气候变化领域集成服务门户(CCPortal): Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning

CCPortal

DOI	10.1029/2020JB021269
	Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning
	Mattéo L.; Manighetti I.; Tarabalka Y.; Gaucel J.-M.; van den Ende M.; Mercier A.; Tasar O.; Girard N.; Leclerc F.; Giampetro T.; Dominguez S.; Malavieille J.
发表日期	2021
ISSN	21699313
卷号	126 期号:4
英文摘要	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. © 2021. The Authors.
英文关键词	convolutional neural network; deep learning; faults; mapping; optical image; U-Net
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187188
作者单位	Université Côte d'Azur, Observatoire de la Côte d'Azur, IRD, CNRS, Géoazur, Sophia Antipolis, France; LuxCarta, Sophia Antipolis, France; Thales Alenia Space, Cannes, France; Université Côte d'Azur, Inria, Sophia Antipolis, France; Université de Montpellier, Géosciences Montpellier, Montpellier, France
推荐引用方式 GB/T 7714	Mattéo L.,Manighetti I.,Tarabalka Y.,et al. Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning[J],2021,126(4).
APA	Mattéo L..,Manighetti I..,Tarabalka Y..,Gaucel J.-M..,van den Ende M..,...&Malavieille J..(2021).Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning.Journal of Geophysical Research: Solid Earth,126(4).
MLA	Mattéo L.,et al."Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning".Journal of Geophysical Research: Solid Earth 126.4(2021).