A nondisruptive reliability approach to assess the health of microseismic sensing networks

D. Neira, G. Soto, J. Fontbona, J. Prado and S. Gaete

Applied Stochastic Models in Business and Industry, 2018, vol. 34, issue 3, 261-277

Abstract: Microseismic sensing networks are important tools for the assessment and control of geomechanical hazards in underground mining operations. In such a setting, the maintenance of a healthy network, that is, one that accurately registers all microseisms above some minimum energy level with acceptable levels of noise, is crucially relevant. In this paper, we develop a nondisruptive method to monitor the health of such a network, by associating with each sensor a set of performance indexes, inspired from reliability engineering, which are estimated from the set of registered signals. Our method addresses 2 relevant features of each of the sensors' behavior, namely, what type of noise is or might be affecting the registering process, and how effective at registering microseisms the sensor is. The method is evaluated through a case study with microseismic data registered at the Chilean underground mine El Teniente. This study illustrates our method's capability to discriminate and rank sensors with satisfactory, poor, or defective sensing performances, as well as to characterize their failure profile or type, an information that can be used to plan or optimize the network maintenance procedures.

Date: 2018
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https://doi.org/10.1002/asmb.2266

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:34:y:2018:i:3:p:261-277

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