Characterizing Geothermal Surface Manifestation Based on Multivariate Geostatistics of Ground Measurements Data

5th ITB International Geothermal Workshop

Ishaq^1 ; Heriawan, Mohamad Nur^1 ; Saepuloh, Asep^2

Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology (ITB), Jl. Ganesha No. 10, West Java, Bandung

40132, Indonesia^1

Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB), Jl. Ganesha No. 10, West Java, Bandung

40132, Indonesia^2

关键词: Co-Kriging;    Linear modeling;    Multivariate geostatistics;    Root mean square errors;    Surface manifestations;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/42/1/012025/pdf
DOI  :  10.1088/1755-1315/42/1/012025

来源: IOP

【 摘 要 】

Mt. Wayang Windu is one of geothermal field located in West Java, Indonesia. The characterization of steam spots at surface manifestation zones based on the soil physical measurements of the area is presented in this study. The multivariate geostatistical methods incorporating the soil physical parameter data were used to characterize the zonation of geothermal surface manifestations. The purpose of this study is to evaluate the performance of spatial estimation method of multivariate geostatistics using Ordinary Cokriging (COK) to characterize the physical properties of geothermal surface manifestations at Mt. Wayang Windu. The COK method was selected because this method is favorable when the secondary variables has more number than the primary variables. There are four soil physical parameters used as the basis of COK method, i.e. Electrical Conductivity, Susceptibility, pH, and Temperature. The parameters were measured directly at and around geothermal surface manifestations including hot springs, fumaroles, and craters. Each location of surface manifestations was measured about 30 points with 30 x 30 m grids. The measurement results were analyzed by descriptive statistics to identify at the nature of data. The correlation among variables was analyzed using linear regression. When the correlation coefficient among variables is higher, the estimation results is expected to have better Linear Coregionalization Model (LCM). LCM was used to analyze the spatial correlation of each variable based on their variogram and cross-variogram model. In oder to evaluate the performance of multivariate geostatistical using COK method, a Root Mean Square Error (RMSE) was performed. Estimation result using COK method is well applicable for characterizing the surface physics parameters of radar images data.