat inspection methods, i.e., Polarimetric Imaging Heliostat Inspection Method (PIHIM). Our preliminary field test results suggest that the PIHIM hold the promise to enable sufficient imaging contrast for real-time autonomous imaging and detection of heliostat field, thus suitable for non-interruptive fast CSP field inspection during its operation.

CSP Field↗

DOE OSTI2021

Statistical analysis of correlations in the x-ray induced Coulomb explosion of iodopyridine

Coulomb explosion imaging is a promising experimental tool to study individual molecules. Here, in this work simulation data for the x-ray induced Coulomb explosion of 2-iodopyridine is analyzed and the involved fragmentation dynamics are described. It is found that particular final ion momenta show correlations that reflect a collision of two atoms during the explosion. Variations of the proton kinetic energies can be associated with variations in the charge build-up time. Covariances of the forces during the explosion can be utilized to simplify the description of the dynamics in reduced dimensionality using only four collective coordinates.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2024

Time projection chamber for GADGET II

The established Gaseous Detector with Germanium Tagging (GADGET) detection system is used to measure weak, low-energy 𝛽-delayed proton decays. It consists of the Gaseous Proton Detector equipped with a MICROMEGAS (MM) readout to detect protons and other charged particles calorimetrically, surrounded by the Segmented Germanium Array (SeGA) for high-resolution detection of prompt 𝛾 rays. To upgrade GADGET's Proton Detector to operate as a compact time projection chamber (TPC) for the detection, three-dimensional imaging and identification of low-energy 𝛽-delayed single- and multiparticle emissions mainly of interest to astrophysical studies. A new high granularity MM board with 1024 pads has been designed, fabricated, installed, and tested. A high-density data acquisition system based on generic electronics for TPCs (GET) has been installed and optimized to record and process the gas avalanche signals collected on the readout pads. The TPC's performance has been tested using a 220 Rn 𝛼-particle source and cosmic-ray muons. In addition, decay events in the TPC have been simulated by adapting the attpcroot data analysis framework. Furthermore, a novel application of two-dimensional convolutional neural networks for GADGET II event classification is introduced. The optimization of data throughput is also addressed. The GADGET II TPC is capable of detecting and identifying 𝛼 particles as well as measuring their track direction, range, and energy. The extracted energy resolution of the GADGET II TPC using P10 gas is about 5.4% at 6.288 MeV ( 220 Rn 𝛼 events), computed using charge integration. Based on a systematic simulation study, we estimated the detection efficiency of the GADGET II TPC for protons and 𝛼 particles, respectively. It has also been demonstrated that the GADGET II TPC is capable of tracking minimum-ionizing particles (i.e., cosmic-ray muons). From these measurements, the electron drift velocity was measured under typical operating conditions. In addition to being one of the first generation of micropattern gaseous detectors (MPGDs) to utilize a resistive anode applied to low-energy nuclear physics, the GADGET II TPC will also be the first TPC surrounded by a high-efficiency array of high-purity germanium 𝛾-ray detectors. As a result, the TPC of GADGET II has been designed, fabricated, and tested and is ready for operation at the Facility for Rare Isotope Beams for radioactive-beam-line experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Electrical Resistivity Tomography based monitoring of stress perturbations to optimize placement of high-precision strain meters

The Center for Understanding Subsurface Signals and Permeability is a new U.S. Department of Energy Earthshot Center focused on understanding and predicting the long-term evolution of permeability in enhanced geothermal systems. The center will use a highly instrumented testbed within the Sanford Underground Research Facility to conduct field scale experiments that elucidate and test capabilities to simulate geochemical-geomechanical interactions and permeability evolution. Here we demonstrate initial developments using previously collected electrical resistivity tomography (ERT) monitoring data with high-performance multi-physics modelling advancements to inform the optimal location of two new monitoring boreholes. Specifically, ERT monitoring data collected during shear stimulation testing shows marked responses to changes in stress during borehole pressurization. We demonstrate how the same response is being simulated, ultimately to train a machine-learning algorithm to estimate rock properties and enable enhanced prediction of stress and strain responses anticipated during future testing campaigns.

