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用于地震预警系统的低成本微机电系统加速度计:意大利中部地震事件期间收集的数据集。

Low-cost MEMS accelerometers for earthquake early warning systems: A dataset collected during seismic events in central Italy.

作者信息

Esposito Marco, Marzorati Simone, Belli Alberto, Ladina Chiara, Palma Lorenzo, Calamita Carlo, Pantaleo Debora, Pierleoni Paola

机构信息

Department of Information Engineering (DII), Università Politecnica delle Marche, Ancona 60131, Italy.

Istituto Nazionale di Geofisica e Vulcanologia (INGV), Osservatorio Nazionale Terremoti, 60131 Ancona, Italy.

出版信息

Data Brief. 2024 Feb 7;53:110174. doi: 10.1016/j.dib.2024.110174. eCollection 2024 Apr.

Abstract

This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25,001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources. The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.

摘要

本文描述了一个加速度信号数据集,该数据集是在意大利中部发生地震期间从一个低成本无线传感器网络(WSN)采集的。该无线传感器网络由5个低成本传感器节点组成,每个节点都嵌入了一个ADXL355三轴MEMS加速度计,固定采样频率为250Hz。数据采集于2023年2月至2023年6月底。在此期间,几个地震序列影响了传感器网络安装的区域。从无线传感器网络采集了连续数据,然后围绕安装地点附近、意大利波伦扎市(MC)附近发生的地震事件的震源时间进行了修剪。总共选择了67个事件,其数据可在意大利国家地球物理与火山研究所(INGV)地震数据中心获取。然后,INGV的分析人员对从无线传感器网络采集的记录进行了人工标注。标注包括P波和S波的到时(当可与背景噪声区分开时),以及人工标注的相关不确定度水平。最终的数据集由328个3×25001的数组组成,每个数组都与其元数据相关联。元数据包括事件数据(震源位置、震源时间、震级、震级类型等)、记录相关数据(均值、中位数、最大和最小振幅、人工到时及到时不确定度)以及传感器特定数据(传感器名称、灵敏度和方向)。此外,还包括一个由非地震记录组成的小数据集,目的是提供仅关于噪声记录的记录,这些记录与电子噪声源和环境/人为噪声源有关。该数据集有潜力用于训练和开发针对低信噪比地震数据的机器学习或信号处理算法。此外,它对于地震研究、结构健康监测以及民用和地震工程应用中的MEMS加速度计性能研究也很有价值。

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