A rapid imaging approach for transient electromagnetic data based on gradient features

Data processing techniques in transient electromagnetic (TEM) methods face numerous challenges, such as the problem of 3-D imaging and the demand for new data processing techniques arising from the development of small-loop TEM devices. We have developed an imaging algorithm based on gradient features derived from TEM data to address these challenges. We found that normalising the gradient field of TEM responses under appropriate background conditions enables imaging of the target. Numerical experiments conducted under theoretical models have validated the feasibility of this approach and highlighted the significance of background fields. We further designed more complex models and achieved 3-D imaging of synthetic data within seconds, demonstrating the method's high efficiency and applicability to high-dimensional data. Subsequently, we applied the algorithm to challenging small-loop TEM data and demonstrated its superior performance compared to the inversion method, which presents a novel approach for processing data from small-loop TEM devices. This study demonstrates that the algorithm possesses characteristics of high efficiency and wide applicability, suitable for TEM data from various dimensions and types of devices. Furthermore, it holds promising potential in assisting the enhancement of the efficiency of the three-dimensional inversion of TEM data.