Abstract | ||
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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 levels 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 a 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 7km10km of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array and oil production facilities. |
Year | DOI | Venue |
---|---|---|
2017 | 10.1016/j.sigpro.2016.10.001 | Signal Processing |
Keywords | Field | DocType |
Seismic arrays,Source localization,Graph clustering,Non-parametric estimation | Data mining,Mathematical optimization,Test statistic,Geophone,Matrix (mathematics),Sensor array,Algorithm,Coherence (physics),Distance matrix,Covariance matrix,Clustering coefficient,Mathematics | Journal |
Volume | Issue | ISSN |
132 | C | 0165-1684 |
Citations | PageRank | References |
2 | 0.45 | 6 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Nima Riahi | 1 | 2 | 0.79 |
Peter Gerstoft | 2 | 86 | 22.34 |