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地震监测:一种准实时地震噪声网络监测系统。

SEISMONOISY: A Quasi-Real-Time Seismic Noise Network Monitoring System.

作者信息

Ruzza Giuseppe, Cogliano Rocco, D'Ambrosio Ciriaco, Falco Luigi, Cardinale Vincenzo, Minichiello Felice, Memmolo Antonino, Castagnozzi Angelo, De Luca Giovanni, Vicari Annamaria

机构信息

Istituto Nazionale di Geofisica e Vulcanologia, Sezione Irpinia, 83035 Grottaminarda, Italy.

出版信息

Sensors (Basel). 2024 May 28;24(11):3474. doi: 10.3390/s24113474.

Abstract

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time.

摘要

本文介绍了SEISMONOISY,这是一款应用程序,旨在通过准实时监测方法来监测整个地震网络地震噪声的时空特征和变化。实际上,我们已将开发的系统应用于监测分布在意大利境内的12个地震网络。这些网络包括国家地震台网(RSN)以及其他覆盖区域较小的区域网络。我们的噪声监测系统使用谱功率密度(PSD)和概率密度函数(PDF)方法,对每个台站在24小时周期内12小时长的地震记录进行分析,在得出考虑全球地震噪声模型的地震噪声水平指数(SNLI)后,能够推断地震台站的噪声特征。SNLI值可用于不同的应用,包括网络性能评估、运行问题识别、新装置选址以及科学研究应用(如火山监测、活动地震序列识别等)。此外,它有助于研究不同频段的主要噪声源以及背景地震噪声特征随时间的变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0854/11174729/50f059a5a88f/sensors-24-03474-g001.jpg

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