A Nonparametric Method for Automatic Denoising of Microseismic Data
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-16
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset.
In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data.
The method consists of three major steps: (1) applying a two-step AIC algorithm to pick P-wave arrival; (2) subtracting the noise power spectrum from the signal power spectrum; (3) recovering the microseismic signal by inverse Fourier transform.
The proposed method is tested on synthetic datasets with different signal types and SNRs, as well as field datasets.
The results of the proposed method are compared against ensemble empirical mode decomposition (EEMD) and wavelet denoising methods, which shows the effectiveness of the method for denoising and improving the SNR of microseismic data.
American Psychological Association (APA)
Peng, Pingan& Wang, Liguan. 2018. A Nonparametric Method for Automatic Denoising of Microseismic Data. Shock and Vibration،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1215255
Modern Language Association (MLA)
Peng, Pingan& Wang, Liguan. A Nonparametric Method for Automatic Denoising of Microseismic Data. Shock and Vibration No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1215255
American Medical Association (AMA)
Peng, Pingan& Wang, Liguan. A Nonparametric Method for Automatic Denoising of Microseismic Data. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1215255
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215255