PLEASE RSVP SEPARATELY HERE.Collection and analysis of data on the contents and structure of the Earth’s subsurface have been important throughout U.S. history for the identification, development, and stewardship of the nation’s energy, mineral, and water resources, and for monitoring and mitigating potential natural and environmental hazards. However, ownership and development of these datasets are dispersed across private industry, government, academic researchers, and others, each with different use cases and approaches to data collection and processing. The lack of data sharing and common data collection, curation, and analysis standards presents a serious barrier to improved scientific understanding of the subsurface.
Development of advanced data analyses such as machine learning and artificial intelligence further open the opportunities for examining rich subsurface datasets to improve scientific and public understanding of the subsurface and to support development, stewardship, and management of subsurface resources in an economically and environmentally sound way. Our discussion will focus on progress, challenges, and opportunities in machine learning and artificial intelligence applied to subsurface datasets.
