Scarplet: Cloud-based Template Matching for Detecting Earthquake-Related Landforms in Large Topographic Datasets

The instrumental record of earthquakes from seismometers and GPS gives a real-time view of earthquakes today, but historic and prehistoric earthquake records are incomplete. One way to expand this archive is to measure attributes of fault scarps and related landforms on Earth’s surface produced by past earthquakes or aseismic motion. This poster presents a Python framework for large-scale, cloud-based image processing of topographic data to identify fault scarps and other landforms.

 Speaker: Robert Sare, Stanford University
 Speaker: George Hilley, Stanford University