ScarpLearn: an automatic scarp height measurement of normal fault scarps using convolutional neural networks

Geomorphic markers such as displaced surfaces, offset rivers or scarps are witnesses to the neotectonic activity of the faults.The characterization (such as fault detailed surface trace, the scarp height, etc.) of these geomorphological 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 present a proof of concept study for the use of deep learning in morphotectonics, specifically on fault markers. We developed a Bayesian supervised machine learning method using one-dimentional (1D) convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScarpLearn. From a topographic profile, ScarpLearn is able to automatically give the cumulative scarp height with an uncertainty. We have developed two versions: one designed for more generalized cases involving profiles with multiple fault scarp (ScarpLearn), and another specifically trained to handle profiles featuring a single fault scarp (ScarpLearn_1F). We apply ScarpLearn for the characterization of active normal faults in extensional settings such as the Trans-Mexican Volcanic Belt and Malawi Rift system. From those specific case studies, we 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). Our results show that we are able to develop a CNN model that is estimating scarp heights on topographic profiles from 5m resolution digital elevation model. We compared the results obtained with ScarpLearn and other non deep-learning methods. ScarpLearn achieves similar accuracy while being much faster and having smaller uncertainties. We invite readers to use and to extend our study: codes to build the synthetic scarp database and for the CNN model ScarpLearn are available at: https://gricad-gitlab.univ-grenoble-alpes.fr/poussel/scarplearn.