|Title||Using graph clustering to locate sources within a dense sensor array|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Riahi N., Gerstoft P|
|Type of Article||Article|
|Keywords||boundary; california; estimation; field; Graph clustering; long beach; noise; Non-parametric; Seismic arrays; source localization|
We develop a model-free technique to identify weak sources within dense sensor arrays using graph clustering No knowledge about the propagation medium is needed except that signal strengths decay to insignificant level! within a scale that is shorter than the aperture. We then reinterpret the spatial coherence matrix of a wave field as a matrix whose support is a connectivity matrix of a graph with sensors as vertices. In a dense network, well separated sources induce clusters in this graph. The geographic spread of these clusters can serve to localize the. sources. The support of the covariance matrix is estimated from limited-time data using a hypothesis test with robust phase-only coherence test statistic combined with a physical distance criterion. The latter criterion ensures graph sparsity and thus prevents clusters from forming by chance. We verify the approach and quantify its reliability on a simulated dataset. The method is then applied to data from a dense 5200 element geophone array that blanketed 7 km x 10 km of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array and oil production facilities.