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NASA NTRS2013

Human and Robotic Mission to Small Bodies: Mapping, Planning and Exploration

This study investigates the requirements, performs a gap analysis and makes a set of recommendations for mapping products and exploration tools required to support operations and scientific discovery for near- term and future NASA missions to small bodies. The mapping products and their requirements are based on the analysis of current mission scenarios (rendezvous, docking, and sample return) and recommendations made by the NEA Users Team (NUT) in the framework of human exploration. The mapping products that sat- isfy operational, scienti c, and public outreach goals include topography, images, albedo, gravity, mass, density, subsurface radar, mineralogical and thermal maps. The gap analysis points to a need for incremental generation of mapping products from low (flyby) to high-resolution data needed for anchoring and docking, real-time spatial data processing for hazard avoidance and astronaut or robot localization in low gravity, high dynamic environments, and motivates a standard for coordinate reference systems capable of describing irregular body shapes. Another aspect investigated in this study is the set of requirements and the gap analysis for exploration tools that support visualization and simulation of operational conditions including soil interactions, environment dynamics, and communications coverage. Building robust, usable data sets and visualisation/simulation tools is the best way for mission designers and simulators to make correct decisions for future missions. In the near term, it is the most useful way to begin building capabilities for small body exploration without needing to commit to specific mission architectures.

Neffian, Ara V.↗

NASA NTRS1990

Thermographic imaging of disbonds in laminated structures

The presence of disbonds significantly decreases the integrity of many laminated structures used in aerospace applications. Critical for the assessment of the effect of these disbonds on structural integrity is a determination of the disbond area. Thermography is able to detect local variations in temperature after external heating resulting from subsurface delaminations. One limitation of thermography is that the heat diffuses within the laminates, causing difficulty in determining the area of the disbond from the thermographic image. A technique has been developed for reducing the thermographic image to an image of subsurface delaminations for some laminated structures. The technique clearly delineates regions of disbond and is able to resolve disbonds which are unresolvable in the thermographic image. The limits of this technique for resolving the area and separation of disbonds are investigated using both experimental measurements and numerical calculations.

Winfree, William P.↗

DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2007

Automated Recognition of 3D Features in GPIR Images

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Park, Han↗

DOE OSTI2023

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

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

58 GEOSCIENCES↗

DOE OSTI2020

Electrical monitoring of saline tracers to reveal subsurface flow pathways in a flat ditch-drained field

Time-lapse electrical resistivity imaging (ERI) combined with salt tracers can enhance our understanding of subsurface flow and transport in ditch-drained agroecosystems. In a nearly level field adjacent to a drainage ditch, we installed 192 electrodes in a 72-m2 plot along with five hydrometric stations to enable time-lapse ERI monitoring during storms. Immediately prior to a wintertime rainstorm, we applied 5 kg of KBr to a shallow trench located roughly 11 m upgradient from the ditch. Antecedent conditions prior to the storm were wet, assuring a hydrologically connected state that favored tracer dissolution and movement in the subsurface. We monitored the evolution of the tracer plume with ERI over a seven-day period. Four-dimensional (4D) delineation of precipitation and tracer transport using time-lapse ERI showed that the tracer plume moved preferentially downward through a discontinuity in the argillic horizon within the first 57 hrs. Over the following 79 hrs, the tracer plume exhibited steady, lateral movement towards the drainage ditch within a more permeable sandy horizon situated just below the argillic. A spatial moment analysis of logarithmic changes in conductivity in the sand layer revealed seepage velocities ranging from 0.0027 to 0.0482 m d-1; these values were positively correlated with hydraulic gradients, suggesting laminar flow processes typical of sandy aquifers. Increasing groundwater velocities with hydraulic gradients suggest that solute transfers from groundwater to ditch are likely to be faster in storms, especially during wet periods. Even so, predominantly slow lateral transport within the sandy horizon, even during storms, indicates that critical source areas of pollutant loss are likely within a few meters of the ditch.

Robinson, Judith L.↗

NASA NTRS1993

Using lunar sounder imagery to distinguish surface from subsurface reflectors in lunar highlands areas

We have developed a method using the Apollo 17 Lunar Sounder imagery data which appears capable of filtering out off-nadir surface noise from highland area profiles, so that subsurface features may now be detected in highland areas as well as mare areas. Previously, this had been impossible because the rough topography in the highland areas created noise in the profiles which could not be distinguished from subsurface echoes. The new method is an image processing procedure involving the computerized selection of pixels which represent intermediate echo intensity values, then manually removing those pixels from the profile. Using this technique, a subsurface feature with a horizontal extent of about 150 km, at a calculated depth of approximately 3 km, has been detected beneath the crater Riccioli in the highlands near Oceanus Procellarum. This result shows that the ALSE data contain much useful information that remains to be extracted and used.

