Delineating faults from seismic images is a key step for seismic structural interpretation,
reservoir characterization, and well placement. In conventional methods, faults are …

Seismic structural interpretation involves highlighting and extracting faults and horizons that
are apparent as geometric features in a seismic image. Although seismic image processing …

The challenging task of automatic seismic fault detection recently gained in quality with the
emergence of deep learning techniques. Those methods successfully take advantage of a …

Seismic coherence is of the essence for seismic interpretation as it highlights seismic
discontinuity features caused by the deposition process, reservoir boundaries, tectonic …

With the explosive growth in seismic data acquisition and the successful application of deep
convolutional neural networks (DCNN) to various image processing tasks within …

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 …

We propose an automatic fault interpretation method by using convolutional neural networks
(CNN). In this method, we construct a 7-layer CNN to first estimate fault orientations (dips …

The development and wide application of high-precision seismic exploration technology
puts forward new requirements for fault interpretation. The problems of poor fault continuity …

In seismic interpretation, fault detection is a crucial step that often requires considerable
manual labor and time. The convolutional neural network (CNN) is state-of-the-art deep …

Numerous types of fault attributes have been proposed to detect faults by measuring
reflection continuities or discontinuities. However, these attributes can be sensitive to other …