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NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗
NASA NTRS1987

Organic geochemistry of sediments from the continental margin off southern New England, U.S.A.--Part I. Amino acids, carbohydrates and lignin

Total organic carbon (TOC), lignin, amino acids, sugars and amino sugars were measured in recent sediments for the continental margin off southern New England. The various organic carbon fractions decreased in concentration with increasing distance from shore. The fraction of the TOC that was accounted for by these major components also decreased with increasing distance from shore. The concentration of lignin indicated that only about 3-5% of the organic carbon in the nearshore sediment was of terrestrial origin. The various fractions were highly correlated, which was consistent with a simple linear mixing model of shelf organic matter with material form the slope and rise and indicated a significant transport of sediment from the continental shelf to the continental slope and rise.

Non-NASA Center↗
DOE OSTI2023

MODFLOW6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, Southern Nevada

The hydrograph for well ER-5-3-2 in Frenchman Flat, southern Nevada, has previously unexplained water-level fluctuations. Four, three-dimensional, groundwater models (MODFLOW 6) were developed to evaluate potential stresses and hydrologic conditions affecting the well ER-5-3-2 hydrograph. Four model scenarios were developed that simulated: (1) wellbore leakage without recharge, (2) wellbore leakage with recharge, (3) shallow (low transmissivity) and deep (high transmissivity) carbonate rocks, and (4) lateral heterogeneity of carbonate rocks. Input and output files for the four model scenarios are in the model and output directories, respectively. Hydraulic conductivity, specific storage, and wellbore-leakage rates (when simulated) were estimated with parameter estimation (PEST) by minimizing a weighted composite, sum-of-squares objective function. The objective function was informed by measurement and Tikhonov regularization observations. Measurement observations included drawdowns from the constant-rate aquifer test and water-level altitudes measured in well ER-5-3-2 from 2001-2021. Tikhonov regularization informed hydraulic conductivity and specific storage parameters that were insensitive to measurement observations, where homogeneity was the preferred relation. Batch files, executables, and MODFLOW 6, PEST, and post-processing utilities are in the ancillary directory. Supplementary data also are included in the ancillary directory, including site information, high-frequency water-level and aquifer-test data, transmissivity estimates, water-chemistry data, and water-temperature analyses. This USGS data release contains data, analyses, and model files for the simulations and analysis results described in U.S. Geological Survey Scientific Investigations Report (https://doi.org/10.3133/sir20225132).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Recovering the effects of subgrid heterogeneity in simulations of radionuclide transport through fractured media

Groundwater flow and contaminant transport through fractured media can be simulated using Discrete Fracture Network (DFN) models which provide a natural description of structural heterogeneity. However, this approach is computationally expensive, with the large number of intersecting fractures necessitated by many real-world applications requiring modeling simplifications to be made for calculations to be tractable. Upscaling methods commonly used for this purpose can result in some loss of local-scale variability in the groundwater flow velocity field, resulting in underestimation of particle travel times, transport resistance and retention in transport calculations. In this paper, a transport downscaling algorithm to recover the transport effects of heterogeneity is tested on a synthetic Brittle Fault Zone model, motivated by the problem of large safety assessment calculations for geological repositories of spent nuclear fuel. We show that the variability in the local-scale velocity field which is lost by upscaling can be recovered by sampling from a library of DFN transport paths, accurately reproducing DFN transport statistic distributions and radionuclide breakthrough curves in an upscaled model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Crustal structure, geophysical models and contemporary tectonism of the Colorado Plateau

A regional analysis of the crust and upper mantle of the Colorado Plateau is presented, using existing geophysical and geological data combined with new surface wave dispersion and groundwater geothermometry data; the tectonic implications of these models are also investigated. Surface wave and seismic refraction data indicate that the crust of the interior of the Colorado Plateau is 44 + or - 3 km thick, and its crustal structure is typical of stable continental areas. Pn velocities, however, appear to be lower (7.8 km/s) than would be expected in a stable region, while silica geothermometry indicates that the average heat flow for the plateau is 55 mW per sq m (1.3 HFU).

