石油地球物理勘探2025，Vol.60Issue(2)：中插1,333-340,9.DOI:10.13810/j.cnki.issn.1000-7210.20240053
蒙特卡洛非负字典学习的微地震去噪方法
Monte Carlo non-negative dictionary learning method for microseismic data denoising
摘要
Abstract
Microseismic monitoring is an essential technology in the field of unconventional oil and gas reser-voirs exploration.It has been widely used in hydraulic fracturing fracture monitoring,CO2 storage,and so on.However,the microseismic signal is weak in energy and easy to be polluted by noise.Its low signal-to-noise ra-tio makes it difficult to obtain good results in subsequent processing.Therefore,microseismic data denoising is a highly important processing step.The denoising effect has a key impact on the accuracy of subsequent source location and the reliability of focal mechanism inversion results.In this paper,a Monte Carlo non-negative dic-tionary learning(MCNDL)method is proposed for microseismic data denoising.The Monte Carlo block can obtain the initial dictionary containing relatively many effective signal features in a small amount of time.In the process of dictionary updating,non-negativity constraints are used to ensure the sparsity of data transformation and reduce the solution space,thus reducing the computational cost and improving denoising accuracy.This study evaluates the performance of the proposed method by using both synthetic and real-world microseismic datasets,comparing it with band-pass(BP)filtering,frequency-wavenumber(F-K)filtering,and K-singular value decomposition(KSVD)techniques.The findings highlight the superior denoising effect and efficiency of the proposed approach.关键词
微地震/地震去噪/非负字典学习/蒙特卡洛/高保真度Key words
microseismic/seismic data denoising/non-negative dictionary learning/Monte Carlo/high fidelity分类
天文与地球科学引用本文复制引用
曾阳,白敏,马昭阳,周子翔,杨博,桂志先..蒙特卡洛非负字典学习的微地震去噪方法[J].石油地球物理勘探,2025,60(2):中插1,333-340,9.基金项目
本项研究受国家自然科学基金项目"基于时间域高斯束变换的多震源数据高精度分离与高效偏移方法研究"(42174159)和"基于字典学习的多震源数据高效高精度最小二乘偏移方法研究"(41904110)以及油气资源与勘探技术教育部重点实验室青年创新团队项目"智能驱动的地震资料高分辨率处理方法"(KPI2021-01)联合资助. （42174159）
