Tracking plant water use, plant-microbe interactions and vulnerability to drought using neutron imaging [Slides]
Abstract not provided.
58 GEOSCIENCES↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Predictive Proxies of Present and Future Lightning in a Superparameterized Model
Abstract A superparameterized climate model is used to assess the global performance of several previously proposed proxies for lightning. In particular, predictors incorporating hydrometeor (ice, graupel) profiles and convective vertical velocities are compared to observations, then used to estimate changes in flash rates with global warming. The choice of microphysics parameterization is also investigated, with all predictors showing higher correlations with Lightning Imaging Sensor/Optical Transient Detector observations when using a 2‐moment scheme compared to a 1‐moment representation. All proxies generally agree in their response to warming over tropical land, with notable decreases in Africa, the Middle East, and northern South America, but disagree over oceans and the midlatitudes. The product of convective available potential energy and precipitation predicts increases over these latter areas, as do the 2‐moment ice‐based proxies, while those of the 1‐moment model tend to show decreases, highlighting the importance of cloud microphysics when using climate models to simulate lightning.
54 ENVIRONMENTAL SCIENCES↗
Mixed imbibition controls the advance of wetting fluid in multiscale geological media
Imbibition plays a central role during multiphase flows in geological media; yet, our understanding of imbibition dynamics across the multiscale features of rocks, including fractures, microcracks, and heterogeneous matrices is limited. Here, we deployed a unique combination of micro- and clinical X-ray CT imaging to probe directly the spatiotemporal evolution of imbibition in a natural, multiscale shale sample. The method enabled unraveling of mixed imbibition behavior, with spontaneous imbibition rates in the matrix pores and microcracks driven by capillarity proceeding as $\propto t^{-1/2}$, whereas pressure-driven flows in the macroscopic fractures yield apparent imbibition rates with $\propto t^{-1}$ scaling. Regardless of the aperture, we showed that spontaneous imbibition proceeds uniformly away from the fractures. The effects of microcracks are further investigated by comparing the dynamics of the imbibition fronts with and without microcracks. Finally, these results highlight the paramount role of microcracks on anomalous front roughening and the need to account for microcracks appropriately in the development of predictive theoretical models.
58 GEOSCIENCES↗
Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation
Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this article, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO 2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO 2 leakage. We validate the performance of our methods using comprehensive numerical tests. Here via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.
58 GEOSCIENCES↗
Improved geophysical monitoring of carbon sequestration through parameter linkage to reservoir modeling
Predictive reservoir modeling, even if present in the form of only basic hydrogeological model assumptions, is expected to accompany the majority of carbon capture and sequestration monitoring activities. It thus represents a source of prior information about the migration of injected fluids that can benefit geophysical survey planning and ensuing monitoring. Constraining the imaging of geophysical monitoring data with reservoir modeling is preferable over standalone geophysical imaging because of additional complementary hydrogeological information. However, fully coupled hydrogeophysical data inversion for flow-modeling parameters that control saturation predictions is an involved process. Within the context of three-dimensional electromagnetic (EM) inversion of data from borehole-to-surface layouts, we employ a "poor people's" alternative. The approach constrains geophysical inversion parameters through saturation predictions. The coupling is realized through spatially variable lower and upper parameter bounds that scale with gas saturation magnitudes, the latter provided by reservoir modeling. In this work, enhancement of three-dimensional time-lapse plume EM imaging is demonstrated for simulated sequestration into a depleted gas reservoir.
58 GEOSCIENCES↗
Adversarial Learning Based Discriminative Domain Adaptation for Geospatial Image Analysis
Not Available
58 GEOSCIENCES↗
The geometry of signal space: a case study of direct mapping between seismic signals and event distribution
SUMMARY Under favourable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal-space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection and scaling, with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning and possible application to studies of repeating events in induced, volcanic and glacial seismicity. Our results place limits on the widely held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.
58 GEOSCIENCES↗
A New Capability of E4D For 3D Parallel Joint Inversion of DC Resistivity And Traveltime Data on Unstructured Mesh
A major challenge in interpreting geophysical data is how to derive consistent three-dimensional (3D) earth models of different physical properties from spatially and temporally limited measurements. Joint inversion with cross-gradient constraints is an approach to find such models by imposing structural similarities between different physical parameters. We have developed a parallel distributed-memory joint inversion code for direct-current (DC) resistivity and traveltime data using the cross-gradient constraint on unstructured mesh. The code utilizes existing E4D framework for parallel forward simulation, distributed storage and computation of the Jacobian matrix of forward operator, and parallel execution of matrix-vector multiplication during inversion. Besides, the joint inversion is solved by nonlinear conjugate gradient algorithm parallelized for DC resistivity and traveltime data. The joint inversion capability of E4D was tested using synthetic data from cross-borehole DC resistivity and traveltime data. The results indicate that the shape and size of the anomalies from the joint inversion are more reliable than those from separate inversions.
