Denoising stacked autoencoders for transient electromagnetic signal denoising

The transient electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field signal (SFS) in the TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information. To re...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Nonlinear processes in geophysics Ročník 26; číslo 1; s. 13 - 23
Hlavní autori: Lin, Fanqiang, Chen, Kecheng, Wang, Xuben, Cao, Hui, Chen, Danlei, Chen, Fanzeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Gottingen Copernicus GmbH 01.03.2019
Copernicus Publications
Predmet:
Analysis
Artificial neural networks
Coils
Data comparison
Electromagnetism
Feature extraction
Geology
Geophysics
Interference
Machine learning
Methods
Neural networks
Noise control
Noise reduction
Principal components analysis
Probability theory
Random noise
Sensors
Technology application
Wavelet analysis
Wavelet transforms
ISSN:1607-7946, 1023-5809, 1607-7946
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The transient electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field signal (SFS) in the TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information. To reduce the noise interference and detect deep geological information, we apply autoencoders, which make up an unsupervised learning model in deep learning, on the basis of the analysis of the characteristics of the SFS to denoise the SFS. We introduce the SFSDSA (secondary field signal denoising stacked autoencoders) model based on deep neural networks of feature extraction and denoising. SFSDSA maps the signal points of the noise interference to the high-probability points with a clean signal as reference according to the deep characteristics of the signal, so as to realize the signal denoising and reduce noise interference. The method is validated by the measured data comparison, and the comparison results show that the noise reduction method can (i) effectively reduce the noise of the SFS in contrast with the Kalman, principal component analysis (PCA) and wavelet transform methods and (ii) strongly support the speculation of deeper underground features.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1607-7946
1023-5809
1607-7946
DOI:10.5194/npg-26-13-2019