中国石油大学学报（自然科学版）2026，Vol.50Issue(1)：65-75,11.DOI:10.3969/j.issn.1673-5005.2026.01.007
基于融合降噪先验与多尺度特征聚合的微地震震源定位方法
Microseismic source localization method based on denoising prior fusion and multi-scale feature aggregation
摘要
Abstract
With the exponential growth of seismic exploration data,traditional microseismic localization methods struggle to meet the real-time monitoring requirements of hydraulic fracturing operations.Moreover,environmental noise contamination during data acquisition often leads to low signal-to-noise ratios(SNR),which significantly degrade source localization accu-racy.To address these challenges,this study proposes a novel hybrid self-supervised/supervised deep learning framework.First,a convolutional denoising autoencoder(CDAE)is employed for self-supervised pretraining to simultaneously denoise raw seismic data and learn latent waveform features.The encoder component of the CDAE is then repurposed as a feature ex-traction module and cascaded with a fully convolutional localization network to construct an end-to-end joint localization mod-el.Finally,the integrated network is fine-tuned using a limited amount of labeled data to establish a nonlinear mapping from noisy microseismic recordings to spatial source distributions.Validation tests conducted on both linear velocity models and the Marmousi-2 benchmark demonstrate that the proposed framework achieves superior localization accuracy compared with U-Net and other baseline networks,even when trained with minimal labeled data.关键词
微地震监测/降噪先验/特征聚合/震源定位/深度学习Key words
microseismic monitoring/denoising prior/feature aggregation/source localization/deep learning分类
天文与地球科学引用本文复制引用
黄建平,王秋阳,李媛媛,黎国龙,路依霖,李三福,段文胜,雷刚林..基于融合降噪先验与多尺度特征聚合的微地震震源定位方法[J].中国石油大学学报（自然科学版）,2026,50(1):65-75,11.基金项目
国家自然科学基金面上项目(42374164) （42374164）
山东省泰山学者特聘专家项目(tstp20230615) （tstp20230615）
中国海油湛江分公司科研项目(202418018212) （202418018212）
中国石油塔里木油田分公司科研项目(671024115010) （671024115010）
中国石油长庆油田分公司科研项目(2024D2ZZ01) （2024D2ZZ01）
中国石化胜利油田分公司科研项目(30200020-24-ZC0613-0044) （30200020-24-ZC0613-0044）