58 GEOSCIENCES↗
Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data
High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R 2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R 2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).
47 OTHER INSTRUMENTATION↗
Reconstructing porous media using generative flow networks
One area of intense scientific interest for the study of sandstones, carbonates, and shale at the pore scale is the use of limited image and petrophysical data to generate multiple realizations of a rock’s pore structure. Such images aid efforts to quantify uncertainty in petrophysical properties, including porosity–permeability transforms. We develop and evaluate a deep learning-based method to synthesize porous media volumes using a so-called generative flow model trained on x-ray microscope images of rock texture and pore structure. These models are optimized on a log-likelihood objective and they synthesize large and realistic three-dimensional images. Further, we demonstrate the rapid generation of sandstone image volumes that display realism as gauged by quantitative comparison of topological features using Minkowski functionals of porosity, specific surface area, and the Euler–Poincaré characteristic (i.e., pore connectivity). We also evaluate the single-phase permeability using Navier–Stokes and lattice Boltzmann methods and show that transport properties of the generated samples match measured trends.
58 GEOSCIENCES↗
Grain2mesh: A Python and cubit mesh generator from unprocessed mesoscale images
Predicting bulk behavior from microscale features constitutes a key objective in multiscale modeling research, often involving numerical models composed of finite elements that capture the diversity of constituent phases, shapes, and orientations within the material. The Grain2mesh toolbox allows the user to input unprocessed mesoscopic images for automatic segmentation, pre-processing, quality control, and numerical mesh generation. The numerical mesh generation incorporates Cubit routines to generate robust multi-phase mesh structure for use in computational mechanics solvers. The python classes developed contain detailed documentation and examples to support standard usage and case-specific alternative options.
58 GEOSCIENCES↗
Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification
Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity model, which suppresses noise in the gradients and mitigates local minima. Furthermore, the velocity model generated by neural networks is input to the same partial differential equation (PDE) solvers used in conventional FWI. The gradients of the neural networks and PDEs are calculated using automatic differentiation, which back propagates gradients through the acoustic PDEs and neural network layers to update the weights of the generative neural network. Experiments on 1D velocity models, the Marmousi model, and the 2004 BP model determine that NNFWI can mitigate local minima, especially for imaging high-contrast features such as salt bodies, and it significantly improves the inversion in the presence of noise. Adding dropout layers to the neural network model also allows analyzing the uncertainty of the inversion results through Monte Carlo dropout. NNFWI opens a new pathway to combine deep learning and FWI for exploiting the characteristics of deep neural networks and the high accuracy of PDE solvers. Because NNFWI does not require extra training data and optimization loops, it provides an attractive and straightforward alternative to conventional FWI.
58 GEOSCIENCES↗
Time-Resolved Neutron Imaging for Hydrogen Uptake in Subsurface Lithologies
Geologic hydrogen production and underground storage are increasingly important for meeting rising energy demands while providing clean-combustion advantages. However, hydrogen’s high diffusivity and propensity for leakage through porous media necessitate direct evaluation of its transport behavior in subsurface materials. Whereas X-ray microcomputed tomography (μCT) studies often employ contrast agents or surrogate gases, this study leverages neutron transmission radiography/CT to observe hydrogen migration in situ. This work represents the first demonstration of real-time neutron radiography of hydrogen migration in reservoir and caprock lithologies. Cylindrical cores of Indiana limestone, Amherst Gray sandstone, and Tumey shale were subjected to constant-pressure hydrogen charging and scanned in real time using high-resolution neutron radiography. Results indicate immediate hydrogen infiltration in sandstone and limestone, with homogeneous distribution detected throughout their pore structure. In contrast, hydrogen remained largely absent from fine-grained shale under the same pressure, except in an apparently localized fracture zone, where neutron signatures confirmed the presence of hydrogen. Subsequent neutron CT of the sandstone sample, using image subtraction against an uncharged reference, corroborated hydrogen distribution patterns. Even under lowpressure, single-phase conditions, distinct neutron imaging signatures of hydrogen were achieved. These preliminary findings underscore the potential of neutron imaging for advancing subsurface hydrogen migration research.
08 HYDROGEN↗
RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models
Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale data by generating grayscale image volumes from 2D SEM and dual modality nanoscale shale images. The results show that our method underestimates the porosity and surface area on the binary baseline datasets but is able to generate realistic grayscale image volumes for shales. With improved binary data preprocessing, we believe that our model is capable of generating synthetic porous media volumes for a very broad class of rocks from shale to carbonates to sandstone.
