Paper Info
Title
A Deep-Learning-Based Generalized Convolutional Model For Seismic Data and Its Application in Seismic Deconvolution
Abstract
The convolutional model, which describes the relation among poststack seismic data, wavelet, and reflectivity, is the foundation of seismic deconvolution (SD). However, this model is only an approximation of the seismic wave equation, and it may not work in complex cases especially when the medium is anelastic, heterogeneous, and anisotropic. In this article, we propose a generalized convolutional model for poststack seismic data. A deep-learning-based data correction term is added to characterize the data ingredients that cannot be characterized by the convolutional model. The data correction term of the new model is realized using the long-short term memory (LSTM)-based deep learning architecture, of which parameters are learned based on the dataset from several well logs. Based on the new model, we propose an SD method and investigate its performance in building reflectivity models using complex numerical examples. The results verified that the new model can accurately characterize complex seismic data, which cannot be characterized by a convolutional model. In addition, the proposed SD method has significant advantages over traditional methods in building high-fidelity reflectivity models in complex cases.
Year
DOI
Venue
2022
10.1109/TGRS.2021.3076991
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Data models, Mathematical model, Deep learning, Deconvolution, Convolutional codes, Numerical models, Convolution, Convolutional model, deep learning, high resolution, machine learning, seismic deconvolution (SD)
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Zhaoqi Gao100.34
Sichao Hu200.34
Chuang Li321.76
Hongling Chen402.03
Xiudi Jiang500.68
Zhibin Pan600.34
Jinghuai Gao79727.94
Zongben Xu83203198.88