- journal_title：Geophysics
- Contributor：Viacheslav Spichak ; Olga Zakharova
- Publisher：Society of Exploration Geophysicists
- Date：2012-
- Format：text/html
- Language：en
- Identifier：10.1190/geo2011-0397.1
- journal_abbrev：Geophysics
- issn：0016-8033
- volume：77
- issue：4
- firstpage：WB179
- section：Near-surface EM
Advances have been made in the subsurface temperature estimation basing on the indirect electromagnetic geothermometer. The approach used was based on the artificial neural network technique, which, contrary to other available approaches, does not imply the prior knowledge of the electrical conductivity mechanisms and rock properties. Application of the indirect EM geothermometer to the interwell space interpolation in three areas (Tien Shan, Kyrgyzstan; Soultz-sous-Forêts, France; and Hengill, Iceland) characterized by different geologic environments indicated that the temperature estimation errors are controlled by four factors: faulting, distance between the EM site and the area where the temperature is estimated, meteoric and groundwater flows, and lateral geologic inhomogeneity (although the latter factor is less restrictive if appropriate EM inversion tools are used). It was demonstrated that the extrapolation errors depend on two factors: spacing between the EM site and the borehole, and ratio between the well length and the extrapolation depth. In particular, the relative accuracy of the temperature extrapolation to the depths twice as large as the borehole depth did not exceed an average of 5%. Using the indirect EM geothermometer, it was possible to reconstruct 2D and 3D temperature models of the studied areas from EM sounding data, which, in turn, enabled us to draw important conclusions regarding the dominating heat transfer mechanisms, fluid circulation paths, and better locations for drilling new boreholes. Application of the indirect EM geothermometer during exploitation of the geothermal reservoirs may enable one to monitor the variations of subsurface temperatures basing on the ground EM monitoring data and forecast future trends.