Semiautomatic fault-surface generation and interpretation using topological metrics

Author:

Lou Yihuai1ORCID,Zhang Bo1ORCID,Yong Pan2,Fang Huijing3ORCID,Zhang Yijiang4ORCID,Cao Danping5ORCID

Affiliation:

1. The University of Alabama, Department of Geological Science, Tuscaloosa, Alabama 35487, USA.(corresponding author).

2. Bohai Oilfield Research Institute, Tianjin Branch of CNOOC Ltd, Tianjing 300452, China..

3. The University of Alabama, Department of Geological Science, Tuscaloosa, Alabama 35487, USA and China University of Petroleum (Beijing), College of Geoscience, Changping-Qu, Beijing 102249, China..

4. The University of Alabama, Department of Geological Science, Tuscaloosa, Alabama 35487, USA and Chengdu University of Technology, College of Geophysics, Chengdu 610059, China..

5. China University of Petroleum (East China), School of Geoscience, Qingdao 266580, China..

Abstract

Seismic fault surfaces are compulsory input for structure modeling that unravels the structural deformation history of the subsurface. Seismic fault attributes provide geoscientists with alternative images of faults. However, seismic fault attributes only highlight possible fault locations and do not directly provide fault surfaces that are required inputs for structural modeling. Interpreters construct seismic fault surfaces using interpreted seismic fault sticks on vertical seismic slices. Interpreting fault sticks on hundreds of seismic slices is time consuming. We have semiautomatically constructed fault surfaces by simulating the procedure of manual seismic fault interpretation. Our algorithm consists of three main steps: (1) obtaining fault sticks in the inline, crossline, and time slices; (2) grouping the fault sticks according to the connectivity and mutual exclusion (topology) between the fault sticks on the inline, crossline, and time slices; and (3) generating the fault surface patches by merging the fault sticks time slice by time slice through the topology analysis. Our algorithm contains one optional step: manually merging the fault patches if needed. We test our algorithm on open access seismic data and our workflow accurately generates fault surfaces for most faults including conjugate faults in the seismic data. Considering that it usually helps to weight the estimation according to the quality of the computed fault attribute, the algorithm computes fault parameters such as fault dip and strike using weighted principal component analysis.

Publisher

Society of Exploration Geophysicists

Subject

Geochemistry and Petrology,Geophysics

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Seismic Fault Recognition Method Based on Region Energy Algorithm;International Journal of Pattern Recognition and Artificial Intelligence;2024-08

2. A computational topology-based method for extracting fault surfaces;Interpretation;2024-06-13

3. Fault Surface Extraction Based on Multirelationship Graph Clustering;IEEE Transactions on Geoscience and Remote Sensing;2024

4. Seismic attribute-assisted seismic fault interpretation;GEOPHYSICS;2023-11-30

5. MTL-FaultNet: Seismic Data Reconstruction Assisted Multitask Deep Learning 3-D Fault Interpretation;IEEE Transactions on Geoscience and Remote Sensing;2023

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