Stress, EGS, CUSSP, 3D Electrical Imaging↗

DOE OSTI2023

Visualizing interfacial collective reaction behaviour of Li–S batteries

Benefiting from high energy density (2,600 Wh kg -1 )) and low cost, lithium-sulfur (Li-S) batteries are considered promising candidates for advanced energy-storage systems. Despite tremendous efforts in suppressing the long-standing shuttle effect of lithium polysulfides, understanding of the interfacial reactions of lithium polysulfides at the nanoscale remains elusive. This is mainly because of the limitations of in situ characterization tools in tracing the liquid-solid conversion of unstable lithium polysulfides at high temporal-spatial resolution. There is an urgent need to understand the coupled phenomena inside Li-S batteries, specifically, the dynamic distribution, aggregation, deposition and dissolution of lithium polysulfides. Here, by using in situ liquid-cell electrochemical transmission electron microscopy, we directly visualized the transformation of lithium polysulfides over electrode surfaces at the atomic scale. Notably, an unexpected gathering-induced collective charge transfer of lithium polysulfides was captured on the nanocluster active-centre-immobilized surface. It further induced an instantaneous deposition of nonequilibrium Li2S nanocrystals from the dense liquid phase of lithium polysulfides. Without mediation of active centres, the reactions followed a classical single-molecule pathway, lithium polysulfides transforming into Li 2 S 2 and Li 2 S step by step. Molecular dynamics simulations indicated that the long-range electrostatic interaction between active centres and lithium polysulfides promoted the formation of a dense phase consisting of Li + and S n 2- (2 < n ≤ 6), and the collective charge transfer in the dense phase was further verified by ab initio molecular dynamics simulations. Finally, the collective interfacial reaction pathway unveils a new transformation mechanism and deepens the fundamental understanding of Li-S batteries.

25 ENERGY STORAGE↗

DOE OSTI2025

Aerial and Processed Model Data Representing As-built Conditions in Coastal Port Arthur, Texas in May 2025

This dataset was collected by the Co-Design Team of the Southeast Texas Urban Integrated Field Lab, a research initiative led by the University of Texas at Austin and funded by the U.S. Department of Energy. The broader project focuses on developing climate-resilient design solutions for the Beaumont–Port Arthur region, with more information available at www.setx-uifl.org. Our team conducted aerial surveys of the Port Arthur coastal neighborhood in May 2025, before the start of construction scheduled for Summer 2026. These pre-construction datasets are designed to facilitate comparative analyses, including pre- and post-construction assessments and simulated inundation scenario evaluations. Aerial images were captured using DroneDeploy autonomous flight systems, with imagery processed through the DroneDeploy engine. All original aerial photographs are provided in JPG format and organized in zipped folders by area. The processed data package includes: 3D surface models Orthomosaics Geospatial and topographic mappings Point clouds For guidance on file contents, structure, and recommended usage, please refer to the included README file.

2D mapping↗

DOE OSTI2020

Simulated X-Ray Radiographic Performance of a Bismuth-Loaded PVT Array

Recent material advancements in organic plastic scintillators enable marked increases in material detection efficiency, light yield, and pulse shape discrimination properties. These advances may resolve significant capability gaps for lowcost, portable, and durable dual-particle imaging (DPI) systems for nuclear safety, security, and safeguard purposes. One such material, a 21% bismuth-loaded polyvinyl toluene (BiPVT), is computationally evaluated as a small, pixelated radiographic array using Monte Carlo N-Particle (MCNP) and Zemax OpticStudio, and it is compared to identical evaluations of EJ-200 and EJ-256 arrays. MCNP software enables estimates of particle interaction and energy deposition, while OpticStudio computes optical light transport within each material. Here, computational estimates of spatial resolution and relative light collection at 370 kVp are found to agree with experimental results for both EJ-200 and EJ-256 arrays, thereby validating predictions of the same for the BiPVT array. As such, for equivalent exposures at 370 kVp, a BiPVT array may provide ~20× the light collection expected from EJ-200 and ~10× that expected from EJ-256. Similar comparisons of estimated light collection are also computed at 150 and 270 kVp, and these results suggest that BiPVT will provide significantly improved performance over EJ-200 and EJ-256 across all energies practical for portable X-ray radiography.

