There are many cases within the Geothermal, Nuclear Security, Biosciences, and Oil and Gas/Exploration which require very large amounts of data to be reduced into actionable information by scientists, engineers, and technicians. Luna, partnered with the Colorado School of Mines (Mines) is proposing to develop a computational framework that uses a combination of Artificial intelligence and Machine Learning (AI/ML) to enable rapid and accurate data reduction of large datasets. The objective of this project is to enable the resulting product to apply across multiple disciplines and fields as mentioned previously, with an initial focus on the teams expertise which is the acquisition and interpretation of Distributed Acoustic Sensing (DAS) data acquired using optical fiber primarily from geothermal field mapping and monitoring for seismic activity, performing perimeter security at nuclear power facilities, and monitoring civil structures such as buildings and bridges. During Phase I, Luna will provide Mines existing large DAS data sets, generate additional data sets, and work with Mines to customize the data format for incorporation into the cloud architecture and design and implement cloud-based analysis and visualization tools. Mines will utilize the cloud architecture for implementation of advanced computation models for full waveform inversion of seismic data sets. Distributed acoustic sensing is already utilized in the oil and gas and geothermal markets. The proposed work will improve data management and analysis of the large amount of data made available through distributed acoustic sensing.
