Integration of Advanced Statistical Analysis Tools and Geophysical Modeling
Abstract
This research program has been focused on advanced technologies for detection and discrimination of military munitions. The underlying premise of the program has been that there is an inherent limitation in the information content associated with magnetometer and EMI sensors deployed for UXO cleanup. To optimize UXO classification one must integrate all available information, both within the measured data itself and within a priori knowledge one may possess. An important class of prior knowledge is represented by the sensor physics, and by placing as much physics as possible into the models and classification features, one removes the need to rely on the limited sensor data to infer such phenomenology. Statistical classifiers are also required to maximize the information extracted from the measured data to infer the unknown model parameters. Further, the statistical classifiers may be used to appropriately exploit other forms of information inherent to the data. For example, while performing classification one may exploit the contextual information provided by all of the unlabeled data at a given site, while also appropriately leveraging related information in data measured at previous sites.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2012
- Accession Number
- ADA571858
Entities
People
- Douglas Oldenburg
- Laurens Beran
- Lawrence Carin
- Leonard Pasion
- Stephen Billings
Organizations
- Duke University