Novel empirical curvelet denoising strategy for suppressing mixed noise of microseismic data

Abstract

We present a novel denoising strategy based on empirical curvelet transform (ECT) for noisy microseismic data. Our approach can simultaneously suppress high-frequency, low-frequency, and shared-bandwidth noises and preserve detailed information on the noisy microseismic data. Initially, we design a new threshold estimation method by adding a scale factor for ECT threshold denoising. Subsequently, we construct an adaptive parameter model employing the similarity standard deviation for the non-local means (NLM) algorithm. Then, we divide the coefficients obtained from the ECT decomposition into two sets based on the energy spectrum, subjecting each set to improved adaptive thresholding and improved NLM denoising algorithms. Eventually, we reconstruct the denoised signals using the empirical curvelet inverse transform. Our results demonstrate that under a signal-to-noise ratio (SNR) of $-$10 dB, the proposed strategy achieves a correlation coefficient of 0.9524, a root mean square error of 0.198, an SNR of 1.36 dB, and reduces the first arrival picking error to 0.00382 s. Furthermore, application on the real microseismic data further confirms that the proposed method can clarify the corresponding first arrival.