SNR Enhancement for Downhole Microseismic Data Using CSST

Abstract

Noise contamination is a significant issue in microseismic data processing due to the low magnitude of high-frequency downhole microseismic signals induced during fluid injection. In this letter, a noncoherent noise attenuation technique based on cycle spinning shearlet transform (CSST) is presented. The CSST algorithm is implemented in three steps. In the first step, we forcibly shift signals so that their features change positions and orientations and then transform the noisy data into shearlet domain to obtain coefficients of different scales and directions. In the second stage, we apply hard thresholding to the resulting coefficients of individual component. Finally, we transform them back into the original domain and averagely superimpose the filtering results to preserve the amplitudes of the signals. The resulting methodology is tested on the synthetic and field datasets that were recorded with a vertical array of receivers. The experimental results show that the proposed CSST algorithm has better performance than the conventional threshold-based shearlet transform denoising method in terms of both high-frequency signal preservation and noise attenuation.