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《应用地球物理(英文版)》 2012-01
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PC-based artif icial neural network inversion for airborne time-domain electromagnetic data

Zhu Kai-Guang1, Ma Ming-Yao1, Che Hong-Wei1 Yang Er-Wei1, Ji Yan-Ju1, Yu Sheng-Bao1, and Lin Jun11. Key Laboratory of Geo-exploration Instrumentation, College of Instrumentation and Electrical Engineering Changchun, Jilin University, Changchun, Jilin, 130026, China  
Traditionally, airborne time-domain electromagnetic (ATEM) data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and over-determined problems in the inversion. The correlation complicates the mapping relation between the ATEM data and the earth parameters and thus increases the inversion complexity. To obviate this, we adopt principal component analysis to transform ATEM data into orthogonal principal components (PCs) to reduce the correlations and the data dimensionality and simultaneously suppress the unrelated noise. In this paper, we use an artificial neural network (ANN) to approach the PCs mapping relation with the earth model parameters, avoiding the calculation of Jacobian derivatives. The PC-based ANN algorithm is applied to synthetic data for layered models compared with data-based ANN for airborne time-domain electromagnetic inversion. The results demonstrate the PC-based ANN advantages of simpler network structure, less training steps, and better inversion results over data-based ANN, especially for contaminated data. Furthermore, the PC-based ANN algorithm effectiveness is examined by the inversion of the pseudo 2D model and comparison with data-based ANN and Zhody’s methods. The results indicate that PC-based ANN inversion can achieve a better agreement with the true model and also proved that PC-based ANN is feasible to invert large ATEM datasets.
【Fund】: supported by the National Natural Science Foundation of China (Grant No. 40974039);; High-Tech Research and Development Program of China (Grant No.2006AA06205);; Leading Strategic Project of Science and Technology Chinese Academy of Sciences (XDA08020500)
【CateGory Index】: P631.325
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