An Infrastructure for Computer Aided Discovery in Geoscience

Next-generation Geoscience needs to handle rapidly growing data volumes from ground-based and space-based sensor networks. As real-world phenomena are mapped to data, the scientific discovery process essentially becomes a search process across multidimensional data sets. The extraction of meaningful discoveries from this sea of data therefore requires highly efficient and scalable machine assistance to enhance human contextual understanding. This is necessary both for testing new hypotheses as well as for the detection of novel events and monitoring for natural hazards.

This project develops a computer-aided discovery approach that provides scientists with better support to answer questions such as: What inferences can be drawn from an identified feature? What does a finding mean and how does it fit into the big theoretical picture? Does it contradict or confirm previously established models and findings? How can concepts and ideas be tested effectively? To achieve this, scientists can programmatically express hypothesized Geoscience scenarios, constraints, and model variations. This approach helps delegate the automatic exploration of the combinatorial search space of possible explanations in parallel on a variety of data sets. Furthermore, programmable crawlers can scale the search and discovery of interesting phenomena on cloud-based infrastructures. The computer-aided discovery prototype is evaluated in case studies from Geospace science, including the exploration of structures in space and time using combined GPS, optics, and Geospace radar data.