Kazuya Ishitsuka 교수

Predicting the temperature, pressure, and permeability at depth is crucial for understanding natural-state geothermal systems. As direct observations of these quantities are limited to well locations, a reliable methodology that predicts the spatial distribution of the quantities from well observations is required. In this study, we developed a physics-informed neural network (PINN), which constrains predictions to satisfy conservation of mass and energy, for predicting spatial distributions of

We have identified areas of soil liquefaction by the analysis of surface changes caused by the 2011 Tohoku earthquake, using synthetic aperture radar (SAR) interferometry in the Kanto region of Japan. Changes in surface scattering properties were evaluated using phase-corrected coherence, computed from the reflective intensity (amplitude) of SAR data. Often, the loss of coherence (decorrelation) is simply considered to represent areas damaged from the disaster. However, temporal decorrelation co

Ground-penetrating radar allows the acquisition of many images for investigation of the pavement interior and shallow geological structures. Accordingly, an efficient methodology of detecting objects, such as pipes, reinforcing steel bars, and internal voids, in ground-penetrating radar images is an emerging technology. In this paper, we propose using a deep convolutional neural network to detect characteristic hyperbolic signatures from embedded objects. As a first step, we developed a migratio

We estimated recent surface displacements around Bangkok by means of persistent scatterer interferometry with ALOS/PALSAR images acquired from November 2007 to December 2010. Land subsidence due to excessive groundwater pumping has been reported in this region. However, we detected ground surface uplift around the mega-city, along with seasonal surface displacement, with high spatial resolution. We then discriminated long-term natural rebound and seasonal displacement by fitting exponential and

Neural network has been successfully used for field-scale temperature estimation based on resistivity data. Although the methodology is able to estimate temperature distribution based on geophysical data with a limited number of temperature data, it has also been shown that the estimation accuracy can decrease as distance increases from temperature measurements. In this letter, we developed a resistivity-based neural kriging approach to improve the accuracy of estimated temperatures. The neural

Persistent scatterer (PS) interferometry analysis has been performed using single-polarimetric synthetic aperture radar (SAR) interferograms, but newer SAR satellites enable the use of polarimetric data for such analyses. In this paper, to analyze polarimetric SAR interferograms, we proposed the maximum likelihood method at the PS selection stage. With the proposed method, interferograms in a certain polarimetric component exhibiting high coherence contribute to the estimation of surface displac

We investigated the post-seismic surface displacement of the 2011 Tohoku earthquake around the Kanto Plain (including the capital area of Japan), which is located approximately 400 km from the epicenter, using a global positioning system network during 2005–2015 and persistent scatterer interferometry of TerraSAR-X data from March 2011 to November 2012. Uniform uplift owing to viscoelastic relaxation and afterslip on the plain has been reported previously. In addition to the general trend, we id

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 temperatu

Abstract 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 cutti

Abstract The 2016 Kumamoto earthquake sequence on April 14 ( M w 6.2) and April 16 ( M w 7.0) altered the regional groundwater level. To better understand the relationship between groundwater level change and surface displacement, we estimated surface displacement in the Kumamoto area (Japan) using persistent scatterer interferometry from 19 ALOS/PALSAR images acquired between January 7, 2007 and March 5, 2011, 28 ALOS-2/PALSAR-2 images acquired between April 17, 2016 and December 10, 2018, and

We propose a method of incorporating the copolarized observations (HH and VV observations) of multipolarimetric SAR acquisitions into persistent scatterer interferometry to estimate the surface displacement and the scatterer height. Interferograms obtained from HH and VV observations were equally weighted on the basis that the magnitude of the backscattered signals from persistent scatterers represented by odd-bounce scattering and even-bounce scattering are the same in the copolarized observati

Standard synthetic aperture radar interferometry (InSAR) time-series analysis uses interferometric pairs selected using arbitrarily fixed baseline criteria, and pixels coherent within the baseline criteria are used for displacement estimations. However, adequate baseline criteria could differ pixel by pixel, especially in suburban areas, because coherence variation depends on the scattering characteristics of specific pixels. Accordingly, it would be better to determine the optimal baseline crit