A multivariate statistical approach is presented to estimate water saturation in shallow heterogeneous formations. An improved factor analysis algorithm is developed to process engineering geophysical sounding data in a more reliable way. Resistivity and nuclear data acquired by cone penetration tools equipped with geophysical sensors are processed simultaneously to give an estimate to factor logs. The new factor analysis procedure is based on the iterative reweighting of data prediction errors using the highly robust most frequent value method, which improves the accuracy of factor scores in case of non-Gaussian data sets. A strong exponential relationship is detected between water saturation and the first factor log. Tests made on penetration logs measured from a Hungarian well demonstrate the feasibility of the most frequent value based factor analysis approach, which is verified by the results of local inverse modeling.


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