石油物探2025，Vol.64Issue(4)：691-700,10.DOI:10.12431/issn.1000-1441.2024.0090
基于多数据融合和自适应加权混合损失函数约束的地震波初至智能拾取方法
An intelligent first-arrival picking method based on multi-data fusion and adaptive weighted hybrid loss function
摘要
Abstract
First-arrival picking is a crucial step in seismic data processing,as its accuracy directly affects velocity model building and static correction.Although conventional CNN-based deep learning methods have achieved remarkable results in first-arrival picking,their performance degrades in a survey with complex surface conditions,e.g.loess tableland,due to the weak energy of first arrivals and strong noises.To address this issue,we propose a deep learning-based first-arrival picking method that integrates multi-data fusion with an adaptive weighted hybrid loss function.To enhance the robustness of the method,seismic,offset,and elevation data are integrated to construct a multi-data fusion model.To enhance the accuracy of first-arrival picking,an adaptive weighting strategy is employed to optimize the combination of multiple loss functions and construct an adaptive weighted hybrid loss function,which effectively constrains the model training process.The tests on three field seismic datasets demonstrate that our method outperforms conventional methods,e.g.STA/LTA and deep semantic segmentation,in picking accuracy and noise robustness in the geologic complexity scenarios with weak first arrivals and strong noises.These results validate the effectiveness and robustness of the proposed method.关键词
初至拾取/卷积神经网络/数据融合/自适应加权混合损失函数Key words
first-arrival picking/convolutional neural network/data fusion/adaptive weighted hybrid loss function分类
能源科技引用本文复制引用
赵军才,马江涛,刘洋,王宁,胡亚东,谭勇..基于多数据融合和自适应加权混合损失函数约束的地震波初至智能拾取方法[J].石油物探,2025,64(4):691-700,10.基金项目
中石化石油工程科技项目(SG-22-47K)资助.This research is financially supported by the Sinopec Petroleum Engineering Technology Project(Grant No.SG-22-47K). （SG-22-47K）