Cooper, Bonnie L.↗

NASA NTRS2021

Fit to be Tied: Embracing Tethered Robots for Exploring Extreme Planetary Environments

Tethers are supportive wires that could provide power, communication, and even science instrument capabilities to planetary rovers, landers, and future human settlements. Whether it be power distribution and optical communication connecting robots exploring extreme, undersea and deep subsurface environments, to your home office on Earth, we live, and will continue to live, in a predominantly ‘wired’ world. Accordingly, as humanity continues to push beyond Earth to exploring extreme planetary terrains like caves, cliffs, craters, and crevasses, and moves towards establishing human settlements on the Moon and Mars, we can expect tethers to play a major role. For exploration purposes, tethers are critical to enabling next-generation, science-focused missions to access high-value, resource-limited targets, where exploring rovers or astronauts lack direct communication or access to viable sunlight for solar power. NASA’s Jet Propulsion Laboratory (JPL) is formulating new mission concepts that could deploy robotic assets into some of the Solar System’s harshest and steepest terrain and, in many cases, tethers are the enabling technology. Examples include i) a rappelling rover to explore vertically along lunar pit walls to determine how the Moon formed, ii) tethered rovers to reach ice deposits on Mars and the Moon, iii) large, tether-deployed antenna structures to probe into the subsurface of a planet and/or look skyward to image the highly red-shifted, early universe, iv) probes that descend into the icy layers of Europa and Enceladus in search of subsurface oceans and, potentially, extant life, and v) science instruments that could be suspended on supportive tethers from balloons on Venus to directly image the surface and sample its clouds. This paper will present a survey of the state-of-the-art for tether related exploration of the solar system and chronicle ongoing work at JPL, which is leading to robust tether designs, high-power and communication transmission over multi-km distances, and highly capable, tether-based rover systems.

McGarey, Patrick↗

NASA NTRS2009

Mid-Infrared Reflectance Imaging of Thermal-Barrier Coatings

An apparatus for mid-infrared reflectance imaging has been developed as means of inspecting for subsurface damage in thermal-barrier coatings (TBCs). The apparatus is designed, more specifically, for imaging the progression of buried delamination cracks in plasma-sprayed yttria-stabilized zirconia coatings on turbine-engine components. Progression of TBC delamination occurs by the formation of buried cracks that grow and then link together to produce eventual TBC spallation. The mid-infrared reflectance imaging system described here makes it possible to see delamination progression that is invisible to the unaided eye, and therefore give sufficiently advanced warning before delamination progression adversely affects engine performance and safety. The apparatus (see figure) includes a commercial mid-infrared camera that contains a liquid-nitrogen-cooled focal plane indium antimonide photodetector array, and imaging is restricted by a narrow bandpass centered at wavelength of 4 microns. This narrow wavelength range centered at 4 microns was chosen because (1) it enables avoidance of interfering absorptions by atmospheric OH and CO2 at 3 and 4.25 microns, respectively; and (2) the coating material exhibits maximum transparency in this wavelength range. Delamination contrast is produced in the midinfrared reflectance images because the introduction of cracks into the TBC creates an internal TBC/air-gap interface with a high diffuse reflectivity of 0.81, resulting in substantially higher reflectance of mid-infrared radiation in regions that contain buried delamination cracks. The camera is positioned a short distance (.12 cm) from the specimen. The mid-infrared illumination is generated by a 50-watt silicon carbide source positioned to the side of the mid-infrared camera, and the illumination is collimated and reflected onto the specimen by a 6.35-cm-diameter off-axis paraboloidal mirror. Because the collected images are of a steady-state reflected intensity (in contrast to the transient thermal response observed in infrared thermography), collection times can be lengthened to whatever extent needed to achieve desired signal-to-noise ratios. Each image is digitized, and the resulting data are processed in several steps to obtain a true mid-infrared reflectance image. The raw image includes thermal radiation emitted by the specimen in addition to the desired reflected radiation. The thermal-radiation contribution is eliminated by subtracting the image obtained with the illumination off from the image obtained with the illumination on. A flat-field correction is then made to remove the effects of non-uniformities in the illumination level and pixel-to-pixel variations in sensitivity. This correction is performed by normalizing to an image of a standard object that has a known reflectance at a wavelength of 4 microns. After correction, each pixel value is proportional to the reflectance (at a wavelength of 4-microns) at the corresponding location on the specimen. Mid-infrared reflectance imaging of specimens that were thermally cyc led for different numbers of cycles was performed and demonstrated t hat mid-infrared reflectance imaging was able to monitor the gradual delamination progression that occurs with continued thermal cycling . Reproducible values were obtained for the reflectance associated w ith an attached and fully delaminated TBC, so that intermediate refle ctance values could be interpreted to successfully predict the numbe r of thermal cycles to failure.

Edlridge, Jeffrey I.↗

DOE OSTI2023

Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images

Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.

58 GEOSCIENCES↗

DOE OSTI2020

Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations

Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. This work presents a simple method and workflow for producing a reliable time-series of inverted seismic velocity models. This method is tested using data acquired during a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated from disparate, discretely-acquired datasets. These practical steps aim to aid the use of the seismic refraction tomography method for the long-term monitoring of landslides prone to hydrological destabilization.

