Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 57, Issue 11, Pages 9138-9155Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2925003
Keywords
Computational modeling; Fault detection; Convolutional neural networks; Feature extraction; Training; Geometry; Convolution; Convolutional neural network (CNN); deep learning; fault; fault dip; fault probability; fault strike; seismic interpretation
Categories
Funding
- NVIDIA Corporation
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We simultaneously estimate fault probabilities, strikes, and dips directly from a seismic image by using a single convolutional neural network (CNN). In this method, we assume a local 3-D fault is a plane defined by a single combination of strike and dip angles. We assume the fault strikes and dips, respectively, are in the ranges of [0, 360) and [64, 85], which are divided into 577 classes corresponding to the situation of no fault and 576 different combinations of strikes and dips. We construct a 7-layer CNN to classify the fault strike and dip in a local seismic cube and obtain the classification probability at the same time. With the fault probability, strike and dip estimated at some seismic pixel, we further compute a fault cube (centered at the pixel) with fault features elongated along the fault plane. By sliding the classification window within a full seismic image, we are able to obtain a lot of overlapping fault cubes which are stacked to compute three full images of enhanced and continuous fault probabilities, strikes, and dips. To train the CNN model, we propose an effective and efficient workflow to automatically create 900 000 synthetic seismic cubes and the corresponding fault class labels. Although trained with only synthetic data sets, our CNN model can be applied to accurately estimate fault probabilities, strikes, and dips within field seismic images that are acquired at totally different surveys. With the estimated three fault images, we further construct fault cells that are represented as small 3-D squares, each square is colored by fault probability and oriented by fault strike and dip. We recursively link the fault cells by following the fault strikes and dips to finally construct fault skins, which are simple linked data structures to represent fault surfaces.
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