Transient electromagnetic (TEM) data can be seriously distorted by induced polarization (IP) phenomena when a polarizable body is present. The TEM field generated by a grounded-wire source contains transverse electric (TE) and transverse magnetic (TM) modes. The IP effect is most commonly studied with the TEM total field, rather than considering the difference between TE and TM fields. To investigate the effect of IP phenomena on the TE and TM fields, we have performed a detailed analysis on IP-distorted TEM data based on numerical and field examples. We first compare the IP effect on the TE and TM fields when polarizable bodies with different polarizable parameters are present. The TM field is more severely affected by the IP effect than the TE field. Compared to a single grounded-wire source, a double-line grounded-wire source can generate a larger TM field in the horizontal electric field. We compare the IP effect on TEM data from single- and double-line grounded-wire TEM configurations, and find that the data from the double-line configuration have a higher TM/TE ratio and are more severely affected by IP phenomena than in the single-line case. Thus, it would be easier to identify and extract the IP response from field data acquired with a double-line grounded-wire source configuration. These results have been verified by a field survey of the Kalatongke copper-nickel ore district, which has predominantly layered geology, in Xinjiang, China.

Exploration Geophysics is published by CSIRO PUBLISHING on behalf of the Australian Society of Exploration Geophysicists. Papers published report the results of significant case histories and relevant original research in geophysics, with emphasis on the Australian and similar environments.

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.