Characterization of normal fault scarp using convolutional neural network: application to Mexico

Abstract

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)