Los Alamos National Laboratory
Phone| Search
T-7 HomeResearchHighlights › SVM Subsurface Imaging
› Contact › People › Research
› Projects › Highlights
› Publications
› LANL/DOE AMR › Summer Programs › Jobs › Visitor Info

Subsurface Imaging with Support Vector Machines

Brendt Wohlberg
Daniel Tartakovsky
Alberto Guadagnini

A typical subsurface environment is heterogeneous, consists of multiple materials, and is often insufficiently characterized by data. The ability to delineate geologic facies and to estimate their properties from sparse data is essential for modeling physical and biochemical processes occurring in the subsurface. Geostatistics has become an invaluable tool for estimating such properties at points in a computational domain where data are not available, as well as for quantifying the corresponding uncertainty. We have recently examined an alternative approach to the problem of facies delineation that uses a pattern classification technique known as the Support Vector Machine (SVM).