Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate

Yang Liu, Changchun Zou, Qiang Chen, Jinhuan Zhao and Caowei Wu
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Yang Liu: School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Changchun Zou: School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Qiang Chen: Key Laboratory of Gas Hydrate, Ministry of Natural Resources, Qingdao Institute of Marine Geology, Qingdao 266071, China
Jinhuan Zhao: Key Laboratory of Gas Hydrate, Ministry of Natural Resources, Qingdao Institute of Marine Geology, Qingdao 266071, China
Caowei Wu: School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China

Energies, 2022, vol. 15, issue 13, 1-17

Abstract: As a new energy source, gas hydrates have attracted worldwide attention, but their exploration and development face enormous challenges. Thus, it has become increasingly crucial to identify hydrate distribution accurately. Electrical resistivity tomography (ERT) can be used to detect the distribution of hydrate deposits. An ERT inversion network (ERTInvNet) based on a deep neural network (DNN) is proposed, with strong learning and memory capabilities to solve the ERT nonlinear inversion problem. 160,000 samples about hydrate distribution are generated by numerical simulation, of which 10% are used for testing. The impact of different deep learning parameters (such as loss function, activation function, and optimizer) on the performance of ERT inversion is investigated to obtain a more accurate hydrate distribution. When the Logcosh loss function is enabled in ERTInvNet, the average correlation coefficient (CC) and relative error (RE) of all samples in the test sets are 0.9511 and 0.1098. The results generated by Logcosh are better than MSE, MAE, and Huber. ERTInvNet with Selu activation function can better learn the nonlinear relationship between voltage and resistivity. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, the best choices for Relu, Selu, Leaky_Relu, and Softplus. Compared with Adadelta, Adagrad, and Aadmax, Adam has the best performance in ERTInvNet with the optimizer. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, respectively. By optimizing the critical parameters of deep learning, the accuracy of ERT in identifying hydrate distribution is improved.

Keywords: deep learning; electrical resistivity tomography; hydrate distribution; numerical simulation; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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