A new generation of precise gradiometer technology is currently under development and applicable to ground and airborne mapping of geologic or anthropogenic features with signal strength as low as a few Eötvös, entirely embedded in noise and geological background. With high sensitivities of future airborne gradiometers, it may be possible to detect such anomalous sources with careful data processing. Both the detection and the estimation of parameters of the feature can be solved as an inverse problem in potential theory. However, one can also use methods developed in communications theory, provided one has some a priori, possible uncertain knowledge of the feature in question. We constructed a matched filter as well as a sophisticated estimation technique to detect and characterize particular small mass anomalies within general geologic background noise using individual gradient and six gradient combination measurements at low aircraft/helicopter altitudes of ranges of 10-30m above terrain clearance. In addition, the performance of the detection and estimation procedures is quantified by standard test statistics.
With these tests, probabilities of false alarm and detection may be assigned to the detection results. We present numerical results in different noise circumstances, for instance, a simulation of airborne gradiometry over moderate terrain with the inclusion of 1E/√Hz instrumental white noise. The proposed approaches are explored and evaluated for their effectiveness in association with location, orientation, size, and depth of a mass anomaly, and in the use of power spectral density (psd) models versus empirical psd's obtained from the noise backgrounds. The numerical results show that a small anomaly, e.g., 2m x 2m x 10m, is detectable at shallow depths by an appropriate matched filter using the empirical psd's and the gradient component in vertical direction. However, the analysis shows that a strong noise level, low spatial resolution, and unknown depth limit the anomaly detectability. The parameter estimation performed through an iterative least-squares process was shown to be successful in estimating locations, orientations, and depth of the anomaly. Hypothesis testing by means of the F-test was used to quantify the performance of the estimation process.
|School:||The Ohio State University|
|Department:||Geodetic Science and Surveying|
|School Location:||United States -- Ohio|
|Source:||DAI-B 79/10(E), Dissertation Abstracts International|
|Subjects:||Geographic information science|
|Keywords:||Airborne gradiometry, Hypothesis testing, Parameter estimation, Signal detection|
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