结合CEEMDAN和主成分分析的低信噪比微地震初至信号检测

微地震资料信噪比过低，传统方法的初至拾取精度与稳定性大多不理想。将基于自适应噪声完备经验模态分解（CEEMDAN）与主成分分析（PCA）相结合，有效地实现了低信噪比资料中的初至特征检测。针对低信噪比微地震资料进行CEEMDAN处理后，对各阶本征模态函数（IMF）进行PCA，再对各阶IMF的主成分进行加权重构，同时对次要成分进行压制与剔除，使三分量信号中具有较强一致性的初至信息得以保留。设计多组不同信噪比的测试信号，对方法的可行性进行测试，并最终应用于三分量实测信号。结果表明，该方法在极低信噪比条件下仍可实现对微地震信号初至的有效识别与检测。

A first arrival detection method in low SNR microseismic signals based on CEEMDAN-PCA

In most cases,conventional methods cannot extract precise and stable first arrivals from microseismic signals due to its low signal-to-noise ratio (SNR).To overcome this difficulty,we propose here a new method based on comprehensive ensemble empirical mode decomposition (CEEMDAN) with adoptive noise and principal component analysis (PCA).First PCA of each order intrinsic mode function (IMF) is carried out after CEEMDAN processing.Then the main components of each order of the IMF are reconstructed and the secondary components are suppressed or removed so that the initial information with strong consistency in three-component signals can be retained.Tests on multi-set synthetic data with different SNR and real data for this method were carried out.The test results provide a solid evidence for the feasibility of the proposed method in first arrival extraction from low SNR microseismic signals.