Machine Learning for automatic fracture mapping and rock identification [2024 INV#WO0000000109499]
Description
Machine learning algorithms are being used to improve detection and characterization of fault surface geometries using the spatial patterns of earthquake locations. We have improved our ability to generate long, continuous, fault surface representations and have implemented non-planar machine learning based fitting approaches.
Detailed example
The outputs are 3D fault models that are meshed at the user's specified resolution, fault model quality metrics, and a fully 3D render of the fault surfaces.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Improved fault geometries that advance our ability to accurately model earthquake hazards.
Controls / human review
ATO: No; PIA: Not published
Data needed
We used publicly available earthquake catalogs from northern California, specialized high-resolution aftershock catalogs, an existing fault models to evaluate model performance.