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  5. Refined prediction of vertical gradient of gravity at Etna volcano gravity network (Italy)
 
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Refined prediction of vertical gradient of gravity at Etna volcano gravity network (Italy)

Author(s)
Zahorec, Pavol  
Division of Geophysics, Earth Science Institute, Slovak Academy of Sciences, Bratislava, Slovak Republic  
Papco, Jurai  
Department of Theoretical Geodesy, Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovak Republic  
Vajda, Peter  
Division of Geophysics, Earth Science Institute, Slovak Academy of Sciences, Bratislava, Slovak Republic  
Greco, Filippo  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OE, Catania, Italia  
Cantarero, Massimo  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OE, Catania, Italia  
Carbone, Daniele  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OE, Catania, Italia  
Language
English
Obiettivo Specifico
1IT. Reti di monitoraggio
Status
Published
JCR Journal
JCR Journal
Journal
Contributions to Geophysics and Geodesy  
Issue/vol(year)
4/48 (2018)
Pages (printed)
299-317
Date Issued
2018
DOI
10.2478/congeo-2018-0014
URI
https://www.earth-prints.org/handle/2122/12233
Subjects

topographic effect

volcano gravity monit...

building correction

DEM

Abstract
Predicted values of the vertical gradient of gravity (VGG) on benchmarks of Etna’s monitoring system, based on calculation of the topographic contribution to the theoretical free-air gradient, are compared with VGG values observed in situ. The verification campaign indicated that improvements are required when predicting the VGGs
at such networks. Our work identified the following factors to be resolved: (a) accuracy of the benchmark position; (b) gravitational effect of buildings and roadside walls adjacent to benchmarks; (c) accuracy of the digital elevation model (DEM) in the proximity of benchmarks. Benchmark positions were refined using precise geodetic methods. The gravitational effects of the benchmark-adjacent walls and buildings were modeled and accounted for in the prediction. New high-resolution DEMs were produced in the innermost zone at some benchmarks based on drone-flown photogrammetry to improve the VGG prediction at those benchmarks. The three described refinements in the VGG prediction improved the match between predicted and in situ observed VGGs at the network considerably. The standard deviation of differences between the measured and predicted VGG values decreased from 36 to 13 μ Gal/m.
Type
article
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Zahorec et al CGG 2018.pdf

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Size

11.55 MB

Format

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