Near real-time subsurface structure imaging using airborne electromagnetic data based on deep learning

The airborne electromagnetic (AEM) method is a modern technique in geophysical surveys with the merits of terrain adaptability and acquisition efficiency, and can image the electrical structure of the Earth’s subsurface down to several hundred meters. AEM survey has been applied extensively in mineral exploration, groundwater monitoring, environment investigation and geological mapping. However, the expansion of survey area and spatial sampling bring about huge volumes of AEM data, which present a serious computational challenge for rapid AEM inversion. Inspired by Google’s neural machine translation system, we develop a fast inversion system guided by deep learning to translate AEM data directly into subsurface resistivity structures. Synthetic tests demonstrate that our proposed inversion system has strong noise robustness and can provide reliable inversion results much more efficiently than the conventional Gauss-Newton algorithm. In the field data application, our system obtains robust inversion results of more than 740,000 AEM soundings acquired by the U.S. Geological Survey from the Leach Lake Basin in California in seconds on a common PC. The inverted images coincide exactly with previous studies of the local geological environment and clearly outline the geometries of the lake, faults and surrounding mountains. Our inversion system can support near real-time underground subsurface imaging for AEM surveys and inject new vigor into resource exploration and tectonic studies.