The traditional transient electromagnetic inversion method using artificial neural network based on gradient descent method is inefficient and can not guarantee global convergence. In order to solve these problems, this paper proposes a transient electromagnetic inversion method based on on online sequential extreme learning machine (OSELM). This approaches is used for inversion of high-dimensional exploration data obtained by transient electromagnetic method. Firstly, the hidden layer parameters (input weight and deviation) are randomly set to simplify the learning process of the model. Then, the prediction samples obtained from the test are added to the training samples as the next update information, and the online sequential extreme learning machine prediction model is established to maximize the inverse accuracy. Finally, the inversion results of two classical TEM layered geoelectric models and a quasi two-dimensional geoelectric model show that the proposed method can solve the problem of nonlinear modeling and high-dimensional data for TEM inversion, and a comparison with extreme learning machine (ELM) shows that this method has more accurate inversion, better generalization ability and higher calculation efficiency, which provides a new idea for the application of neural network in geophysical inversion.
He Z X, Zhao Z, Liu H Y, et al. TFEM for oil detection: Case studies[J]. Leading Edge, 2012,31(5):518-521. doi: 10.1190/tle31050518.1.
[2]
Tantum S L, Collins L M. A comparison of algorithms for subsurface target detection and identification using time-domain electromagnetic induction data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(6):1299-1306. doi: 10.1109/36.927453.
Yang H Y, Li F P, Yue J H, et al. Transient electromagnetic optimization inversion of conical field source based on “smoke circle” theory[J]. Journal of China University of Mining and Technology, 2016,45(6):1230-1237.
[7]
Puzyrev V, Swidinsky A. Inversion of 1D frequency-and time-domain electromagnetic data with convolutional neural networks[J]. Computers and Geosciences, 2020,149:104681. doi: 10.1016/j.cageo.2020.104681.
He Y, Zhang J, Liu H S. Application of surface wave iterative inversion based on neural network[J]. Journal of Southwest Petroleum University:Natural Science Edition, 2010,32(1):40-44.
Ji Y J, Xu J, Wu Q, et al. Semi aerial apparent resistivity inversion based on neural network electrical source[J]. Journal of Radio Science, 2014,29(5):973-980.
[11]
Srinivas Y, Raj A S, Oliver D H, et al. A robust behavior of Feed Forward Back propagation algorithm of Artificial Neural Networks in the application of vertical electrical sounding data inversion[J]. Geoscience Frontiers, 2012,3(5):729-736.
[12]
Maiti S, Erram V C, Gupta G, et al. ANN based inversion of DC resistivity data for groundwater exploration in hard rock terrain of western Maharashtra (India)[J]. Journal of Hydrology, 2012,464:294-308. doi: 10.1016/j.jhydrol.2012.07.020.
[13]
Johnson O L, Aizebeokhai A P. Application of artificial neural network for the inversion of electrical resistivity data[J]. Journal of Informatics and Mathematical Sciences, 2017,9(2):297-316.
[14]
Wilamowski B M, Yu H. Neural network learning without backpropagation[J]. IEEE Transactions on Neural Networks, 2010,21(11):1793-1803. doi: 10.1109/TNN.2010.2073482.
[15]
Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: a new learning scheme of feedforward neural networks[C]// IEEE International Joint Conference on Neural Networks. IEEE, 2005.
[16]
Cai Z, Gu J, Luo J, et al. Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy[J]. Expert Systems with Applications, 2019,138. doi: 10.1016/j.eswa.2019.07.031.
Wang B Y, Zhao S, Zhang S M. Distributed power load forecasting algorithm based on cloud computing and extreme learning machine[J]. Power System Technology, 2014,38(2):526-531. doi: 10.13335/j.1000-3673.pst.2014.02.039.
[18]
She Q, Chen K, Ma Y, et al. Sparse representation-based extreme learning machine for motor imagery EEG classification[J]. Computational Intelligence & Neuroscience, 2018(1):1-9. doi: 10.1190/tle31050518.1.
Wei D, Liu D S, Yan D Q, et al. Neighborhood preserving extreme learning machine for face image recognition[J]. Computer Engineering and Applications, 2019,55(11):187-191.
[20]
Li H F, Wang S H, Zou Z, et al. An integrated methodology for rule extraction from ELM—Based vacuum tank degasser multiclassifier for decision-making[J]. Energies, 2019,12(18):3535. doi: 10.3390/en12183535.
[21]
Liang N Y, Huang G B, Saratchandran P, et al. A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE Transactions on Neural Networks, 2006,17:1411-1423. doi: 10.1109/TNN.2006.880583.
[22]
裴飞. 基于在线序列极限学习机的变压器故障诊断研究[D]. 北京:华北电力大学, 2015.
[22]
Pei F. Research on transformer fault diagnosis based on online sequential extreme learning machine[D]. Beijing: North China Electric Power University, 2015.
Zhang M Y, Wen Y Y, Yang X T, et al. An online sequential extreme learning machine algorithm based on incremental weighted average[J]. Control and Decision Making, 2017,32(10):1887-1893.
[24]
Yin J C, Zou Z J, Xu F, et al. Online ship roll motion prediction based on grey sequential extreme learning machine[J]. Neurocomputing, 2014,129(10):168-174.
[25]
Kaufman A A, Keller G V. Frequency and transient sounding[J]. Elsevier Methods in Geochemistry & Geophysics, 1983,21. doi: 10.1016/0031-9201(83)90051-1.
[26]
Vignoli G, Fiandaca G, Christiansen A V, et al. Sharp spatially constrained inversion with applications to transient electromagnetic data[J]. Geophysical Prospecting, 2014,63(1):243-255. doi: 10.1111/1365-2478.12185.