A Deep learning estimation for probing Depth of Transient Electromagnetic Observation
The probing depth of transient electromagnetic method (TEM) refers to the depth range at which changes in underground conductivity can be effectively detected. It typically ranges from tens of meters to several kilometers and is influenced by factors such as instrument parameters and the conductivity of the subsurface structure. Rapid and accurate calculating the probing depth is beneficial for determining the feasibility of exploration engineering, setting appropriate inversion parameters and improving exploration accuracy. However, mainstream methods suffer from issues such as low computational precision, large uncertainties, or high computational requirements, making them unsuitable for processing massive airborne electromagnetic data. In this study, we propose a prediction model based on deep learning that can directly compute the probing depth from the TEM responses, and its effectiveness and accuracy is validated through synthetic models and field measurements. Furthermore, we apply this algorithm to deep learning-based ATEM inversion by constraining the one-dimensional resistivity models in the training set above the probing depth, to reduce the non-uniqueness of the inversion, accelerate the convergence, and improve its prediction accuracy.
Email Address of Submitting Authorrongjiang.email@example.com
ORCID of Submitting Authororcid.org/0000-0001-9559-2712
Submitting Author's InstitutionYangtze Region Delta Institute, University of Electronic Science and Technology of China
Submitting Author's Country