Extraction of Multiple Electrical Parameters From IP-Affected Transient Electromagnetic Data Based on LSTM-ResNet

The induced polarization (IP) effect due to a polarizable body distorts transient electromagnetic (TEM) data, thereby potentially triggering sign reversal phenomena in the measured response. The measured horizontal electric field associated with a grounded-wire TEM is more strongly affected by IP effects than the measured vertical field, meaning that data inversion is more problematic for its component. The traditional inversion method, which assumes a frequency independent resistivity, is complex to extract the chargeability. Yet, the chargeability provides critical information, so it is important to extract the chargeability in addition to the resistivity from IP-affected TEM data. Thus, we proposed a data-driven method based on deep learning to recover the resistivity and chargeability of IP-affected horizontal electric fields. This method, named LSTM-ResNet, combines long short-term memory (LSTM) and a residual network (ResNet) to estimate subsurface electrical properties. Synthetic tests showed that LSTM-ResNet is computationally efficient and accurate for inversion problems. Based on the inverse results with data added noise, we found that a well-trained neural network was not sensitive to noise. A case study was performed by applying LSTM-ResNet to field data collected by a grounded-wire TEM survey at the Kalatongke copper-nickel ore deposit. LSTM-ResNet recovered the simultaneous resistivity and chargeability distributions of subsurface structures from the IP-affected horizontal electric TEM field. The results show a high-chargeability and low-resistivity layer, which was consistent with the lithologic profiles based on drilling cores, indicating the accuracy and robustness of the proposed framework for multi-parameter inversion.