Geothermal energy has become an attractive renewable source of energy around the globe. Developing effective geophysical methods for geothermal exploration is vital for studying these resources. It is well known that electric conductivity is an important indicator of the location of geothermal sources. One of the most widely used geophysical techniques for analyzing the deep electrical conductivity structure is the magnetotelluric (MT) method. At the same time, the airborne electromagnetic (EM) surveys represent effective methods for the near-surface conductivity study. In this paper, we jointly analyze the Helicopter Transient Electromagnetic (HeliTEM) and magnetotelluric (MT) data acquired in some geothermal areas of Japan. The advantage of this approach over the analysis of the MT data alone is related to the fact that MT data are strongly affected by the near-surface inhomogeneities. Furthermore, the airborne HeliTEM data provide complementary information about the near-surface conductivity distribution, which we use to constrain the results of MT inversion. Thus, the joint interpretation of MT and HeliTEM data produces more reliable information about the deep conductivity model. This paper discusses the methods of 3D inversion of HeliTEM data and how to use these data in 3D MT inversion. The developed approach to the joint interpretation of the HeliTEM and MT data is illustrated by practical inversion of the HeliTEM and MT data collected over the geothermal field in Japan.

Extensive geophysical surveys were conducted over the geothermal field by Japan Oil, Gas and Metals National Corporation (JOGMEC) and Idemitsu Kosan Co. Ltd. (Idemitsu), which included airborne gravity gradiometry (AGG) and magnetotelluric (MT) surveys. The goal of this project was to study the location and structure of geothermal energy sources in the surveys area. The observed AGG and MT data were analyzed separately and jointly using 3D inversion methods. For joint inversion, we used the approach based on Gramian constraints (Zhdanov et al., 2012; Zhdanov, 2015). The Gramian method enforces the correlation between the different physical parameters of the inverse models, or their transforms, thus ensuring that the inversion produces a consistent image of the subsurface geological formation. This paper summarizes the principles of the joint inversion algorithm used in the project. We also present the results of the standalone and joint inversions to demonstrate the effectiveness of the developed method for geothermal resource exploration.