Interpretation of Frequency-Domain Airborne Electromagnetic Data Based on the Deep Neural Network Incorporating Topographic Information

Abstract

Deep neural networks (DNNs) have recently been used to interpret frequency-domain electromagnetic (EM) data; therefore, vast amounts of information can be rapidly interpreted. However, in airborne surveys that include mountainous regions, severe topographic changes distort the EM data, and they can lead to unreasonable interpretations. DNN-based inversions that reduce the effects of topographic changes have not yet been proposed, whereas some conventional inversion techniques can correct topographic distortions. Since DNN-based interpretation needs various training datasets and its performance depends on the characteristics of training dataset, it is important to generate various airborne EM (AEM) data and resistivity model pairs. However, it is almost impossible to derive the EM responses of 2-D or 3-D resistivity models incorporating diverse topographic patterns, because such models require many variables and computation costs are limited. Therefore, we suggested the pseudo-1-D interpretation of EM data, which can consider the topographic changes using EM responses and topographic information at three neighboring data points. We include topographic information in DNN training as slope angle between the data points. Compared with conventional inversion, our trained DNN model recovers the synthetic resistivity models more reasonably. In addition, we used the trained DNN model to evaluate a real AEM dataset from Southcentral Alaska. The trained DNN provided reasonable interpretation results despite that the data were acquired at mountainous area with rough topography. Since the trained DNN model can provide predictions rapidly, the interpretation time was drastically reduced. We believe that this research can increase the practical field applicability of AEM survey.