Share Email Print
cover

Proceedings Paper

Seismic data fusion anomaly detection
Author(s): Kyle Harrity; Erik Blasch; Mark Alford; Soundararajan Ezekiel; David Ferris
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Detecting anomalies in non-stationary signals has valuable applications in many fields including medicine and meteorology. These include uses such as identifying possible heart conditions from an Electrocardiography (ECG) signals or predicting earthquakes via seismographic data. Over the many choices of anomaly detection algorithms, it is important to compare possible methods. In this paper, we examine and compare two approaches to anomaly detection and see how data fusion methods may improve performance. The first approach involves using an artificial neural network (ANN) to detect anomalies in a wavelet de-noised signal. The other method uses a perspective neural network (PNN) to analyze an arbitrary number of “perspectives” or transformations of the observed signal for anomalies. Possible perspectives may include wavelet de-noising, Fourier transform, peak-filtering, etc.. In order to evaluate these techniques via signal fusion metrics, we must apply signal preprocessing techniques such as de-noising methods to the original signal and then use a neural network to find anomalies in the generated signal. From this secondary result it is possible to use data fusion techniques that can be evaluated via existing data fusion metrics for single and multiple perspectives. The result will show which anomaly detection method, according to the metrics, is better suited overall for anomaly detection applications. The method used in this study could be applied to compare other signal processing algorithms.

Paper Details

Date Published: 19 June 2014
PDF: 7 pages
Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 90890L (19 June 2014); doi: 10.1117/12.2058039
Show Author Affiliations
Kyle Harrity, Indiana Univ. of Pennsylvania (United States)
Erik Blasch, Air Force Research Lab. (United States)
Mark Alford, Air Force Research Lab. (United States)
Soundararajan Ezekiel, Indiana Univ. of Pennsylvania (United States)
David Ferris, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 9089:
Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II
Matthew F. Pellechia; Kannappan Palaniappan; Shiloh L. Dockstader; Paul B. Deignan; Peter J. Doucette; Donnie Self, Editor(s)

© SPIE. Terms of Use
Back to Top