Keller, G. R.↗
NASA NTRS1994

Impact winter and the Cretaceous/Tertiary extinctions: Results of a Chicxulub asteroid impact model

The Chicxulub impact crater in Mexico is the site of the impact purported to have caused mass extinctions at the Cretaceous/Tertiary (K/T) boundary. 2-D hydrocode modeling of the impact, coupled with studies of the impact site geology, indiate that between 0.4 and 7.0 x 10(exp 17) g of sulfur were vaporized by the impact into anhydrite target rocks. A small portion of the sulfur was released as SO3 or SO4, which converted rapidly into H2SO4 aerosol and fell as acid rain. A radiative transfer model, coupled with a model of coagulation indicates that the aerosol prolonged the initial blackout period caused by impact dust only if the aerosol contained impurities. A larger portion of sulfur was released as SO2, which converted to aerosol slowly, due to the rate-limiting oxidation of SO2. Our radiative transfer calculations, combined with rates of acid production, coagulation, and diffusion indicate that solar transmission was reduced to 10-20% of normal for a period of 8-13 yr. This reduction produced a climate forcing (cooling) of -300 W/sq.m, which far exceeded the +8 W/sq.m greenhouse warming, caused by the CO2 released through the vaporization of carbonates, and therefore produced a decade of freezing and near-freezing temperatures. Several decades of moderate warming followed the decade of severe cooling due to the long residence time of CO2. The prolonged impact winter may have been a major cause of the K/T extinctions.

Pope, Kevin O.↗
DOE OSTI2019

A configurational force for adaptive re-meshing of gradient-enhanced poromechanics problems with history-dependent variables

Here, we introduce a mesh-adaption framework that employs a multi-physical configurational force and Lie algebra to capture multiphysical responses of fluid-infiltrating geological materials while maintaining the efficiency of the computational models. To resolve sharp gradients of both displacement and pore pressure, we introduce an energy-estimate-free re-meshing criterion by extending the configurational force theory to consider the energy dissipation due to the fluid diffusion and the gradient-dependent plastic flow. To establish new equilibria after remeshing, the local tensorial history-dependent variables at the integration points are first decomposed into spectral forms. Then, the principal values and directions are projected onto smooth fields interpolated by the basis function of the finite element space via the Lie-algebra mapping. Our numerical results indicate that this Lie algebra operator in general leads to a new trial state closer to the equilibrium than the ones obtained from the tensor component mapping approach. A new configurational force for dissipative fluid-infiltrating porous materials that exhibit gradient-dependent plastic flow is introduced such that the remeshing may accommodate the need to resolve the sharp pressure gradient as well as the strain localization. The predicted responses are found to be not influenced by the mesh size due to the micromorphic regularization, while the adaptive meshing enables us to capture the width of deformation bands without the necessity of employing fine mesh everywhere in the domain.

42 ENGINEERING↗
DOE OSTI2024

Time-lapse seismic inversion for CO 2 saturation with SeisCO2Net: An application to Frio-II site

Seismic monitoring of geological CO 2 storage (GCS) involves highly nonlinear seismic inversion and petrophysical inversion, making it challenging to estimate CO 2 volume efficiently and detect possible early CO 2 leakages. Deep learning (DL) using convolutional neural networks (CNNs) has shown promise in solving highly nonlinear seismic inversion problems. However, direct estimation of CO 2 plume extent/saturation from time-lapse seismic gathers using DL is still underexplored, with no reported field applications to date. The investigation of field data is primarily hindered by scarcity of field data for neural network training. Other obstacles include highly nonlinear seismic-petrophysics inverse relationship, and presence of noise in field seismic data. We introduce SeisCO2Net, a deep CNN that predicts CO 2 saturation maps directly from time-lapse full waveform shot gathers. For training, we use site-specific geological information, fluid flow physics, rock physics, and seismic modeling to generate synthetic datasets that closely resemble the CO 2 storage site. Synthetic tests show promising results, inspiring us to apply SeisCO2Net's trained weights on field data collected at Frio-II GCS site by leveraging transfer learning principles. As reference, we compare SeisCO2Net's predicted CO 2 saturation maps with results obtained from physics-based inversion. Our analyses show both methods display similar CO 2 plume shapes, reasonable CO 2 plume characteristics, and comparable saturation values. Our results suggest pre-training CNNs on physics-informed synthetic datasets and then applying the learned weights to field data is a viable approach to estimating field CO 2 saturation. This method effectively addresses the scarcity of field training data, thus encouraging the feasibility of long-term GCS monitoring.

