吉林大学学报(信息科学版) ›› 2022, Vol. 40 ›› Issue (5): 701-709.

• •    下一篇

基于粒子滤波的微地震信号去噪方法

李学贵1a,1b,1c, 高 明1a,1b,1c, 吴润桐2 , 王如意3 , 乾龙1a,1b,1c, 鉴 振1a,1b,1c, 李文森1a,1b,1c, 周英杰1a,1b,1c   

  1. 1. 东北石油大学 a. 计算机与信息技术学院; b. 人工智能能源研究院; c. 黑龙江省石油大数据与智能分析重点实验室, 黑龙江 大庆 163318; 2. 大庆油田采油工程研究院 钻井室, 黑龙江 大庆 163453; 3. 中国石油集团 工程技术研究院有限公司, 北京 102206
  • 收稿日期:2021-12-08 出版日期:2022-10-10 发布日期:2022-10-10
  • 作者简介:李学贵(1982— ), 男, 山东临沂人, 东北石油大学副教授, 博士, 硕士生导师, 主要从事深度学习、 智能物探和大数据 技术等研究, (Tel)86-18944583796(E-mail)lixg82@ 163. com。
  • 基金资助:
    国家自然科学基金区域联合基金资助项目(U21A2019); 中国石油科技创新基金资助项目(2018D-5007-0302); 黑龙江省 博士后基金资助项目(LBH-Z18045); 东北石油大学青年科学基金资助项目(2019QNL-56)

Denoising Method of Microseismic Signal Based on Particle Filter

LI Xuegui1a,1b,1c, GAO Ming1a,1b,1c, WU Runtong2 , WANG Ruyi3 , ZI Qianlong1a,1b,1c, JIAN Zhen1a,1b,1c, LI Wensen1a,1b,1c, ZHOU Yingjie1a,1b,1c   

  1. 1a. School of Computer and Information Technology; 1b. Artificial Intelligence Energy Research Institute; 1c. Heilongjiang Key Laboratory of Big Data and Intelligent Analysis of Petroleum, Northeast Petroleum University, Daqing 163318, China; 2. Drilling Room, Daqing Oilfield Oil Production Engineering Research Institute, Daqing 163453, China; 3. Engineering Technology Research Institute Company Limited, China Petroleum, Beijing 102206, China
  • Received:2021-12-08 Online:2022-10-10 Published:2022-10-10

摘要: 针对微地震信号非高斯、 非线性且信号能量较弱等问题, 提出一种基于粒子滤波的微地震信号去噪 方法。 通过建立微地震信号的状态方程, 提取原始信号的背景噪声, 将其与状态方程之和作为观测方程, 联立 状态方程与观测方程建立状态空间模型, 并通过重要性采样和重采样近似估计后验概率密度, 从而求解去噪后 的微地震信号, 提高微地震信号的去噪效果。 在模拟微地震资料和真实微地震资料中的应用表明, 与传统去噪 方法相比, 该方法处理效果更好, 去除噪声同时保留有效信号, 信噪比得到有效提高, 因此具有良好的应用 前景。

关键词: 微地震, 粒子滤波, 重要性采样, 重采样, 信噪比

Abstract: Aiming at the problems of non Gaussian, non linear and weak signal energy of microseismic signal, a denoising method of microseismic signal based on particle filter is proposed. By establishing the state equation of the microseismic signal, extracting the background noise of the original signal, taking the sum of the state equation and the state equation as the observation equation, establishing the state space model by combining the state equation and the observation equation, and approximately estimating the posterior probability density through importance sampling and resampling, so as to solve the denoisedmicroseismic signal and improve the denoising effect of the microseismic signal. The application in simulated microseismic data and real microseismic data shows that compared to the traditional denoising method, this method has better processing effect, removes the noise and retains the effective signal, and the signal-to-noise ratio is effectively improved. Therefore, it has a good application prospect.

Key words: microseismic, particle filter, sequential importance sampling, resampling, signal to noise ratio

中图分类号: 

  • TP391