THE APPLICATION OF THE SEMIPARAMETRIC GSTAR MODEL IN DETERMINING GAMMA-RAY LOG DATA ON SOIL LAYERS

Abstract

This research examines the semiparametric Generalized Space-Time Autoregressive (GSTAR) spacetime modeling and determines its spatial weight. In general, the spatial weights used are uniform, binary weights, and based on the distance, the result is a fixed weight. The GSTAR model is a stochastic model that takes into account its random variables. Thus, it is necessary to study the random spatial weights. This study introduced a new method to estimate the observed value of the GSTAR model semiparametric with a uniform kernel. The data involved the Gamma Ray (GR) log data on four coal drill holes. The semiparametric GSTAR modeling aimed to predict the amount of log GR in the unobserved soil layer based on the observation data information on the layer above it and its surrounding location. The results revealed that semiparametric GSTAR modeling could predict the presence of coal seams and their thickness of drill holes. The results also highlight the validity test on the out-sample data that the error in each borehole results in a small error. In addition, the error tends to approach the actual observed value at a depth of 1 meter down.