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 …

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 …

Fault detection in a seismic image is a key step of structural interpretation. Structure-oriented
smoothing with edge-preserving removes noise while enhancing seismic structures and …

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 …

Picking horizons from seismic images is a fundamental step that could critically impact
seismic interpretation quality. We have developed an unsupervised approach, waveform …

Most seismic horizon extraction methods are based on seismic local reflection slopes that
locally follow seismic structural features. However, these methods often fail to correctly track …

Frequency-dependent amplitude absorption and phase velocity dispersion are typically
linked by the causality-imposed Kramers–Kronig relations, which inevitably degrade the …

Accurate estimation of volumetric seismic dip is of great significance for subsequent seismic
processing and interpretation works. Recently, with the development of deep learning …

Stacking sequence and, more generally, fibre orientation, are critical parameters in fibrous
composite materials since they govern mechanical performance. This paper presents a …

Horizon extraction and fault detection are essential in seismic interpretation and are closely
related to each other. Most existing methods tend to deal with these two tasks …