Machine Learning in Geology

A Pipeline for Automatic Classification of Shear-Sense Indicating Clasts

Presenter: Ari Encarnacion

Presenter Status: Undergraduate student

Academic Year: 20-21

Semester: Spring

Faculty Mentor: Gurman Gill

Department: Computer Science

Funding Source/Sponsor: Other

Other Funding Source/Program: National Science Foundation

Screenshot URL: https://drive.google.com/uc?id=1IVp89dAaQsyXV_CmnycCe0YKOn-FLGqS

Abstract:
We are constructing a machine learning (ML) powered, automated pipeline for classifications and detections of shear-sense indicating clasts in photomicrographs. Classifications include Sinistral (Counter-Clockwise aka CCW) and Dextral (Clockwise aka CW) shearing. Detections refer to the location of clasts in photomicrographs. Current efforts involve improving final classification results, gathering more data, and experimentation with different combinations of object detectors and classifiers. This submission focuses on the current pipeline structure and how detections could improve classification results. Future work includes pipeline assembly and providing user access to the model via an app. This app will employ our pipeline to provide automatic classification & detections to the user. This will provide users with vital data, and feedback on app-generated results will benefit our pipeline.

The video presentation can be found at this unlisted YouTube link:

https://youtu.be/CRlZEe4oNBc