METHODS article
Front. Earth Sci.
Sec. Solid Earth Geophysics
Volume 13 - 2025 | doi: 10.3389/feart.2025.1561288
This article is part of the Research TopicAdvances and New Methods in Reservoirs Quantitative Characterization Using Seismic DataView all 16 articles
Fault surface construction method based on point cloud surface reconstruction
Provisionally accepted
- China University of Petroleum, Qingdao, China
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Fault surface construction plays an important role in seismic structural interpretation and building structural models. Significant researches have been invested in fault surface extraction in the past few years. In these studies, the main challenges include the following: Some fault samples are locally missing, the noisy and the geological discontinuity features, fault surfaces may form complicated intersections with each other, some adjacent faults with similar directions are difficult to be classified. In this paper, we propose a point cloud surface reconstruction method to automatically construct and classify the fault surfaces from a 3D seismic image so that the fault surfaces are constructed completely and accurately. In this method, we first use the fault-scanning method to smooth the fault attribute image and thin the smoothed fault attribute image. We then pick the seed points from the thinned attribute image as control points and use Random Sample Consensus method to compute optimal surface that pass through these seed points. Finally, we construct the complete fault surfaces by merging all these optimal surface patches and use Moving Least Square to reconstruct the fault surfaces to smooth the fault surfaces and interpolate the possible holes on the fault surfaces. With fitting the fault surfaces by MLS, we can also accurately estimate fault orientations. We demonstrate the efficiency and effectiveness of the method by using model seismic data and open access seismic data that are complicated by intersecting faults and noise.
Keywords: fault scanning method, Fault surface construction, Optimal surface, random sample consensus, Moving least square, point cloud surface reconstruction
Received: 15 Jan 2025; Accepted: 10 Jul 2025.
Copyright: © 2025 Dong, Song, Hu, Zeng and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lin Dong, China University of Petroleum, Qingdao, China
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