滤波器(信号处理)
降噪
反射(计算机编程)
计算机科学
数据集
噪音(视频)
地震学
度量(数据仓库)
还原(数学)
数据质量
地质学
匹配滤波器
数据挖掘
人工智能
计算机视觉
工程类
数学
几何学
图像(数学)
公制(单位)
程序设计语言
运营管理
作者
Yangkang Chen,Alexandros Savvaidis,Yunfeng Chen,Omar M. Saad,Sergey Fomel
出处
期刊:Geophysics
[Society of Exploration Geophysicists]
日期:2023-06-21
卷期号:88 (6): WC13-WC23
标识
DOI:10.1190/geo2023-0020.1
摘要
Distributed acoustic sensing (DAS) enables the recording of earthquake signals at an unprecedented low cost with high durability using fiber optic cables. It is often compromised by relatively lower data quality in single-channel measurements compared with traditional seismic receivers but compensated by high-density recordings from hundreds of channels per earthquake event. The multichannel nature of DAS data sets facilitates the applications of well-developed coherency-based denoising methods arising from reflection seismology for detecting more earthquakes. We first introduce a coherency measure for detecting earthquake signals from DAS data sets. Then, we apply a moving-rank-reduction (MRR) filter to enhance the DAS data quality so as to improve the earthquake detectability. The MRR filter is tailored from a rank-reduction filter that is widely used in processing multichannel reflection seismic data. We find that a simple band-pass or median filter is incapable of revealing weak signals generated from small-magnitude or far-away earthquake events, whereas the MRR filter significantly improves the signal-to-noise ratio that enables the detection of those weak signals. We apply the MRR method and the coherency measure on the San Andreas Fault Observatory at Depth DAS data sets for denoising and earthquake detection. As a result, our framework detects all 31 cataloged events, outperforming the previous detection of 25 events using the same data set.
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