石油地球物理勘探2026，Vol.61Issue(3)：571-583,13.DOI:10.13810/j.cnki.issn.1000-7210.20250213
基于对称双解码结构的多任务协同初至拾取框架
A multi-task collaborative first-arrival picking framework based on symmetric dual-decoders structure
摘要
Abstract
Data-driven intelligent first-arrival picking methods primarily rely on supervised learning using labeled data.However,a significant disparity exists between the representational capacities of mainstream local-focus la-beling methods and global-segmentation labeling methods.The former emphasizes the learning of local wave-form details of first arrivals,while the latter focuses on capturing global structural features of the wavefield,re-sulting in constrained learning perspectives for traditional single-task models.Consequently,this paper proposes a Multi-task collaborative first-arrival picking framework based on a symmetric dual-decoders structure(MT-SDD picking).First,by leveraging the complementary representational strengths of these two labeling strate-gies,the MT-SDD framework employs a dual-path decoder with a symmetrical configuration and incorporates a feature fusion module that integrates Transformer blocks with a multi-scale convolutional pyramid.Second,through a multi-stage optimization strategy,the framework progressively directs the model in transitioning from learning single-perspective features to multi-perspective feature fusion,ultimately achieving a substantial ad-vancement in both picking accuracy and stability.Finally,comprehensive ablation experiments substantiate the rationale and efficacy of the MT-SDD framework,demonstrating its superior performance in high-precision pick-ing and cross-site generalization capabilities.Validation results based on active seismic data from the Halfmile and Brunswick mining areas in Canada indicate that,in comparison to traditional single-task picking models,the proposed method reduces the average first-arrival picking error by 20%.Accuracy rates across various error tolerances have been consistently enhanced:at error thresholds of 2 ms,4 ms,8 ms,and 10 ms,the accuracy achieved 95.6%,97.8%,98.9%,and 99.2%,respectively.These figures represent improvements of 0.7%,0.3%,0.1%,and 0.1%over the single-task baseline method.关键词
深度学习/初至拾取/多任务/特征融合/监督学习Key words
deep learning/first-arrival picking/multi-task/feature fusion/supervised learning分类
天文与地球科学引用本文复制引用
李含阳,董宏丽,李学贵,李佳慧..基于对称双解码结构的多任务协同初至拾取框架[J].石油地球物理勘探,2026,61(3):571-583,13.基金项目
本项研究受国家自然科学基金区域创新发展联合基金项目"基于分布式算法和大数据驱动的微地震信号去噪与反演研究"(U21A2019)、国家自然科学基金青年科学基金项目"面向不完备数据场景的油气管网故障诊断和异常预警方法研究"(62403119)和黑龙江省自然科学基金项目"中—低成熟度页岩原位加热资源潜力分级评价研究"(LH2024D005)联合资助. （U21A2019）
