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In this issue of ECS Interface, we highlight some exciting success stories for low-dimensional materials in technologically critical fields. These are areas where emergent properties and processes within quantum-confined low-dimensional materials (and heterostructures) enable novel applications beyond what can be achieved in bulk materials.
Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information about SmartTensors can be found at the SmartTensors Github Repository and the SmartTensors page at LANL.gov. Links to these pages are included in this submission. GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration. GeoThermalCloud.jl includes: - site data - simulation scripts - jupyter notebooks - intermediate results - code outputs - summary figures - readme markdown files GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites: - Brady: geothermal exploration of the Brady geothermal site, Nevada - SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region - GreatBasin: geothermal exploration of the Great Basin region, Nevada Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.
Earthquake magnitudes are widely relied upon measures of earthquake size. Although moment magnitude (M w ) has become the established standard for moderate and large earthquakes, difficulty in reliably measuring seismic moments for small (generally M w <4) earthquakes has meant that magnitudes for these events remain plagued by a patchwork of inconsistent measurement scales. Because of this, magnitudes of small earthquakes and statistics derived from them can be biased. Furthermore, because small earthquakes are much more numerous than large ones, many applications, such as seismic hazard modeling, depend critically on analysis of events characterized by magnitudes other than M w . Therefore, to assess this problem, we apply coda envelope analysis to reliably determine moment magnitudes for a case study of small earthquakes from northern Oklahoma and southern Kansas. Not surprisingly, we find significant differences among M L , m bLg , and M w for M ~2–4 earthquakes examined here. More troublingly, we find that relations designed to convert other magnitudes to M w , which are relied upon for important applications such as seismic hazard analysis, often increase rather than decrease this bias for our dataset. In our case study, we find that converted magnitudes can result in a systematic bias sometimes exceeding 0.5 magnitude units, a difference that typically corresponds to a factor of ~3 in seismicity rate. Moreover, we find a correspondingly large bias in Gutenberg–Richter b-values, controlled primarily by inaccurate magnitude scaling in the conversion relationships. Although this study focuses on a relatively small geographic area, we can expect that similar issues exist with varying severity in other regions. Therefore, magnitudes of small earthquakes and their associated statistics, including seismicity rates and b-values, should be treated with caution.
The overarching aim of the project was to improve our understanding of inflation and its end. The following were the main goals of the project: 1. Develop a novel statistical framework to understand particle production during inflation and reheating in scenarios with many poorly constrained components and calculate potential observational signatures. The goal was to be achieved in the following steps (i) Develop the theoretical framework (ii) Apply the framework to inflation and reheating 2. Develop analytical and numerical tools to explore the end of inflation and the energy transfer from inflation to daughter fields during reheating. The goal was to be achieved in two steps: (i) Characterize the eq. of state at the end of inflation using non-perturbative techniques and lattice simulations. (ii) Develop a novel numerical algorithm for gauge fields in a cosmological setting.
Over a three year period, from 2016-2019, this project demonstrated the scientific benefits of integrating the Titan supercomputer at Oak Ridge Leadership Computing Facility into traditional high throughput grid based distributed computing systems managed by PanDA, the workflow management system used for the execution of all distributed computing applications by the ATLAS experiment at the Large Hadron Collider. PanDA manages millions of batch jobs daily at hundreds of clusters worldwide on request by thousands of physicist users, and processes more than an exabyte of data annually using grid middleware. High levels of operational use of Titan was sustained by PanDA in order to meet the physics goals of ATLAS. The success of this project led to the use of other supercomputers worldwide by ATLAS, and to the adoption of PanDA by other experiments and other scientists. Multiple innovative operational and computer science research goals were achieved supporting the use of supercomputers for scientific domains with large scale distributed data and distributed processing needs.
The project is motivated by the challenges, risks, and costs associated with geothermal exploration and production. Many processes and parameters impacting geothermal conditions are poorly understood. Diverse datasets are available to help characterize subsurface geothermal conditions (public and proprietary; satellite, airborne surveys, vegetation/water sampling, geological, geophysical, etc.). Yet, it is not clear how to properly leverage these datasets for geothermal exploration due to an incomplete understanding of how physical processes impacting subsurface geothermal conditions are represented in these observations. Recent advancements in machine learning (ML) provide great promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the 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. Our goals and work under Phases 1 and 2 (as proposed) of this project address all these needs.
Abstract not provided.
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The U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office (NNSA/NFO) planned to demolish two buildings at the Area 1 Subdock at the Nevada National Security Site (NNSS) in Nye County, Nevada, to meet environmental management mission requirements. A review was conducted under Title 54 United States Code (USC) § 306101 (commonly known as Section 106 of the National Historic Preservation Act) and its implementing regulations, 36 Code of Federal Regulations (CFR) Part 800. As a result, a Memorandum of Agreement (MOA) was developed to mitigate the effects of the building demolitions. Stipulation III.B of the MOA requires an architectural survey of the Area 1 Subdock. Prior to this survey, the Subdock had not been systematically recorded. Therefore, an area of approximately 33 hectares (81 acres) was surveyed for historic properties by Desert Research Institute personnel. This effort resulted in the identification, recording, and evaluation of the potential Area 1 Subdock Historic District (SHPO Resource No. D377), including the identification of its contributing components. This district is recommended as eligible for the National Register of Historic Places (NRHP) under Criteria A and C. It contains 14 primary resources which include individual buildings, structures, storage yards, and infrastructure. Of these resources, all except one are recommended as elements that contribute to the district during its period of significance corresponding to nuclear testing from 1985 through 1992. Four of the resources (B18847, B18848, S2772, S2773) are recommended as individually eligible for the NRHP.
Abstract not provided.
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Explore the source record for details and available documents.