Physics-informed neural network for inverse modeling of natural-state geothermal systems

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 method-ology 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 temperature, pressure, and permeability of natural-state hydrothermal systems. We assessed the characteristics of the proposed method by applying it to 2D synthetic models of geothermal systems. Our results showed that the PINN outperformed the conventional neural network in terms of prediction accuracy. Among the PINN-predicted quantities, the errors in the predicted temperatures in the unexplored regions were significantly reduced. Furthermore, we confirmed that the predictions decreased the loss of the conservation laws. Thus, our PINN approach guarantees physical plausibility, which has been impossible using existing machine learning approaches. As permeability investigations in geothermal wells are often limited, we also demonstrate that the resistivity model obtained using the magnetotelluric method is effective in supplementing permeability observations and improving its prediction accuracy. This study demonstrated for the first time the usefulness of the PINN to a geothermal energy problem

Measurement of thermal conductivities of drill cuttings and quantification of the contribution of thermal conduction to the temperature log of the Hachimantai geothermal field, Japan

This study proposed and evaluated a method of measuring the thermal conductivity (TC) of drill cuttings from several igneous and pyroclastic rocks using the transient plane source principle, which allows quick and reliable measurements. The estimated bulk TCs of rocks were within an error of <10%, and suitable models were found. Measurements were applied to drill cuttings obtained along a well in the Hachimantai geothermal field, Japan, and TCs were obtained at ∼25 m intervals to a depth of 1700 m. Our analysis of the temperature profile using estimated TCs suggest the possible presence of fluid-flow zones in the well

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

Integrated genetic and clinical prognostic factors for aggressive adult T-cell leukemia/lymphoma

成人T細胞白血病リンパ腫（ATL）におけるゲノム情報と臨床情報を統合したリスクモデルを確立 --ATLの個別化医療を推進--. 京都大学プレスリリース. 2023-04-10.The prognosis of aggressive adult T-cell leukemia/lymphoma (ATL) is poor, and allogeneic hematopoietic stem-cell transplantation (allo-HSCT) is a curative treatment. To identify favorable prognostic patients after intensive chemotherapy, and who therefore might not require upfront allo-HSCT, we aimed to improve risk stratification of aggressive ATL patients aged <70 years. The clinical risk factors and genetic mutations were incorporated into risk modeling for overall survival (OS). We generated the m7-ATLPI, a clinicogenetic risk model for OS, that included the ATL prognostic index (PI) (ATL-PI) risk category, and non-silent mutations in seven genes, namely TP53, IRF4, RHOA, PRKCB, CARD11, CCR7, and GATA3. In the training cohort of 99 patients, the m7-ATLPI identified a low-, intermediate-, and high-risk group with 2-year OS of 100%, 43%, and 19%, respectively (hazard ratio [HR] 5.46, p < 0.0001). The m7-ATLPI achieved superior risk stratification compared to the current ATL-PI (C-index 0.92 vs. 0.85, respectively). In the validation cohort of 84 patients, the m7-ATLPI defined low-, intermediate-, and high-risk groups with a 2-year OS of 81%, 30%, and 0%, respectively (HR 2.33, p = 0.0094), and the model again outperformed the ATL-PI (C-index 0.72 vs. 0.70, respectively). The simplified m7-ATLPI, which is easier to use in clinical practice, achieved superior risk stratification compared to the ATL-PI, as did the original m7-ATLPI; the simplified version was calculated by summing the following: high-risk ATL-PI category (+10), low-risk ATL-PI category (−4), and non-silent mutations in TP53 (+4), IRF4 (+3), RHOA (+1), PRKCB (+1), CARD11 (+0.5), CCR7 (−2), and GATA3 (−3)

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements