Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform
Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform
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DOI：
10.1190/geo2015-0598.1
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发表时间：
2016-07-01
期刊：
影响因子：
3.3
通讯作者：
Horton, Stephen P.
中科院分区：
文献类型：
--
作者：
Mousavi, S. Mostafa;Langston, Charles A.;Horton, Stephen P.
表面阵列记录的典型微震数据具有低信噪比（S/N）和高度非平稳噪声的特点，这使得检测小事件变得困难。目前，在处理之前使用阵列或基于互相关的方法来增强信噪比。我们开发了一种改进信噪比并同时检测微震事件的替代方法。所提出的方法基于同步压缩连续小波变换（SS-CWT）和单通道数据的自定义阈值。 SS-CWT 允许对随时间和频率变化的噪声进行自适应滤波，并比传统小波变换提供更高的分辨率。同时，该算法结合了使用阈值小波系数的检测程序，并将到达检测为特征函数中的局部最大值。使用合成信号和现场微震数据对该算法进行了测试，并将我们的结果与传统的去噪和检测方法进行了比较。该技术可以去除小幅度信号中的大部分噪声并检测事件并估计发生时间。
Typical microseismic data recorded by surface arrays are characterized by low signal-to-noise ratios (S/Ns) and highly nonstationary noise that make it difficult to detect small events. Currently, array or crosscorrelation-based approaches are used to enhance the S/N prior to processing. We have developed an alternative approach for S/N improvement and simultaneous detection of microseismic events. The proposed method is based on the synchrosqueezed continuous wavelet transform (SS-CWT) and custom thresholding of single-channel data. The SS-CWT allows for the adaptive filtering of time-and frequency-varying noise as well as offering an improvement in resolution over the conventional wavelet transform. Simultaneously, the algorithm incorporates a detection procedure that uses the thresholded wavelet coefficients and detects an arrival as a local maxima in a characteristic function. The algorithm was tested using a synthetic signal and field microseismic data, and our results have been compared with conventional denoising and detection methods. This technique can remove a large part of the noise from small-amplitudes signal and detect events as well as estimate onset time.