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Automatic velocity picking based on optimal key points tracking algorithm

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Picking velocities from semblances manually is laborious and necessitates experience.Although various methods for automatic velocity picking have been developed,there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy.The conventional method of velocity picking from a semblance volume is computationally demanding,highlighting a need for a more efficient strategy.In this study,we introduce a novel method for automatic velocity picking based on multi-object tracking.This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency.First,we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters.These cluster centers embody the maximum likelihood velocities of the main subsurface structures.Second,our proposed method tracks key points within the semblance vol-ume.Kalman filter is adopted to adjust the tracking process,followed by interpolation on these tracked points to construct the final velocity model.Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm.We further compare the performances of the clustering method(CM),the proposed tracking method(TM),and the variational method(VM)on a field dataset from the Gulf of Mexico.The results attest that our method offers su-perior accuracy than CM,achieves comparable accuracy with VM,and benefits from a reduced computational cost.

Velocity pickingMulti-object trackingDensity clusteringKalman filter

Yong-Hao Wang、Wen-Kai Lu、Song-Bai Jin、Yang Li、Yu-Xuan Li、Xiao-Feng Gu

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The Institute for Artificial Intelligence(THUAI),Tsinghua University,Beijing,100084,China

State Key Laboratory of Intelligent Technology and Systems,Tsinghua University,Beijing,100084,China

Beijing National Research Center for Information Science and Technology(BNRist),Beijing,100084,China

The Department of Automation,Tsinghua University,Beijing,100084,China

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国家重点研发计划NSFCNSFCNSFC

2018YFA0702501419741264167411642004101

2024

10.1016/j.petsci.2023.11.019

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(2)