Whiteley, J. S.↗

DOE OSTI2022

Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Geophysical Impacts and Spectroscopic Identification of a Hydrous Iron Sulfate on Icy Worlds

Over geologic time-scales, large volumes of exogenic sulfur ions from Io's plasma torus have been supplied to the surface of Europa and Ganymede, which, combined with recent interpretations of orbiter images, dynamical modeling, and surface-subsurface exchange, suggests further sulfur transport into the interior of the icy worlds. These observations motivate mixed-phase spectral modeling for interpreting orbiter spectroscopy data and determination of hydration states of candidate surface materials including hydrous sulfates. In this work, we present a combined experimental and theoretical study of the low temperature and high pressure vibrational spectral signature of the iron-sulfate monohydrate endmember, szomolnokite (FeSO 4 ·H 2 O). By employing synchrotron Fourier-transform infrared spectroscopy (FTIR) in the diamond anvil cell up to 23 GPa and down to 20 K, we explore the extreme range of pressure-temperature domains relevant to icy environments throughout our solar system and beyond. Combined with our density-functional theory quantum-mechanics molecular dynamics results, we demonstrate that experimentally observed infrared features in the O-H stretching region commonly associated with nH 2 O (n > 1) hydration states can be attributed to a pure monohydrate without the need for pressure-induced exsolved ice, other coexisting hydrous iron sulfates, or strong overtone and combination modes. We further discuss the possibility of lateral variations in density and shear properties on icy worlds associated with temperature variations and the high-pressure phases of kieserite group monohydrated sulfates.

Geosciences↗

DOE OSTI2020

The Value of Information From Horizontal Distributed Acoustic Sensing Compared to Multicomponent Geophones Via Machine Learning

Abstract Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. The value of information (VOI) metric is used to objectively quantify and compare the value of two types of seismic receiver data via a machine learning approach. The demonstrated VOI methodology is novel by including spatial models from seismic data and obtaining the information statistics from machine learning. Our two-dimensional numerical experiments compare images created from sparsely spaced (80 m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. We used a three-fold cross validation of a U-Net convolutional neural networks to achieve average classification statistics. The results suggest that when horizontal sources are utilized, geophones and DAS identify reflectors and non-reflectors at roughly the same rate. The average F1 score for horizontal DAS is 0.939 and 0.931 for geophones. For images created from a vertical source, DAS performed marginally better (F1 = 0.919) than geophones (F1 = 0.877). Our transferrable methodology can provide guidance on which acquisition scenarios can improve images of important structures in the subsurface and present an efficient method for obtaining reliability statistics from high-dimensional, spatial data.

Energy & Fuels↗

DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗

NASA NTRS2003

Astrobiology Exploration Strategies for the Mars Polar Regions Using Balloon Platforms

Montgolfiere balloons can provide a unique near-surface platform for an extended traverse over the polar regions of Mars. During the polar summer, such solar powered balloons would remain in the constant sun of the polar summer and could remain airborne for many weeks or even months as the atmospheric circulation would drive the balloons around the polar region many times before the balloon would cross the terminator. Such a platform for scientific measurements could provide in situ sampling of the atmosphere for trace disequilibrium species that might be indicators of present geological or biological activity in this region. It could furthermore provide high resolution imaging, deep electromagnetic (EM) sounding for subsurface stratigraphy and liquid water, and high spatial resolution neutron measurements of subsurface ice. Technologies for robust balloon deployment on entry and controlled encounters with the surface and near subsurface for sample acquisition in otherwise inaccessible regions are presently being studied and developed with support from NASA.

Mahaffy, P. R.↗

NASA NTRS2003

Astrobiology Exploration Strategies for the Mars Polar Regions Using Balloon Platforms

Montgolfiere balloons can provide a unique near-surface platform for an extended traverse over the polar regions of Mars. During the polar summer, such solar powered balloons would remain in the constant sun of the polar summer and could remain airborne for many weeks or even months as the atmospheric circulation would drive the balloons around the polar region many times before the balloon would cross the terminator. Such a platform for scientific measurements could provide in situ sampling of the atmosphere for trace disequilibrium species that might be indicators of present geological or biological activity in this regon. It could furthermore provide high resolution imaging, deep electromagnetic (EM) sounding for subsurface stratigraphy and liquid water, and high spatial resolution neutron measurements of subsurface ice. Technologies for robust balloon deployment on entry and controlled encounters with the surface and near subsurface for sample acquisition in otherwise inaccessible regions are presently being studied and developed with support from NASA.

Mahaffy, P. R.↗

NASA NTRS1997

Practical SQUID Instrument for Nondestructive Testing

We report on the development of a scanning eddy-current imaging system designed to detect deep subsurface flaws in conducting materials. A high transition temperature (high-T c) superconducting quantum interference device (SQUID) magnetometer is employed to provide the required sensitivity at low frequencies, while a combination of small cylindrical high-Tc superconducting and A-metal shields enable the instrument to be scanned in a magnetically noisy environment, rather than the object under test. The shields are arranged to prevent unwanted excitation and ambient noise fields from reaching the SQUID, and to enhance spatial resolution and minimize undesirable edge effects. Thus far, the instrument has successfully detected cracks and pits through 10 layers of aluminum, with a combined thickness of 5 cm at room temperature.

Tralshawala, N.↗