The transient electromagnetic (TEM) method is a commonly used, nonintrusive, geophysical method, but inherent mutual induction between the transmitter (TX) and receiver (RX) coils strongly influences the measurements. We have developed an opposing-coils configuration to greatly reduce this effect. Three coils are used in this system. The upper opposing coil is physically the same as the lower TX coil, and they are concentric and parallel to the middle RX coil. A pair of currents with equal amplitudes but reverse directions is injected into the opposing and TX coils. Theoretical calculations in free space show that the received magnetic field by the RX coil is zero, which indicates that the mutual induction effect could be largely reduced. Physical experiments prove that an almost-pure secondary field could be acquired using this system. We have studied an optimal separation between the TX and opposing coils to guarantee that the primary magnetic field is powerful and the instrument is compacted for field work. Then, the efficient exploration depth of this system for typical geoelectric models was simulated to be approximately 15–50 m. Comparisons of simulated responses over highly conductive thick plates in free space and a field test over a culvert structure between this system and EM-47 showed that the system has enhanced sensitivity and lateral resolution. This system can be used in near-surface investigations, e.g., groundwater, environmental, and engineering investigations.

Due to the strong capability of building complex nonlinear mapping without involving linearization theory and high prediction efficiency; the deep learning (DL) technique applied to solve geophysical inverse problems has been a subject of growing interest. Currently, most DL-based inversion approaches are fully data-driven (namely standard deep learning), the performance of which largely depends on the training sample sets. However, due to the heavy burden of time and computational resources, it can be challenging to supply such a massive and exhaustive training dataset for generic realistic exploration scenarios and to perform network training. In this work, based on the recent advances in physics-based networks, the physical laws of magnetotelluric (MT) wave propagation is incorporated into a purely data-driven DL approach (PlainDNN) and thus builds a physics-driven DL MT inversion scheme (PhyDNN). In this scheme, the forward operator modeling MT wave propagation is integrated into the network training loop, in the form of minimizing a hybrid loss objective function composed of the data-driven model misfit and physics-based data misfit, to guide the network training. Consequently, the proposed PhyDNN method will take the advantage of the fully data-driven DL and conventional physics-based deterministic methods, allowing it to deal with complex realistic exploration scenarios. Quantitative and qualitative analysis results demonstrate that the PhyDNN can honor the physical laws of the MT inverse problem, and with other conditions unchanged, the PhyDNN outperforms the PlainDNN and the classical deterministic Occam inversion method. When processing field data, the PhyDNN method yields considerably impressive inversion results compared to the Occam method, and the corresponding simulated MT responses agree well with the real measurements, which confirms the effectiveness and applicability of the PhyDNN method.

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