Imaging subsurface resistivity structure from airborne electromagnetic induction data using deep neural network

ABSTRACT

Due to the rapid development and spread of deep learning technologies, potential applications of artificial intelligence technology in the field of geophysical inversion are being explored. In this study, we applied a deep neural network (DNN) to reconstruct one-dimensional electrical resistivity structures from airborne electromagnetic (AEM) data for varying sensor heights. We used numerical models and their simulated AEM responses to train the DNN to be an inversion operator, and determined that it was possible to train the DNN without the use of stabilisers on the subsurface structures. We compared the quantitative performance of DNN and Gauss–Newton inversion of synthetic datasets, and demonstrated that DNN inversion reconstructed the subsurface structure more accurately, and within a significantly shorter period. We subsequently applied DNN inversion to a field dataset to quantify the effectiveness and applicability of the proposed method for real data. The results of the current study will open new avenues for real-time imaging of subsurface structures from AEM data.

KEYWORDS:

• Airborne electromagnetics
• electrical resistivity
• inversion

Acknowledgments

This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (NO. 20164010201120 and 20182510102470). The authors appreciate Dr. Ki Ha Lee and Soon Jee Seol for their constructive discussions. Also, they wish to thank UBC-GIF for sharing the DIGHEM dataset.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry and Energy, Republic of Korea (NO. 20164010201120 and 20182510102470).