58 GEOSCIENCES↗
DOE OSTI2023

Linking Stream Chemistry to Subsurface Redox Architecture

As drinking-water scarcity grows worldwide, we need to improve predictions of the quantity and quality of our water resources. An overarching problem for model improvement is that we do not know the geological structure of aquifers in sufficient detail. In this work, we demonstrate that mineral-water reactions imprint structure in the subsurface that impacts the flow and transport of some chemical species. Specifically, pyrite, a ubiquitous mineral, commonly oxidizes and depletes in the upper layers of the weathering profile in most humid watersheds, only remaining at depths of meters. We hypothesize that variations in concentrations (C) of pyrite-derived sulfate released into rivers as a function of discharge (q) reflect the rate-limiting step and depth of pyrite-oxidizing layers. We found that log C - log q behaviors thus differ in small and large watersheds in the Susquehanna River Basin as well as in selected watersheds in the Western United States. Although coal mining changes pyrite oxidation from closed to open system with respect to O 2 , patterns in stream chemistry as a function of discharge are consistent with deep and shallow pyrite oxidation zones in small and large watersheds respectively. Therefore, understanding the subsurface patterns of mineral reactions and how they affect the architecture of aquifers will elucidate patterns of changing river chemistry and our ability to manage water resources in the future under accelerated land use and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

CO2-SCREEN

CO2 Storage prospeCtive Resource Estimation Excel aNalysis (CO2-SCREEN) is a tool which was developed by the Department of Energy’s National Energy Technology Laboratory (DOE-NETL) to screen saline formations for prospective CO2 storage resources. CO2-SCREEN applies U.S. DOE methods and equations for estimating prospective CO2 storage resources for saline formations. This provides a dependable method for calculating prospective CO2 storage resources which allows for consistent comparison of results between different research efforts. CO2-SCREEN latest version (v5.0) is a python application that utilizes a Java front end user interface. The 3.0 version consists of an Excel spreadsheet containing geologic inputs and outputs, linked to a GoldSim Player model that calculates prospective CO2 storage resources via Monte Carlo simulation. https://www.youtube.com/watch?v=lhakk-HYfOI

CO2 Storage Resource,Prospective Storage,Screening↗
DOE OSTI2017

Novel Coupled Thermochronometric and Geochemical Investigation of Blind Geothermal Resources in Fault-Controlled Dilational Corners

Geothermal plays in extensional and transtensional tectonic environments have long been a major target in the exploration of geothermal resources and the Dixie Valley area has served as a classic natural laboratory for this type of geothermal plays. In recent years, the interactions between normal faults and strike-slip faults, acting either as strain relay zones have attracted significant interest in geothermal exploration as they commonly result in fault-controlled dilational corners with enhanced fracture permeability and thus have the potential to host blind geothermal prospects. Structural ambiguity, complications in fault linkage, etc. often make the selection for geothermal exploration drilling targets complicated and risky. Though simplistic, the three main ingredients of a viable utility-grade geothermal resource are heat, fluids, and permeability. Our new geological mapping and fault kinematic analysis derived a structural model suggest a two-stage structural evolution with (a) middle Miocene N -S trending normal faults (faults cutting across the modern range), - and tiling Olio-Miocene volcanic and sedimentary sequences (similar in style to East Range and S Stillwater Range). NE-trending range-front normal faulting initiated during the Pliocene and are both truncating N-S trending normal faults and reactivating some former normal faults in a right-lateral fashion. Thus the two main fundamental differences to previous structural models are (1) N-S trending faults are pre-existing middle Miocene normal faults and (2) these faults are reactivated in a right-later fashion (NOT left-lateral) and kinematically linked to the younger NE-trending range-bounding normal faults (Pliocene in age). More importantly, this study provides the first constraints on transient fluid flow through the novel application of apatite (U-Th)/He (AHe) and 4 He/ 3 He thermochronometry in the geothermally active Dixie Valley area in Nevada.

