基于EM-KF算法的微地震信号去噪方法
Microseismic Signal Denoising Method Based on EM-KF Algorithm
李学贵 1张帅 2吴钧 3段含旭 2王泽鹏2
作者信息
- 1. 东北石油大学 计算机与信息技术学院,黑龙江大庆 163318;东北石油大学 人工智能能源研究院,黑龙江大庆 163318;东北石油大学 黑龙江省石油大数据与智能分析实验室,黑龙江大庆 163318
- 2. 东北石油大学 计算机与信息技术学院,黑龙江大庆 163318;东北石油大学 黑龙江省石油大数据与智能分析实验室,黑龙江大庆 163318
- 3. 大庆油田有限责任公司勘探开发研究院,黑龙江大庆 163712
- 折叠
摘要
针对微地震信号能量较弱,噪声较强,使微地震弱信号难以提取问题,提出了一种基于EM-KF(Expectation Maximization Kalman Filter)的微地震信号去噪方法.通过建立一个符合微地震信号规律的状态空间模型,并利用EM(Expectation Maximization)算法获取卡尔曼滤波的参数最优解,结合卡尔曼滤波,可以有效地提升微地震信号的信噪比,同时保留有效信号.通过合成和真实数据实验结果表明,与传统的小波滤波和卡尔曼滤波相比,该方法具有更高的效率和更好的精度.
Abstract
Microseismic monitoring technology has been widely used in unconventional oil and gas development.The microseismic signal has weak energy and strong noise,which makes the follow-up work difficult and requires high-precision and accurate data.To solve the problem of extracting weak microseismic signals,an EM-KF(Expectation Maximization Kalman Filter)-based method is proposed for denoising microseismic signals.By establishing a state space model that conforms to the laws of microseismic signals and using the EM(Expectation Maximization)algorithm to obtain the optimal solution of the parameters for the Kalman filter,the signal-to-noise ratio of microseismic signals can be effectively improved while retaining the effective signals.The experimental results of synthetic data and real data show that this method has higher efficiency and better accuracy than traditional wavelet filtering and Kalman filtering.
关键词
微地震/EM算法/卡尔曼滤波/信噪比Key words
microseism/expectation maximization(EM)algorithm/Kalman filter/signal to noise ratio引用本文复制引用
基金项目
国家自然科学基金(U21A2019)
中国石油科技重大专项(2021ZZ10)
黑龙江省揭榜挂帅科技攻关基金(DQYT-2022-JS-750)
黑龙江省自然科学基金联合引导基金(LH2022F008)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据