Locating sources in a dense array through network-based clustering
Locating sources in a dense array through network-based clustering
复制标题
通过基于网络的聚类在密集阵列中定位源
DOI：
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发表时间：
2016
期刊：
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通讯作者：
P. Gerstoft
中科院分区：
文献类型：
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作者：
N. Riahi;P. Gerstoft
提出了一种利用网络方法识别密集传感器阵列中弱源的非参数方法。除了信号强度在比孔径短的尺度内衰减到微不足道的水平之外，不需要关于传播介质的知识。然后，我们重新解释的波场的空间相干矩阵作为一个矩阵，其支持是一个网络的顶点（传感器）连接到社区的连接矩阵。在渐近的情况下，这些社区对应于与各个源相关联的传感器集群。相干矩阵的支持估计从有限的时间数据使用一个强大的假设检验结合物理距离标准。后者确保了足够的网络稀疏性，以防止网络社区的偶然形成。我们验证了模拟数据的方法，并量化其可靠性。然后将该方法应用于来自覆盖长滩（CA）市7 × 10 km的密集5200元件地震检波器阵列的数据。分析显示，一架直升机穿过阵列和石油生产设施。
A non-parametric technique to identify weak sources within dense sensor arrays is developed using a network approach. 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 network of vertices (sensors) connected into communities. In the asymptotic case these communities correspond to sensor clusters associated with individual sources. The support of the coherence matrix is estimated from limited-time data using a robust hypothesis test combined with a physical distance criterion. The latter ensures sufficient network sparsity to prevent network communities from forming by chance. We verify the approach on simulated data and quantify its reliability. The method is then applied to data from a dense 5200 element geophone array that blanketed 7 χ 10 km of the city of Long Beach (CA). The analysis exposes a helicopter traversing the array and oil production facilities.