Fault visualization enhancement using ant tracking technique and its application in the Taranaki basin, new Zealand

Zhang, Tianze; Lin, Yani; Liu, Kelly H.; Alhakeem, Aamer; Gao, Stephen S.

doi:10.1190/segam2017-17654531.1

Cited by 4 publications

(1 citation statement)

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“…For the automatic extraction of fault lines and planes, ant tracking [10] can be used to extract fault lines based on curvature [11,12], chaos [13][14][15], and the coherence cube [16,17]. However, many parameters need to be considered for the ant tracking method, and adjustments to these parameters substantially affect the accuracy of fault detection.…”

Section: Introductionmentioning

confidence: 99%

Fault detection by skeleton extraction based on orientation field consistency

Zhong

et al. 2022

PLoS ONE

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A fault detection method using skeleton extraction based on orientation field consistency is proposed to improve the efficiency of fault detection, reduce the influence of transverse nonstructural factors on fault detection, and realize automatic fault extraction. In fingerprint image processing, the consistency of the orientation field reaches a maximum value when all orientations are parallel and takes a smaller value when not all orientations are parallel. The orientation field ceases to be parallel in the presence of a stratigraphic discontinuity, and the consistency of the orientation field in the corresponding region is lower than that in parallel regions. This characteristic can be exploited to extract discontinuous regions from seismic data. Then, binarization and closing operations are used to extract fault areas and increase fault continuity. Finally, a skeleton extraction method based on extracting the longitudinal center point is used to identify the fault lines. Compared with the classical ant tracking method, the proposed method requires the adjustment of fewer parameters, thus simplifying fault identification process to a certain extent. Moreover, the proposed method effectively suppresses transverse discontinuities, highlights the longitudinal fault characteristics, and strengthens fault continuity.

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Section: Introductionmentioning

confidence: 99%

Fault detection by skeleton extraction based on orientation field consistency

Zhong

et al. 2022

PLoS ONE

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A hybrid network for three‐dimensional seismic fault segmentation based on nested residual attention and self‐attention mechanism

Sun,

Jiang,

et al. 2024

Geophysical Prospecting

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Fault detection is a crucial step in seismotectonic interpretation and oil–gas exploration. In recent years, deep learning has gradually proven to be an effective approach for detecting faults. Due to complex geological structures and seismic noise, detection results of such approaches remain unsatisfactory. In this study, we propose a hybrid network (NRA‐SANet) that integrates a self‐attention mechanism into a nested residual attention network for a three‐dimensional seismic fault segmentation task. In NRA‐SANet, the nested residual coding structure is designed to fuse multi‐scale fault features, which can fully mine fine‐grained fault information. The two‐head self‐attention decoding structure is designed to construct long‐distance fault dependencies from different feature representation subspaces, which can enhance the understanding of the model regarding the global fault distribution. In order to suppress the interference of seismic noise, we propose a fault‐attention module and embed it into the model. It utilizes the weighted and the separate‐and‐reconstruct channel strategy to improve the model sensitivity to fault areas. Experiments demonstrate that NRA‐SANet exhibits strong noise robustness, while it can also detect more continuous and more small‐scale faults than other approaches on field seismic data. This study provides a new idea to promote the development of seismic interpretation.

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