Directional complex-valued coherence attributes for discontinuous edge detection

Wang, Shangxu; Yuan, Sanyi; Yan, Binpeng; He, Yanxiao; Sun, Wenju

doi:10.1016/j.jappgeo.2016.03.016

Cited by 39 publications

(9 citation statements)

References 14 publications

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“…In seismology, cross-correlation is often used to evaluate the coherencies between waveforms from different stations (Wang et al 2016). It has been widely used to estimate signal delay times (VanDecar & Crosson 1990) and in seismic interferometry (Halliday & Curtis 2008;Wapenaar et al 2011).…”

Section: Multichannel Coherency Migrationmentioning

confidence: 99%

Automated seismic waveform location using multichannel coherency migration (MCM)–I: theory

Shi

Angus²,

Rost

et al. 2018

Geophysical Journal International

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Section: Multichannel Coherency Migrationmentioning

confidence: 99%

Automated seismic waveform location using multichannel coherency migration (MCM)–I: theory

Shi

Angus²,

Rost

et al. 2018

Geophysical Journal International

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“…, ; Gersztenkorn and Marfurt ; van Bemmel and Pepper ; Cohen and Coifman ; Tingdahl and de Rooij ; Al‐Dossary, Wang and McFarlane ; Di and Gao ; Wang et al . ). However, besides the faults, the discontinuity analysis also highlights some certain non‐faulting features, such as salt domes and diapirs, stratigraphic channels and coherent noises present in seismic data.…”

Section: Introductionmentioning

confidence: 97%

Semi‐automatic fault/fracture interpretation based on seismic geometry analysis

AlRegib

2019

Geophysical Prospecting

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A B S T R A C TFault and fracture interpretation is a fundamental but essential tool for subsurface structure mapping and modelling from 3D seismic data. The existing methods for semi-automatic/automatic fault picking are primarily based on seismic discontinuity analysis that evaluates the lateral changes in seismic waveform and/or amplitude, which is limited by its low resolution on subtle faults and fractures without apparent vertical displacements in seismic images. This study presents an innovative workflow for computer-aided fault/fracture interpretation based on seismic geometry analysis. First, the seismic curvature and flexure attributes are estimated for highlighting both the major faults and the subtle fractures in a seismic volume. Then, fault probability is estimated from the curvature and flexure volumes for differentiation between the potential faults and non-faulting features in the geometric attributes. Finally, the seeded fault picking is implemented for interpreting the target faults and fractures guided by the knowledge of interpreters to avoid misinterpretation and artefacts in the presence of faulting complexities as well as coherent seismic noises. Applications to two 3D seismic volumes from the Netherlands North Sea and the offshore New Zealand demonstrate the added values of the proposed method in imaging and picking the subtle faults and fractures that are often overlooked in the conventional seismic discontinuity analysis and the following fault-interpretation procedures.

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“…For example, as the first edge-detection tool, the coherence attribute (Bahorich and Farmer, 1995) estimates the lateral similarity of seismic waveforms and thereby is effective in depicting the faults and stratigraphic features that obviously break the waveform continuity. Since its popularity, a number of variations and schemes have been developed in improving such attribute (e.g., Luo et al, 1996;Marfurt et al, 1998;Gersztenkorn and Marfurt, 1999;Cohen and Coifman, 2002;Tingdahl and de Rooij, 2005;Di and Gao, 2014;Wang et al, 2016). While clearly highlighting the major faults of apparent displacements, however, most of the edge-detection tools are less efficient for subtle structure interpretation, such as fracture characterization and facies analysis, in which the lateral variation of seismic signals is subtle and beyond the resolution of edge detectors.…”

Section: Introductionmentioning

confidence: 99%

Real-time seismic-image interpretation via deconvolutional neural network

Wang

AlRegib

2018

SEG Technical Program Expanded Abstracts 2018

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Seismic interpretation is now serving as a fundamental tool for depicting subsurface geology and assisting activities in various domains, such as environmental engineering and petroleum exploration. In the past decades, a number of computer-aided tools have been developed for speeding the interpretation process and improving the interpretation accuracy. However, most of the existing interpretation techniques are designed for interpreting a certain seismic feature (e.g., faults and salt domes) in a seismic section or volume at a time; correspondingly, the rest features would be ignored. Full-feature interpretation becomes feasible with the aid of multiple classification techniques. When implemented into the seismic domain, however, the major drawback is the low efficiency particularly for a large dataset, since the classification need to be repeated at every seismic sample. To resolve such limitation, this study proposes implementing the deconvolutional neural network (DCNN) for the purpose of real-time seismic interpretation, so that all the important features in a seismic image can be identified and interpreted both accurately and simultaneously. The performance of the new DCNN tool is verified through application of segmenting the F3 seismic dataset into nine major features, including salt domes, strong reflections, steep dips, etc. Good match is observed between the results and the original seismic signals, indicating not only the capability of the proposed DCNN network in seismic image analysis but also its great potentials for realtime seismic feature interpretation of an entire volume.

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Directional complex-valued coherence attributes for discontinuous edge detection

Cited by 39 publications

References 14 publications

Automated seismic waveform location using multichannel coherency migration (MCM)–I: theory

Automated seismic waveform location using multichannel coherency migration (MCM)–I: theory

Semi‐automatic fault/fracture interpretation based on seismic geometry analysis

Real-time seismic-image interpretation via deconvolutional neural network

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