(U-Th)/He↗
DOE OSTI2017

Test to Extract Soil Properties Using the Seismic HammerTM Active Seismic Source

Geologic material properties are necessary parameters for ground motion modeling and are difficult and expensive to obtain via traditional methods. Alternative methods to estimate soil properties require a measurement of the ground's response to a force. A possible method of obtaining these measurements is active-source seismic surveys, but measurements of the ground response at the source must also be available. The potential of seismic sources to obtain soil properties is limited, however, by the repeatability of the source. Explosives, and hammer surveys are not repeatable because of variable ground coupling or swing strength. On the other hand, the Seismic Hammer TM (SH) is consistent in the amount of energy it inputs into the ground. In addition, it leaves large physical depressions as a result of ground compaction. The volume of ground compaction varies by location. Here, we hypothesize that physical depressions left in the earth by the SH correlate to energy recorded by nearby geophones, and therefore are a measurement of soil physical properties. Using measurements of the volume of shot holes, we compare the spatial distribution of the volume of ground compacted between the different shot locations. We then examine energy recorded by the nearest 50 geophones and compare the change in amplitude across hits at the same location. Finally, we use the percent difference between the energy recorded by the first and later hits at a location to test for a correlation to the volume of the shot depressions. We find that: * Ground compaction at the shot-depression does cluster geographically, but does not correlate to known surface features. * Energy recorded by nearby geophones reflects ground refusal after several hits. * There is no correlation to shot volume and changes in energy at particular shot locations. Deeper material properties (i.e. below the depth of surface compaction) may be contributing to the changes in energy propagation. * Without further processing of the data, shot-depression volumes are insufficient to understanding ground response to the SH. Without an accurate understanding of the ground response, we cannot extract material properties in conjunction with the SH survey. Additional processing including picking direct arrivals and static corrections may yield positive results.

58 GEOSCIENCES↗
DOE OSTI2019

Yearly report for the period Jan. 2018 – Jan. 2019 IC Project: w17_unedetection - “Simulation of underground nuclear explosions using the combined finite discrete element method” and “Fracture Formation and Permeability Evolution at in situ Pressure, Temperature and Stress Conditions”

Understanding the underlying physical processes associated with an underground nuclear explosion (UNE) can help with detection and identification of a suspected event. The Underground Nuclear Explosion Signatures Experiment (UNESE) is a collaborative effort between national laboratories aimed towards improving our understanding of physical and radiochemical signatures associated with a UNE. One primary objective of this effort is to perform hydrodynamic modeling of a UNE, with the purpose of estimating the extent of damage caused by such an event. Estimating the damage associated with an event is important for: a) detection of possible physical signatures at the surface of the earth such as fractures, spallation, and cratering and (b) damage to subsurface structures such as tunnels. Additionally, the damage produced by the hydrodynamic models play can be used to inform subsurface flow and transport models. The Combined Finite-Discrete Element Method (FDEM), implemented as an in-house code known as HOSS (Hybrid Optimization Software Suite), is used to model the damage associated with a UNE in various geological settings.

