We investigate techniques for earth dam and levee health monitoring and automatic detection of anomalous events in passive seismic data. We have developed a novel data-driven workflow that uses machine learning and geophysical data collected from sensors located on the surface of the levee to identify internal erosion events. In this paper, we describe our research experiments with binary and one-class Support Vector Machines (SVMs). We used experimental data from a laboratory earth embankment (80% normal and 20% anomalies) and extracted nine spectral features from decomposed segments of the time series data. The two-class SVM with 10-fold cross validation achieved over 97% accuracy. Experiments with the one-class SVM use the top two features selected by the ReliefF algorithm and our results show that we can successfully separate normal from anomalous data observations with over 83% accuracy.
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