首页|A regression approach for seismic first-break picking

A regression approach for seismic first-break picking

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The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(BiLSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are contin-uous values of FBs rather than the discrete ones of the binary distribution.The picking results of syn-thetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of 9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.

First-break pickingLow signal-to-noise ratioRegressionBiLSTMTraveltimeGeometryNoisy seismic data

Huan Yuan、San-Yi Yuan、Jie Wu、Wen-Jing Sang、Yu-He Zhao

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CNPC Key Laboratory of Geophysical Exploration,China University of Petroleum(Beijing),Beijing 102249,China

Research Institute of Petroleum Exploration & Development-Northwest(NWGI),Key Laboratory of reservoir characterization of CNPC,PetroChina,Lanzhou 730020,Gansu,China

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaStrategic Cooperation Technology Projects of China National Petroleum Corporation(CNPC)China University of Petroleum-BeijingR&D Department of China National Petroleum Corporation

2018YFA070250442174152ZLZX2020-032022DQ0604-01

2024

10.1016/j.petsci.2023.11.028

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(3)