基于自适应Morlet小波的低信噪比微震信号初至拾取方法研究

First arrival picking method in low SNR micro-seismic signal based on self-adaptive Morlet wavelet

针对影响微震初至拾取的资料信噪比过低的问题,传统方法的拾取精度与稳定性大多不太理想.为了克服低信噪比条件下初至无法有效拾取的缺点,本文设计了一种对目标成分具有高敏感性的自适应Morlet小波基,通过利用该小波基对微震记录进行小波分解,利用三分量数据的有效成分在小波域内具有特征相关性,对三分量小波系数进行主成分分析,提取主成分特征,最终对各级主成分进行加权重构,实现对低信噪比微震信号的初至拾取.在设计有不同信噪比的模型实验与实际资料应用中,该算法均表现出优异的抗噪性能.在极低信噪比条件下,仍能精确指示有效成分的初至.模型实验与实际资料处理结果均验证了本方法对极低信噪比微震资料的初至拾取处理上的有效性与实用性.本方法在微震监测等相关领域中具有较高的理论与应用价值.

Most of micro-seismic signals have low Signal-to-Noise Ratio(SNR)due to the strong background noise,which makes is difficult to locate the first arrival time.Both accuracy and stability of conventional methods is poor in this situation.To overcome this problem,here we proposed a new method based on the adaptive Morlet wavelet and Principal Component Analysis(PCA)process in wavelet coefficients matrix.The 3 components of micro-seismic signal make it possible to extract the features in wavelet coefficients domain.Then the reconstructed signal from weighted features presents an obvious first arrival.Tests on synthetic signals with different SNR show the excellent anti-noise performance.Under the condition of very low SNR,it can still accurately indicate the first arrival time of effective components.Both the model experiment and actual data processing results present that the method proposed in this paper is effective and practical in first arrival picking and has high theoretical and practical value in the related fields such as micro-seismic monitoring.