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Salt Delineation From Electromagnetic Data Using Convolutional Neural Networks

Title
Salt Delineation From Electromagnetic Data Using Convolutional Neural Networks
Author
설순지
Keywords
Convolutional neural networks (CNNs); electrical resistivity inversion; electromagnetic (EM); salt body
Issue Date
2019-04
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v. 16, NO 4, Page. 519-523
Abstract
With recent advances in machine learning, convolutional neural networks (CNNs) have been successfully applied in many fields, and several attempts have been made in the field of geophysics. In this letter, we investigated the mapping of subsurface electrical resistivity distributions from electromagnetic (EM) data with CNNs. To begin imaging electrical resistivity using CNNs, we carried out precise delineation of a subsurface salt structure, which is indispensable for identification of offshore hydrocarbon reservoirs, using towed streamer EM data. For training the CNN model, an electrical resistivity model, including a salt body, and corresponding EM data calculated through numerical modeling were used as the label and input, respectively. The optimal weights and biases of the CNN were obtained minimizing the mean-square error between the predicted resistivity distribution and the target label. The final CNN model was selected using a validation data set during training. After training, we applied the trained CNN to test data sets of noisy data and simulated-SEAM data, which were not provided to the network during training. The test results demonstrate that our trained CNN model is stable, reliable, and efficient, and indicate the possibility of successful application of our CNN model to field data. Our study has shown the promising potential of CNNs for identifying defined subsurface electrical resistivity structures that are difficult to find using conventional EM inversion.
URI
https://ieeexplore.ieee.org/document/8527527https://repository.hanyang.ac.kr/handle/20.500.11754/122099
ISSN
1545-598X; 1558-0571
DOI
10.1109/LGRS.2018.2877155
Appears in Collections:
RESEARCH INSTITUTE[S](부설연구소) > ETC
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