FaultNet: A deep CNN model for 3D automated fault picking

Zhang, Qie; Yusifov, Anar; Joy, Corey; Shi, Yuexi; Wu, Xinming

doi:10.1190/segam2019-3215930.1

Cited by 11 publications

(1 citation statement)

References 9 publications

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“…A few other works have been conducted in oceanography (Bézenac et al., 2019), geodesy (Rouet‐Leduc, Hulbert, McBrearty, & Johnson, 2020), volcanology (Ren, Peltier, et al., 2020), and rock physics (Hulbert, Rouet‐Leduc, Johnson, et al., 2019; Ren, Dorostkar, et al., 2019; Rouet‐Leduc, Hulbert, Lubbers, et al., 2017; Srinivasan et al., 2018; You et al., 2020). However, few works have been conducted so far to address fault issues with machine learning, and all of them have targeted seismic images, that is, transformed vertical images of the Earth interior where actual faults cannot be observed directly (Araya‐Polo et al., 2017; Guitton, 2018; Tingdahl & De Rooij, 2005; Wu and Fomel, 2018; Zhang et al., 2019). As far as we know, there has been no study addressing the identification and mapping of tectonic fractures and faults directly observable in optical images of the Earth ground surface.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning

Mattéo

Manighetti

Tarabalka³

et al. 2021

JGR Solid Earth

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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.

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