58 GEOSCIENCES↗
Hydraulic fracture mapping using wavelet-based fusion of wave transmission and emission measurements
Fracture characterization is essential to hydrocarbon and geothermal exploration and production as well as carbon geo-sequestration. Active and passive measurements have been extensively applied to map fractures across various scales. Active and passive methods have inherent advantages and limitations that complement each other. Here, in this study, to improve the mapping of embedded fractures and the surrounding geomechanically altered regions, we integrate active shear-wave transmission measurement with passive acoustic-emission measurement collected during a lab-scale hydraulic-fracturing experiment. The proposed data fusion leverages the advantages of the two modalities of measurements, while minimizing their individual limitations. Polarized shear-wave transmission (active) measurement was collected before and after the hydraulic fracturing, and the acoustic emission (passive) measurement was collected during hydraulic fracturing. Two sets of two-dimensional maps of fracture and fracture-induced damage in axial, median and frontal planes were obtained by separately processing active and passive measurements. These 2D maps are then fed to wavelet-based image fusion technique to integrate the two sources of information for the reliable mapping/imaging of the embedded fractures and the surrounding geomechanically altered regions.
58 GEOSCIENCES↗
Fracture Intersections under Stress: Laboratory Data and Code [Data set]
The connectivity of natural and induced fractures governs the injection and withdrawal of fluids from subsurface reservoirs. Connectivity depends on intersections that control how fluids mix and move through the entire system. Here, we present data sets from 3D X-ray microscopy measurements of simple fracture networks under stress. 3D printing was used to create prismatic blocks that formed fracture networks composed of 2 orthogonal fractures. The network orientation was either "x" or "+" relative to an applied vertical stress. 3D data sets were collected for normal loads of 25, 100 and 200 Newtons for samples with fracture surfaces with either correlated or uncorrelated asperity distributions. The file contains data from the 12 samples analyzed along with an example code used to extract the intersection geometry. Additional experimental details can be found in the manuscript "Geologic Stress Modulates Fluid Mixing at Fracture Intersections" (10.1038/s43247-026-03525-9)and supplemental information to appear in Communications Earth & Environment in 2026.
02 PETROLEUM↗
A Benthic Habitat Monitoring Approach for Marine and Hydrokinetic Sites (Final Technical Report)
This final technical report summarizes the work completed as part of the Standardized and Cost-Effective Benthic Habitat Mapping and Monitoring Tools for MHK Environmental Assessments project funded by U.S. Department of Energy (DOE) under contract DE-EE007826 to Integral Consulting Inc. The overall goal of this project was to demonstrate a consistent, repeatable, and semi-automated seafloor and sediment mapping approach for rapidly characterizing benthic physical and biological/habitat conditions to support marine environmental assessments for marine and hydrokinetic energy sites. The approach evaluated combines sediment profile imaging and plan view (SPI/PV) technology with multibeam echosounder surveys as an effective and low-cost benthic habitat mapping protocol. A key innovation was the development of a semi-automated computer vision system that standardizes the extraction of data from the SPI/PV images. Other elements of the project were SPI camera hardware modifications, including the design and fabrication of a prototype “power” SPI camera to improve camera prism penetration in firm substrates, and outreach to agency regulators and other stakeholders on this habitat mapping approach. This technical report consists of five main subsections that summarize: 1) the benthic mapping approach; 2) benthic mapping results from the three areas’ surveys; 3) the development and performance of the image processing algorithms; 4) SPI camera hardware improvements and prototype testing; and 5) the regulatory outreach efforts.
16 TIDAL AND WAVE POWER↗
InversionNet3D: Efficient and Scalable Learning for 3-D Full-Waveform Inversion
Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has shown some encouraging results in obtaining 2-D velocity maps. However, due to high computational complexity and large memory consumption, the reconstruction of 3-D high-resolution velocity maps via deep networks is still a great challenge. Here, in this article, we present InversionNet3D (InvNet3D), an efficient and scalable encoder–decoder network for 3-D FWI. The proposed method employs group convolution in the encoder to establish an effective hierarchy for learning information from multiple sources while cutting down unnecessary parameters and operations at the same time. The introduction of invertible layers further reduces the memory consumption of intermediate features during training and, thus, enables the development of deeper networks with more layers and higher capacity as required by different application scenarios. Experiments on the 3-D Kimberlina dataset demonstrate that InvNet3D achieves state-of-the-art reconstruction performance with lower computational cost and lower memory footprint compared to the baseline.
58 GEOSCIENCES↗