1. College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China 2. National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, China 3. Integrated Geophysical Simulation Laboratory, Chang’an University, Xi’an 710054, China
The transient electromagnetic method (TEM) commonly uses the all-time apparent resistivity parameter for interpretation, which involves complex formulas and time-consuming iterative processes. Based on the characteristics of TEM data, this study employed the artificial neural network (ANN) for TEM pseudo-resistivity imaging. First, this study designed a multi-hidden-layer BP neural network and calculated a response amplitude through TEM analysis. The response amplitude, as the mapping parameter of pseudo resistivity, was used for network training. Then new data outside the training set were used to test the trained network. A homogeneous half-space and one-dimensional layered model was built to verify the correctness and adaptability of the neural network. The imaging of the three-dimensional geoelectric model was performed. As revealed by the results, the pseudo resistivity calculated based on the neural network can reflect the target anomalies of the geoelectric model, with highly accurate network imaging results. Finally, the measured data were processed using the neural network algorithm, further indicating that the neural network-based imaging can serve as a basis for data interpretation. This study verified the feasibility of the ANN in TEM imaging, thus providing a new approach for TEM imaging.
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