Characterization of hydrothermal alteration along geothermal wells using unsupervised machine-learning analysis of X-ray powder diffraction data

Zonal distribution of hydrothermal alteration in and around geothermal fields is important for understanding the hydrothermal environment. In this study, we assessed the performance of three unsupervised classification algorithms—K-mean clustering, the Gaussian mixture model, and agglomerative clustering—in automated categorization of alteration minerals along wells. As quantitative data for classification, we focused on the quartz indices of alteration minerals obtained from rock cuttings, which were calculated from X-ray powder diffraction measurements. The classification algorithms were first examined by applying synthetic data and then applied to data on rock cuttings obtained from two wells in the Hachimantai geothermal field in Japan. Of the three algorithms, our results showed that the Gaussian mixture model provides classes that are reliable and relatively easy to interpret. Furthermore, an integrated interpretation of different classification results provided more detailed features buried within the quartz indices. Application to the Hachimantai geothermal field data showed that lithological boundaries underpin the data and revealed the lateral connection between wells. The method’s performance is underscored by its ability to interpret multi-component data related to quartz indices

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

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

Hydrothermal system beneath the crater of Tarumai volcano, Japan : 3-D resistivity structure revealed using audio-magnetotellurics and induction vector

Audio-magnetotelluric (AMT) measurements were recorded in the crater area of Tarumai volcano, northeastern Japan. This survey brought the specific structures beneath the lava dome of Tarumai volcano, enabling us to interpret the relationship between the subsurface structure and fumarolic activity in the vicinity of a lava dome. Three-dimensional resistivity modeling was performed to achieve this purpose. The measured induction vectors pointed toward the center of the dome, implying the topographic effect. However, estimation of the topographic effect showed that the measured vector was not explained only by this effect. This suggested that the distribution of induction vectors still held information of the subsurface structure and could be helpful in determining the geometry of 3-D bodies. The 3-D modeling was based on a quasi-one-dimensional layered structure that included topography. The final model revealed that the andesitic lava dome is characterized by comparatively low resistivity (50 Ωm), and that two conductive bodies (50 and 1-5 Ωm) are present beneath the lava dome. The shallower of these conductors is interpreted as an aquifer, such as a buried crater lake. The deeper, extremely conductive body corresponded to a convecting zone containing rising hydrothermal fluid. The shallower aquifer critically controls the temperature and chemical components of the fumarolic gasses. High-temperature gas supplied from deeper part that encounters the shallow aquifer loses its water-soluble components and heat, resulting in weak and low-temperature fumaroles. In contrast, most of the gas, which ascends outside the area of the shallower aquifer, is released as high-temperature fumaroles. This study provides an insight that the shallow aquifer in the crater area plays a significant role in the property of fumaroles at the volcanic surface

Surface displacements of Aso volcano after the 2016 Kumamoto earthquake based on SAR interferometry: implications for dynamic triggering of earthquake-volcano interactions

The 2016 Kumamoto earthquake involved a series of events culminating in an Mw 7.0 main shock on 2016 April 16; the main-shock fault terminated in the caldera of Aso volcano. In this study, we estimated surface displacements after the 2016 Kumamoto earthquake using synthetic aperture radar interferometry analysis of 16 Phased Array Type L-band Synthetic Aperture Radar-2 images acquired from 2016 April 18 to 2017 June 12 and compared them with four images acquired before the earthquake. Ground subsidence of about 8 cm was observed within about a 3 km radius in the northwestern part of Aso caldera. Because this displacementwas not seen in data acquired before the 2016 Kumamoto earthquake, we attribute this displacement to the 2016 Kumamoto earthquake. Furthermore, to estimate the source depth of the surface displacement, we applied the Markov chain Monte Carlo method to a spherical source model and obtained a source depth of about 4.8 km. This depth and position are nearly in agreement with the top of a low-resistivity area previously inferred from magnetotelluric data; this area is thought to represent a deep hydrothermal reservoir. We concluded that this displacement is due to the migration of magma or aqueous fluids