42 ENGINEERING↗
DOE OSTI2021

A New Approach to Predict Hydrogeological Parameters Using Shear Waves from the Multichannel Analysis of Surface Waves Method

For near-surface contaminant characterization, the accurate prediction of hydrogeological parameters in anisotropic and heterogeneous environments has been a challenge since the last decades. However, recent advances in near-surface geophysics have facilitated the use of geophysical data for hydrogeological characterization in the last few years. A pseudo 3-D high resolution P-wave shallow seismic reflection survey was performed at the P Reactor Area, Savannah River Site, South Carolina in order to delineate and predict migration pathways of a large contaminant plume including trichloroethylene. This contaminant plume originates from the northwest section of the reactor facility that is located within the Upper Atlantic Coastal Plain. The data were collected with 40 Hz geophones, an accelerated weight-drop as seismic source and 1 m receiver spacing with near- and far-offsets of 0.5 and 119.5 m, respectively. In such areas with near-surface contaminants, a detailed subsurface characterization of the vadose zone hydraulic parameters is very important. Indeed, an inexpensive method of deriving such parameters by the use of seismic reflection surveys is beneficial, and our approach uses the relationship between seismic velocity and hydrogeological parameters together with empirical observations relating porosity to permeability and hydraulic conductivity. Shear wave velocity ( V s ) profiles were estimated from surface wave dispersion analysis of the seismic reflection data and were subsequently used to derive hydraulic parameters such as porosity, permeability, and hydraulic conductivity. Additional geophysical data including core samples, vertical seismic profiling, surface electrical resistivity tomography, natural gamma and electrical resistivity logs allowed for a robust assessment of the validity and geological significance of the estimated V s and hydrogeological models. The results demonstrate the usefulness of this approach for the upper 15 m of shallow unconsolidated sediments even though the survey design parameters were not optimal for surface wave analysis due to the higher than desired frequency geophones.

Engineering↗
NASA NTRS1987

The interpretation of crustal dynamics data in terms of plate interactions and active tectonics of the Anatolian Plate and surrounding regions in the Middle East

The long term objective of this project is to interpret NASA's Crustal Dynamics measurements (SLR) in the Eastern Mediterranean region in terms of relative plate motions and intraplate deformation. The approach is to combine realistic modeling studies with an analysis of available geophysical and geological observations to provide a framework for interpreting NASA's measurements. This semi-annual report concentrates on recent results regarding the tectonics of Anatolia and surrounding regions from ground based observations. Also briefly reported on is progress made in using GPS measurements to densify SLR observations in the Eastern Mediterranean.

Toksoz, M. Nafi↗
NASA NTRS1990

Outer planets; Proceedings of Symposium 4 and the Topical Meeting of the 27th COSPAR Plenary Meeting, Espoo, Finland, July 18-29, 1988

Various papers on the magnetospheres, atmospheres, satellites, and rings of the outer planets are presented. Individual topics addressed include: thermal plasma in outer planet magnetospheres, neutral gas-plasma interaction in the Io plasma torus, satellite-plasma interactions, comparison of planetary magnetic fields, energization process of trapped particles in outer planets, remote sensing of planetary plasma, comparison of the deep atmospheres of the giant planets, seasonal thermal structure of giant planet atmospheres, CCD imaging of Neptune at methane-band wavelengths, physical parameters for the Uranus atmosphere, Uranus photochemistry, Uranus electroglow production, vibrationally excited H2 in Saturn's upper atmosphere, Titan, origin of outer planet satellite systems, microphysical modeling of Titan's aerosols, tectonics of icy satellites, geological evolution of Ganymede, photometric techniques for atmosphereless solar system bodies, the new rings, narrow rings, and time variability of the Jovian system.

Johnson, T. V.↗
NASA NTRS1991

Integration of optical and microwave images for geologic mapping and resource explorations

Multispectral images in the visible, near-IR, short-wave IR, thermal IR, and microwave regions, obtained during the Geologic Remote Sensing Field Experiment were combined with data from Landsat TM, laboratory, and field spectral measurements, as well as traditional geologic mapping and were used to produce thematic image maps showing details of the surface geology for an area at the north end of Death Valley (California/Nevada). The integrated multispectral images provide new geologic information which can be used in mineral deposit models to provide exploration targets. The data demonstrated the relationship between the surface composition of materials and remote sensing in the visible and infrared.

Kruse, Fred A.↗