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.