No abstract

No abstract

Regression or regression-like models are often employed in potential modeling, i.e., for the targeting of resources, either based on 2D map images or 3D geomodels both in raster mode or based on spatial point processes. Recently, machine learning techniques such as artificial neural networks have gained popularity also in potential modeling. Using artificial neural networks, decent results in the prediction of the target event are obtained. However, insight into the problem, e.g., about importance of specific covariables, is difficult to obtain. On the other hand, logistic regression has a well understood statistical foundation and works with an explicit model from which knowledge can be gained about the underlying problem. However, establishing such an explicit model is rather difficult for real world problems. We propose a model selection strategy for logistic regression, which includes nonlinearities for improved classification results. At the same time, interpretability of the results is preserved.

No abstract