Multifractal Characterization of Geologic Noise for Improved UXO Detection and Discrimination

摘要

Pushing sensors and algorithms to the limit to minimize the chance of overlooking unexploded ordnance (UXO) increases the chance that noise will be misidentified as signal and money wasted excavating scrap metal or chucks of magnetic rock. Approaches to improve the signal to noise ratio typically follow one of three tracks: (1) development of new sensors that are either more sensitive, or less noisy; (2) fusion of data from multiple sensors so that the chances of confounding all of the detectors simultaneously is reduced; or (3) development of computer algorithms to extract the signal(s) from the noise. In the case of wide-area surveys that use methods such as helicopter-borne magnetometry, the battle against noise has focused primarily on instrument noise (e.g., thermal noise), platform noise (e.g., magnetic noise created by the helicopter), or interference created by multiple UXO targets in cluttered settings. What has been largely ignored is the 'noise' created by the rock and soil that surrounds the buried ordnance. The goal of this pilot project was to investigate a new approach to the characterization and simulation of geologic noise using multifractal analysis that captures the scale-dependent variability arising from geologic heterogeneity in different environments. By combining geologic noise simulations with models for the geophysical signatures of UXO, the researchers aim to create synthetic datasets that can be used both to test and improve UXO discrimination algorithms developed by other researchers, and to develop more reliable estimates of the ratio of false positives to false negatives for a particular geologic environment. In this report we discuss the multifractal methodology and its application to three data sets: Isleta Pueblo, NM; Fort Ord, CA; and the Sierra Army Depot, CA.