Abstract

This study reports experimental results of not only thermal conductivity but also compression and shear wave velocities for wide range of rock samples recovered in Korea. Total 45 rock specimens are gathered to represent the different origins, mineralogy, and density. The divided bar method is implemented to obtain thermal conductivity in steady-state and piezo-transducers at kHz ranges are used to measure wave velocities. Measured values are subjected to multiple-regression and statistical analysis with dominant factors of density, which allows constructing the correlative relationship between thermal conductivity and stiffness. Artificial Neural Network (ANN) which learns relationships between data predicts thermal conductivity of rock based on collected physical properties using nonlinear multiple-regression. The statistical approaches using optimal interpolation help understanding and extending correlated multiple geophysical properties in characterization of rocks.

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