42 ENGINEERING↗

DOE OSTI2022

CodEx: A Modular Framework for Joint Temporal De-Blurring and Tomographic Reconstruction

In many computed tomography (CT) imaging applications, it is important to rapidly collect data from an object that is moving or changing with time. Tomographic acquisition is generally assumed to be step-and-shoot, where the object is rotated to each desired angle, and a view is taken. However, step-and-shoot acquisition is slow and can waste photons, so in practice fly-scanning is done where the object is continuously rotated while collecting data. However, this can result in motion-blurred views and consequently reconstructions with severe motion artifacts. In this paper, we introduce CodEx, a modular framework for joint de-blurring and tomographic reconstruction that can effectively invert the motion blur introduced in sparse view fly-scanning. The method is a synergistic combination of a novel acquisition method with a novel non-convex Bayesian reconstruction algorithm. CodEx works by encoding the acquisition with a known binary code that the reconstruction algorithm then inverts. Using a well chosen binary code to encode the measurements can improve the accuracy of the inversion process. The CodEx reconstruction method uses the alternating direction method of multipliers (ADMM) to split the inverse problem into iterative deblurring and reconstruction sub-problems, making reconstruction practical to implement. Here we present reconstruction results on both simulated and binned experimental data to demonstrate the effectiveness of our method.

42 ENGINEERING↗

DOE OSTI2023

A Comparison of Solidification Structures and Submicroscale Cellular Segregation in Rapidly Solidified Stainless Steels Produced via Two-Piston Splat Quenching and Laser Powder Bed Fusion

Fusion-based additive manufacturing techniques leverage rapid solidification (RS) conditions to create parts with complex geometries, unique microscale/nanoscale morphological features, and elemental segregation. Three custom composition stainless steel alloys with varying chromium equivalence to nickel equivalence ratio (Cr eq /Ni eq ) between 1.53 and 1.95 were processed using laser powder bed fusion (LPBF) and/or two-piston splat quenching (SQ) to produce solidification rates estimated between 0.4 and 0.8 m/s. Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were utilized to collect high-resolution images, electron backscatter diffraction (EBSD) phase identification, and measure cellular segregation. Similar features were observed in both LPBF and SQ samples including phase and microstructure, nanoscale oxide particles, cell size, and segregation behavior. However, dislocation pileup was observed along the cell boundaries only in the LPBF austenite solidified microstructure. Targeted adjustment of the SQ feedstock Cr and Ni concentrations, within the ASTM A240 specification for 316L resulted in no observable impact on the cell size, oxide particle size, or magnitude of segregation. Also, the amount of Ni segregation in the ferrite solidified microstructures did not significantly differ, regardless of Cr/Ni eq or processing technique. Here, SQ is demonstrated as capable of simulating RS rates and microstructures similar to LPBF for use as an alternative screening tool for new RS alloy compositions.

42 ENGINEERING↗

DOE OSTI2021

Ionizing radiation effects in SONOS-based neuromorphic inference accelerators

Here, we evaluate the sensitivity of neuromorphic inference accelerators based on Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects. Data retention statistics were collected for 16 Mbit of 40 nm SONOS digital memory exposed to ionizing radiation from a Co-60 source, showing good retention of the bits up to the maximum dose of 500 krad(Si). Using this data, we formulate a rate-equation-based model for the TID response of trapped charge carriers in the ONO stack, and predict the effect of TID on intermediate device states between ‘program’ and ‘erase’. This model is then used to simulate arrays of low-power, analog SONOS devices that store 8-bit neural network weights and support in situ matrix-vector multiplication. We evaluate the accuracy of the irradiated SONOS-based inference accelerator on two image recognition tasks – CIFAR-10 and the challenging ImageNet dataset – using state-of-the-art convolutional neural networks, such as ResNet-50. We find that across the datasets and neural networks evaluated, the accelerator tolerates a maximum TID between 10 krad(Si) and 100 krad(Si), with deeper networks being more susceptible to accuracy losses due to TID.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Finding Hidden Patterns in High Resolution Wind Flow Model Simulations

Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data collection. Given the large size of data, finding hidden patterns in wind flow model simulations are critical for reducing the dimensionality of the analysis. In this work, we first perform dimension reduction with two autoencoder models: the CNN-based autoencoder (CNN-AE) [1], and hierarchical autoencoder (HIER-AE) [2], and compare their performance with the Principal Component Analysis (PCA). We then investigate the super-resolution of the wind flow data. By training a Generative Adversarial Network (GAN) with 300 epochs, we obtained a trained model with 2× resolution enhancement. We compare the results of GAN with Convolutional Neural Network (CNN), and GAN results show finer structure as expected in the data field images. Also, the kinetic energy spectra comparisons show that GAN outperforms CNN in terms of reproducing the physical properties for high wavenumbers and is critical for analysis where high-wavenumber kinetics play an important role.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Support of Adhesion Mechanisms in Al 2 O 3 Aerosol Deposition Through Laser-Induced Particle Impact Testing

Aerosol deposition (AD) is a kinetic spray process capable of depositing ceramic coatings at room temperature, but AD process development is generally a laborious exploration of a large process parameter space. Here, this paper presents a case study investigating whether laser-induced particle impact testing (LIPIT) could be applied to expedite development of an alumina (Al 2 O 3 ) coating on nickel (Ni): Specifically, whether LIPIT measurements could predict critical velocities of adhesion on Ni and Al 2 O 3 , and the effect of ball milling the Al 2 O 3 powder. Because LIPIT has a diffraction-limited lower bound on imageable particle size, the usefulness of Al 2 O 3 powder agglomerates as a proxy for single particles was additionally studied. Overall, LIPIT measurements and AD sprays agreed that ball milling dramatically improves adhesion. Additionally, LIPIT measurements of critical velocity of adhesion of Al 2 O 3 powder agglomerates on Ni and Al 2 O 3 substrates (150 meters per second [m/s] and 250 m/s, respectively) quantitatively agreed with predictions from a previously published model based on picoindentation and molecular dynamics simulations. Together, these findings support the established hypothesis that Al 2 O 3 adheres via a dislocation-mediated mechanism in AD, that Al 2 O 3 powder agglomerates adhere as individual constituent particles rather than collectively, and that, for this case study, LIPIT measurements were predictive of AD process parameters.

Al2O3↗

DOE OSTI2025

Physics-constrained superresolution diffusion for six-dimensional phase space diagnostics

Adaptive physics-constrained superresolution diffusion is developed for noninvasive virtual diagnostics of the six-dimensional (6D) phase space density of charged particle beams. An adaptive variational autoencoder embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 32 6 pixel 6D tensor representation of the beam's 6D phase space density is generated. Projecting from a 6D tensor generates physically consistent two-dimensional projections. Physics-guided superresolution diffusion transforms low-resolution images of the 6D density to high resolution 256 × 256 pixel images. Unsupervised adaptive latent space tuning enables tracking of time-varying beams without knowledge of time-varying initial conditions. The method is demonstrated with experimental data and multiparticle simulations at the HiRES UED. The general approach is applicable to a wide range of complex dynamic systems evolving in high-dimensional phase space. The method is shown to be robust to distribution shift without retraining. Published by the American Physical Society 2025

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗

DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Monitoring the Morphology of M87* in 2009-2017 with the Event Horizon Telescope

The Event Horizon Telescope (EHT) has recently delivered the first resolved images of M87*, the supermassive black hole in the center of the M87 galaxy. These images were produced using 230 GHz observations performed in 2017 April. Additional observations are required to investigate the persistence of the primary image feature—a ring with azimuthal brightness asymmetry—and to quantify the image variability on event horizon scales. To address this need, we analyze M87* data collected with prototype EHT arrays in 2009, 2011, 2012, and 2013. While these observations do not contain enough information to produce images, they are sufficient to constrain simple geometric models. We develop a modeling approach based on the framework utilized for the 2017 EHT data analysis and validate our procedures using synthetic data. Applying the same approach to the observational data sets, we find the M87* morphology in 2009–2017 to be consistent with a persistent asymmetric ring of ∼40 μas diameter. The position angle of the peak intensity varies in time. In particular, we find a significant difference between the position angle measured in 2013 and 2017. These variations are in broad agreement with predictions of a subset of general relativistic magnetohydrodynamic simulations. We show that quantifying the variability across multiple observational epochs has the potential to constrain the physical properties of the source, such as the accretion state or the black hole spin.

79 ASTRONOMY AND ASTROPHYSICS↗