斯特隆博利火山三十年地面变形监测。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Valentina Bruno, Bellina Di Lieto, Salvatore Gambino, Mario Mattia, Danilo Messina, Laura Privitera, Pierdomenico Romano, Massimo Rossi
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引用次数: 0

摘要

自1992年以来,INGV收集了倾斜、全球导航卫星系统(GNSS)和应变仪数据,用于监测斯特隆博利火山的喷发和爆炸活动。在这段时间内,发生了与喷发活动、阵发性爆炸、膨胀/收缩周期和许多其他火山学和地球物理感兴趣的现象有关的地面变形。来自永久大地测量网的数据集跨越1992/2022年期间,采样率可变,取决于单个仪器:从GNSS站的每日3D坐标到应变仪的1 Hz数据。GNSS、倾斜和应变仪数据的时间序列涵盖了在斯特隆博利观测到的火山和构造事件的整个光谱。还提供了gnss导出的速度场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Thirty years of ground deformation monitoring at Stromboli volcano.

Thirty years of ground deformation monitoring at Stromboli volcano.

Thirty years of ground deformation monitoring at Stromboli volcano.

Thirty years of ground deformation monitoring at Stromboli volcano.

Since 1992, INGV has collected tilt, GNSS (Global Navigation Satellite System) and strainmeter data, for the monitoring of effusive and explosive activity of Stromboli volcano. In this timespan, ground deformations related to eruptive activity, paroxystic explosions, cycles of inflation/deflation and many other phenomena of volcanological and geophysical interest have occurred. The dataset from the permanent geodetic networks spans the period 1992/2022 with a variable sampling rate, depending on the individual instrument: from the daily 3D coordinates of GNSS stations to the 1 Hz data of the strainmeter. The time series of GNSS, tilt and strainmeters data cover the entire spectrum of the volcanic and tectonic events observed at Stromboli. A GNSS-derived velocity field is also provided.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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