Correlation Processing Of Local Seismic Data: Applications for Autonomous Sensor Deployments

Dodge, D A

Lawrence Livermore National Laboratory

关键词: Monitoring;    Classification;    Hypocenters;    Excavation;    Processing;   

DOI  :  10.2172/1016983
RP-ID  :  LLNL-TR-462535
RP-ID  :  W-7405-ENG-48
RP-ID  :  1016983

美国|英语

来源: UNT Digital Library

【 摘 要 】

Excavation and operation of an underground facility is likely to produce an extensive suite of seismic signals observable at the surface for perhaps several km. Probably a large fraction of such signals will be correlated, so the design of a monitoring framework should include consideration of a correlation processing capability. Correlation detectors have been shown to be significantly more sensitive than beam-forming power detectors. Although correlation detectors have a limited detection footprint, they can be generalized into multi-rank subspace detectors which are sensitive over a much larger range of source mechanisms and positions. Production of subspace detectors can be automated, so their use in an autonomous framework may be contemplated. Waveform correlation also can be used to produce very high precision phase picks which may be jointly inverted to simultaneously relocate groups of events. The relative precision of the resulting hypocenters is sufficient to visualize structural detail at a scale of less than a few tens of meters. Three possible correlation processor systems are presented. All use a subspace signal detection framework. The simplest system uses a single-component sensor and is capable of detection and classification of signals. The most complicated system uses many sensors deployed around the facility, and is capable of detection, classification, and high-precision source location. Data from a deep underground mine are presented to demonstrate the applicability of correlation processing to monitoring an underground facility. Although the source region covers an area of about 600m by 580m, all but two of the events form clusters at a threshold of 0.7. All the events could have been detected and classified by the subspace detection framework, and high-precision picks can be computed for all cluster members.