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  4. 岩石圈演化国家重点实验室
A Sparse Spike Deconvolution Algorithm Based on a Recurrent Neural Network and the Iterative Shrinkage-Thresholding Algorithm

文献类型：期刊论文

作者Pan, Shulin4; Yan, Ke5; Lan, Haiqiang1; Badal, Jose2; Qin, Ziyu3
刊名ENERGIES
出版日期2020-06-01
卷号13期号:12页码:13
关键词seismic wavelet sparse spike deconvolution ISTA RNN BPTT
DOI10.3390/en13123074
英文摘要Conventional sparse spike deconvolution algorithms that are based on the iterative shrinkage-thresholding algorithm (ISTA) are widely used. The aim of this type of algorithm is to obtain accurate seismic wavelets. When this is not fulfilled, the processing stops being optimum. Using a recurrent neural network (RNN) as deep learning method and applying backpropagation to ISTA, we have developed an RNN-like ISTA as an alternative sparse spike deconvolution algorithm. The algorithm is tested with both synthetic and real seismic data. The algorithm first builds a training dataset from existing well-logs seismic data and then extracts wavelets from those seismic data for further processing. Based on the extracted wavelets, the new method uses ISTA to calculate the reflection coefficients. Next, inspired by the backpropagation through time (BPTT) algorithm, backward error correction is performed on the wavelets while using the errors between the calculated reflection coefficients and the reflection coefficients corresponding to the training dataset. Finally, after performing backward correction over multiple iterations, a set of acceptable seismic wavelets is obtained, which is then used to deduce the sequence of reflection coefficients of the real data. The new algorithm improves the accuracy of the deconvolution results by reducing the effect of wrong seismic wavelets that are given by conventional ISTA. In this study, we account for the mechanism and the derivation of the proposed algorithm, and verify its effectiveness through experimentation using theoretical and real data.
WOS关键词BACKPROPAGATION
资助项目State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation[PLN201733] ; Natural Gas and Geology Key Laboratory of Sichuan Province[2015trqdz03] ; National Natural Science Foundation of China[NSFC 41204101]
WOS研究方向Energy & Fuels
语种英语
WOS记录号WOS:000550189900001
出版者MDPI
资助机构State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation ; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation ; Natural Gas and Geology Key Laboratory of Sichuan Province ; Natural Gas and Geology Key Laboratory of Sichuan Province ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation ; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation ; Natural Gas and Geology Key Laboratory of Sichuan Province ; Natural Gas and Geology Key Laboratory of Sichuan Province ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation ; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation ; Natural Gas and Geology Key Laboratory of Sichuan Province ; Natural Gas and Geology Key Laboratory of Sichuan Province ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation ; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation ; Natural Gas and Geology Key Laboratory of Sichuan Province ; Natural Gas and Geology Key Laboratory of Sichuan Province ; National Natural Science Foundation of China ; National Natural Science Foundation of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/97201]  
专题地质与地球物理研究所_岩石圈演化国家重点实验室
通讯作者Pan, Shulin
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, State Key Lab Lithospher Evolut, Beijing 100049, Peoples R China
2.Univ Zaragoza, Phys Earth, Sci B, Pedro Cerbuna 12, Zaragoza 50009, Spain
3.Chengdu Univ Technol, Sch Geophys, Chengdu 610059, Peoples R China
4.Southwest Petr Univ, Sch Earth Sci & Technol, Chengdu 610500, Peoples R China
5.Petro China Southwest Oil & Gasfield Co, Chengdu 610051, Peoples R China
推荐引用方式
GB/T 7714		 Pan, Shulin,Yan, Ke,Lan, Haiqiang,et al. A Sparse Spike Deconvolution Algorithm Based on a Recurrent Neural Network and the Iterative Shrinkage-Thresholding Algorithm[J]. ENERGIES,2020,13(12):13.
APA Pan, Shulin,Yan, Ke,Lan, Haiqiang,Badal, Jose,&Qin, Ziyu.(2020).A Sparse Spike Deconvolution Algorithm Based on a Recurrent Neural Network and the Iterative Shrinkage-Thresholding Algorithm.ENERGIES,13(12),13.
MLA Pan, Shulin,et al."A Sparse Spike Deconvolution Algorithm Based on a Recurrent Neural Network and the Iterative Shrinkage-Thresholding Algorithm".ENERGIES 13.12(2020):13.

入库方式： OAI收割

来源：地质与地球物理研究所

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