Adaptive Threshold Based Shearlet Transform Noise Attenuation Method for Surface Microseismic Data

2015 ◽  
Author(s):  
C. Zhang ◽  
Y. Li ◽  
H.B. Lin ◽  
B.J. Yang ◽  
N. Wu
Keyword(s):  
Adaptive Threshold ◽  
Noise Attenuation ◽  
Shearlet Transform ◽  
Microseismic Data

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

scholarly journals SNR enhancement for downhole microseismic data based on scale classification shearlet transform

2018 ◽  
Vol 15 (3) ◽  
pp. 658-667 ◽  
Author(s):  
Juan Li ◽  
Shuo Ji ◽  
Yue Li ◽  
Zhihong Qian ◽  
Weili Lu
Keyword(s):  
Shearlet Transform ◽  
Microseismic Data ◽  
Scale Classification
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