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At least 19 records

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY

Volatiles and Redox Along the East African Rift

Abstract The upper mantle under the Afar Depression in the East African Rift displays some of the slowest seismic wave speeds observed globally. Despite the extreme nature of the geophysical anomaly, lavas that erupted along the East African Rift record modest thermal anomalies. We present measurements of major elements, H 2 O, S, and CO 2 , and Fe 3+ /ΣFe and S 6+ /ΣS in submarine glasses from the Gulf of Aden seafloor spreading center and olivine‐, plagioclase‐, and pyroxene‐hosted melt inclusions from Erta Ale volcano in the Afar Depression. We combine these measurements with literature data to place constraints on the temperature, H 2 O, andfO 2 of the mantle sources of these lavas as well as the initial and final pressures of melting. The Afar mantle plume is C/FOZO/PHEM in isotopic composition, and we suggest that this mantle component is damp, with 852 ± 167 ppm H 2 O, not elevated infO 2 compared to the depleted MORB mantle, and has temperatures of ∼1401–1458°C. This is similar infO 2 and H 2 O to the estimates of C/FOZO/PHEM in other locations. Using the moderate H 2 O contents of the mantle together with the moderate thermal anomaly, we find that melting begins at around 93 km depth and ceases at around 63 km depth under the Afar Depression and at around 37 km depth under the Gulf of Aden, and that ∼1%–29% partial melts of the mantle can be generated under these conditions. We speculate that the presence of melt, and not elevated temperatures or high H 2 O contents, are the cause for the prominent geophysical anomaly observed in this region.

Geochemistry & Geophysics

Neutron capture signatures determined from gadolinium isotope compositions of uranium ore concentrates using MC-ICPMS

We present Gd isotope compositions on 25 well-characterized uranium ore concentrates (UOCs). About half the UOCs have isotope depletions in 157 Gd coupled with anticorrelated excesses in 158 Gd due to neutron capture effects. UOCs from older ore bodies and with higher U contents show larger neutron capture effects than UOCs from younger ore bodies with lower U contents. These Gd data are correlated with their previously measured Sm isotope compositions, and we use the Sm data to estimate the neutron fluence of these samples. In conclusion, this work demonstrates how Gd isotope signatures can be employed as a new tool for nuclear forensics.

Earth Sciences

Experimental Observation of a New Attenuation Mechanism in hcp ‐Metals That May Operate in the Earth's Inner Core

Abstract Seismic observations show the Earth's inner core has significant and unexplained variation in seismic attenuation with position, depth and direction. Interpreting these observations is difficult without knowledge of the visco‐ or anelastic dissipation processes active in iron under inner core conditions. Here, a previously unconsidered attenuation mechanism is observed in zinc, a low pressure analog ofhcp‐iron, during small strain sinusoidal deformation experiments. The experiments were performed in a deformation‐DIA combined with X‐radiography, at seismic frequencies (∼0.003–0.1 Hz), high pressure and temperatures up to ∼80% of melting temperature. Significant dissipation (0.077 ≤ Q −1 (ω) ≤ 0.488) is observed along with frequency dependent softening of zinc's Young's modulus and an extremely small activation energy for creep (⩽7 kJ mol −1 ). In addition, during sinusoidal deformation the original microstructure is replaced by one with a reduced dislocation density and small, uniform, grain size. This combination of behavior collectively reflects a mode of deformation called “internal stress superplasticity”; this deformation mechanism is unique to anisotropic materials and activated by cyclic loading generating large internal stresses. Here we observe a new form of internal stress superplasticity, which we name as “elastic strain mismatch superplasticity.” In it the large stresses are caused by the compressional anisotropy. If this mechanism is also active inhcp‐iron and the Earth's inner‐core it will be a contributor to inner‐core observed seismic attenuation and constrain the maximum inner‐core grain‐size to ≲10 km.

Geochemistry & Geophysics

High Sodium Solubility in Magnesiowüstite in Iron‐Rich Deep Lower Mantle

Abstract (Mg,Fe)O ferropericlase‐magnesiowüstite has been proposed to host the majority of Earth's sodium, but the mechanism and capacity for incorporating the alkali cation remain unclear. In this work, experiments in the laser‐heated diamond anvil cell and first‐principles calculations determine the solubility of sodium and favorability of sodium incorporation in iron‐rich magnesiowüstite relative to (Mg,Fe)SiO 3 bridgmanite. Reaction of Mg/(Mg + Fe) (Mg#) 55 and 28 olivine with NaCl at 33–128 GPa and 1600–3000 K produces iron‐rich magnesiowüstite containing several percent sodium, while iron‐rich bridgmanite contains little to no detectable sodium. In sodium‐saturated magnesiowüstite, sodium number [Na/(Na + Mg + Fe)] is 2–5 atomic percent at pressures below 60 GPa and drastically increases to 10–20 atomic percent at deep lower mantle pressures. For these two compositions, there is no significant dependence of the results on Mg#. Our calculations not only show consistent results with experiments but further indicate that such an increase in solubility and partitioning of Na into magnesiowüstite is driven by the spin transition in iron. These results provide fundamental constraints on the crystal chemistry of sodium at lower‐mantle conditions. If the sodium capacity of (Mg,Fe)O is not strongly dependent on Mg#, (Mg,Fe)O in the lower mantle may have the capacity to store the entire sodium budget of the Earth.

Geochemistry & Geophysics