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

Acta Geophysica

article

Sun, Xiaofu¹  Li, Yue¹ 

[1] Department of Information, College of Communication Engineering, Jilin University

关键词: Adaboost classifier;    Desert low-frequency noise suppression;    Seismic low-frequency signal detection;    Synchrosqueezing transform;   

DOI  :  10.1007/s11600-020-00408-1

学科分类：地球科学(综合)

来源: Polska Akademia Nauk * Instytut Geofizyki

【 摘 要 】

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 effective 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 classification techniques obtained from supervised machine learning to identify the coefficients 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 coefficients of signal and noise. And then, we train them in the Adaboost classifier. Finally, when the training is completed, we can obtain the final classifier that can effectively separate the signal from noise. We perform tests on synthetic and field records, and the results show great advantages in suppressing random noise as well as retaining effective signal amplitude.

【 授权许可】

Unknown