Denoising of desert seismic signal based on synchrosqueezing transform and Adaboost algorithm

Seismic data in desert area generally have low signal-to-noise ratio (SNR) due to special surface conditions. Desert noise is characterized as low-frequency, non-Gaussian and non-stationary noise, which makes the noise suppression in desert area more challenging by conventional methods. Conventional methods are efective for the signal with high SNR, but in desert seismic signal, the SNR is low and the signal can easily be obliterated in desert noise. In this paper, we propose an approach that operates in synchrosqueezing transform (SST) domain and use classifcation techniques obtained from supervised machine learning to identify the coefcients associated with signal and noise. First of all, we transform the real desert seismic data into time–frequency domain by SST. Secondly, we select features by calculating the SST coefcients of signal and noise. And then, we train them in the Adaboost classifer. Finally, when the training is completed, we can obtain the fnal classifer that can efectively separate the signal from noise. We perform tests on synthetic and feld records, and the results show great advantages in suppressing random noise as well as retaining efective signal amplitude.

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Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)