石油物探2026，Vol.65Issue(3)：506-520,15.DOI:10.12431/issn.1000-1441.2025.0261
基于改进V-Net与自适应难样本挖掘的三维地震断层智能识别
Intelligent identification of 3D seismic faults based on improved V-Net and adaptive hard example mining
摘要
Abstract
Accurate 3D fault identification remains a key technical challenge in seismic interpretation.Traditional manual interpretation methods are inefficient and highly subjective,while existing deep learning-based approaches suffer from incomplete fault continuity characterization and sample imbalance.To address these issues,we propose a 3D seismic fault intelligent recognition method based on an improved V-Net architecture and adaptive hard example mining.The architecture adopts an improved upsampling strategy that combines convolutional channel transformation with nearest-neighbor interpolation to address the checkerboard artifacts introduced by deconvolution-based upsampling in conventional V-Net.This strategy effectively mitigates the interference of artifacts on fault boundary features,improves the recognition accuracy of small-scale faults and fault boundaries,and simultaneously reduces parameter redundancy and computational cost.To tackle the challenges of sparse fault samples and ambiguous boundaries in seismic data,we design an adaptive hard example mining loss function that incorporates geological prior knowledge.This loss function employs a dynamic threshold mechanism to identify hard examples and applies a differentiated weighting strategy to fault regions,enabling the model to more effectively learn fine-scale fault features and alleviate the imbalance caused by the scarcity of fault samples.Additionally,a continuity constraint term implemented via gradient penalty is introduced to ensure the geological plausibility of the predicted fault structures.Experimental results on both synthetic and field seismic data demonstrate that the proposed method achieves superior accuracy,continuity,and generalization compared with the conventional V-Net,with an F1 score of 0.796 2 and a mIoU of 0.7891.This work provides a new technical pathway for automatic 3D seismic fault identification and broadens the application potential of deep learning in geophysical exploration.关键词
三维地震/断层识别/改进V-Net/深度学习/自适应难样本挖掘Key words
3D seismic data/fault identification/improved V-Net/deep learning/adaptive hard example mining分类
能源科技引用本文复制引用
王健伟,严曙梅,盛志超,刘舒,徐升博,徐天吉..基于改进V-Net与自适应难样本挖掘的三维地震断层智能识别[J].石油物探,2026,65(3):506-520,15.基金项目
中国石油化工股份有限公司上海海洋油气分公司科研项目(34000000-24-ZC0613-0067)和中国石油天然气集团有限公司重大科技专项(2023ZZ05)共同资助. This research is financially supported by the Research Project of Sinopec Shanghai Offshore Petroleum Company(Grant No.34000000-24-ZC0613-0067)and CNPC Science and Technology Major Project(Grant No.2023ZZ05). （34000000-24-ZC0613-0067）
