Constraining temperature at depth of the Kakkonda geothermal field, Japan, using Bayesian rock-physics modelling of resistivity: Implications to the deep hydrothermal system

Temperature-at-depth estimation is important for assessing supercritical geothermal resources. Bayesian rock-physics modelling of electrical resistivity is effective for estimating temperatures at depth. In this study, we improved a previously proposed Bayesian framework and demonstrated its effectiveness by estimating subsurface temperatures in the Kakkonda geothermal field, Japan. The proposed framework allows the estimation of either effective porosities or salinities in addition to temperatures; further, we were able to constrain the possible states of the crustal fluid at depth based on the estimates. The estimated 3D temperature structure was consistent with available deep temperature logs. Furthermore, the estimated results suggest the existence of a magmatic-hydrothermal system at depth in the field.