Insufficient site parameterization remains a major stumbling block for efficient and reliable prediction of flow and transport in a subsurface environment. The lack of sufficient parameter data is usually dealt with by treating relevant parameters as random fields, which enables one to employ various geostatistical and stochastic tools. The major conceptual difficulty with these techniques is that they rely on the ergodicity hypothesis to interchange spatial and ensemble statistics. Instead of treating deterministic material properties as random, we introduce tools from machine learning to deal with the sparsity of data. To demonstrate the relevance and advantages of this approach, we apply one of these tools, the Support Vector Machine, to delineate geologic facies from hydraulic conductivity data.