Merge-Optimization Method of Combined Tomography of Seismic Refraction and Resistivity Data

This paper discussed a novel application called merge-optimization method that combines resistivity and seismic refraction data to provide a detailed knowledge of the studied site. This method is interesting because it is able to show strong accuracy of two geophysical imaging methods based on many of data points collected from the conducted geophysical surveys of disparate data sets based strictly on geophysical models as an aid for model integration for two-dimensional environments. The geophysical methods used are high resolution methods. The resistivity imaging used in this survey is able to resolve the subsurface condition of the studied site with low RMS error (less than 2.0%) and 0.5 metre electrodes interval. For seismic refraction method, high resolution of seismic is used for correlation with resistivity results. Geophones spacing is 1.0 metre and the total number of shot-points is 15, which provides very dense data point. The algorithms of merge-optimization have been applied to two data sets collected at the studied site. The resulting images have been proven to be successful because they satisfy the data and are geometrically similar. The regression coefficient found for conductivity-resistivity correlation is 95.2%. 1. Introduction The characterization of the subsurface requires a detailed knowledge of several properties of the composing rocks and fluids. Whereas some of these properties can be measured directly (seismic and borehole methods), other properties have to be estimated by indirect measurement methods such as resistivity, TEM, and magnetic. However, it is not uncommon that the geophysical data yield models of limited accuracy which may not contribute significantly to our understanding of the subsurface condition or may show incompatibilities. Thus, a new technique needs to be produced not only for better interpretation by geophysics but also for nongeophysical background people such as engineers and architects. The distribution of uncorrelated physical properties seems to be controlled by common subsurface attributes, when taken into account, able to improve and resolve the accuracy of the geophysical imaging results. An outstanding feature of the subsurface that is common to the geophysical data is the geometrical distribution of the physical properties which can be measured by the physical property changes. This condition of commonality can be incorporated in the process of estimation to obtain meaningful and more reliable subsurface imaging results. 2. Methodology for Merge-Optimization Method In this paper, seismic