Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data

Abstract

Recorded seismic signals are often corrupted by noise. We have developed an automatic noise-attenuation method for single-channel seismic data, based upon high-resolution time-frequency analysis. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time-frequency picture. Noise can be distinguished from the signal and attenuated more easily in this reassigned domain. The threshold level is estimated using a general cross-validation approach that does not rely on any prior knowledge about the noise level. The efficiency of the thresholding has been improved by adding a preprocessing step based on kurtosis measurement and a postprocessing step based on adaptive hard thresholding. The proposed algorithm can either attenuate the noise (either white or colored) and keep the signal or remove the signal and keep the noise. Hence, it can be used in either normal denoising applications or preprocessing in ambient noise studies. We tested the performance of the proposed method on synthetic, microseismic, and earthquake seismograms.