Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning. Issue 4 (27th April 2021)

• Record Type: Journal Article
• Authors: Mattéo, Lionel; Manighetti, Isabelle; Tarabalka, Yuliya; Gaucel, Jean‐Michel; van den Ende, Martijn; Mercier, Antoine; Tasar, Onur; Girard, Nicolas; Leclerc, Frédérique; Giampetro, Tiziano; Dominguez, Stéphane; Malavieille, Jacques
• Is Part Of: Journal of geophysical research. Volume 126:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-04-27
• Subjects: convolutional neural network -- deep learning -- faults -- mapping -- optical image -- U‐Net; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2020JB021269
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: Faults form dense, complex multi‐scale networks generally featuring a master fault and myriads of smaller‐scale faults and fractures off its trace, often referred to as damage. Quantification of the architecture of these complex networks is critical to understanding fault and earthquake mechanics. Commonly, faults are mapped manually in the field or from optical images and topographic data through the recognition of the specific curvilinear traces they form at the ground surface. However, manual mapping is time‐consuming, which limits our capacity to produce complete representations and measurements of the fault networks. To overcome this problem, we have adopted a machine learning approach, namely a U‐Net Convolutional Neural Network (CNN), to automate the identification and mapping of fractures and faults in optical images and topographic data. Intentionally, we trained the CNN with a moderate amount of manually created fracture and fault maps of low resolution and basic quality, extracted from one type of optical images (standard camera photographs of the ground surface). Based on a number of performance tests, we select the best performing model, MRef, and demonstrate its capacity to predict fractures and faults accurately in image data of various types and resolutions (ground photographs, drone and satellite images and topographic data). MRef exhibits good generalization capacities, making it a viable tool for fast and accurate mapping of fracture and fault networks in image and topographic data. The MRef model can thus be used to analyze fault organization, geometry, and statistics at various scales, key information to understand fault and earthquake mechanics. Key Points: We adapt a U‐Net Convolutional Neural Network to automate fracture and fault mapping in optical images and topographic data We provide a trained model MRef able to identify and map fractures and faults accurately in image data of various types and resolutions We use MRef to analyze fault organization, patterns, densities, orientations and lengths in six fault sites in western USA