Denoising of the Bouguer Anomaly Data Based on the Discrete Shearlet Transform
Mingming Xu, Yican Wu, Chunmei Yang · Proceedings · 2015
Summary Denoising analysis always plays a crucial role in data processing of the potential fields (i.g., gravity and magnetic fields). Generally, denoising methods are classified into two categories: Space Domain Denoising and Transform Domain Denoising. Shearlet Transform belongs to the multi-scale transform which has some important characteristics such as simpler mathematical algorithm, better directional sensitivity and sparsity, and anisotropy representation. In this paper, we intend to apply a denoising method based on the Discrete Shearlet Transform (DST) to the gravity field data. The proposed method is tested on the combined theoretical model of Bouguer anomaly. Through the comparison with the other denoising methods, the results indicate that the proposed denoising method can not only improve the signal-to-noise ratio (SNR) of field data, but also retain the integrity of the useful signal effectively.