Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/93847
Title: An analysis of hardstone quarrying from a blast monitoring perspective
Authors: Cini, Timothy (2021)
Keywords: Stone industry and trade -- Malta
Upper Coralline limestone -- Malta
Lower Coralline limestone -- Malta
Quarries and quarrying -- Malta
Blasting -- Malta
Soils -- Vibration -- Malta
Issue Date: 2021
Citation: Cini, T. (2021). An analysis of hardstone quarrying from a blast monitoring perspective (Bachelor's dissertation).
Abstract: Quarrying for stone has been part of Maltese culture and economy for hundreds of years. Methods for the extraction of hardstone, ‘Ġebla tal-Qawwi’, have advanced over time, presently utilising explosives to blast large quantities of material en masse. Ground vibration resulting from these blasts were the main focus of this study, along with other features of blast monitoring. The aim of the study is to analyse the relation between ground vibration and explosives in quarry blasts, while also attempting to distinguish between quarry blasts and micro earthquakes. This study also looks at other features of quarry blasts and microearthquake recordings in order to provide further insight. Primary data was gathered using specialised instrumentation (Tromino) with the collaboration of quarry owners. Secondary data was collected from online sources and tabulated. A calibration graph was created and utilised to identify unreported quarry blasts and estimate the amount of ANFO used. Findings suggest that there are plausible ways to distinguish between earthquakes and micro earthquakes, using features such as frequency range, ground roll and the first P-wave polarity. These were used to identify various seismic signals which were probably unreported quarry blasts. The calibration graph was used to obtain an estimate for the amount of explosive used. It is recommended that further research focusing on aspects such as legislation and the deployment of portable seismographs in the field would help to better understand quarry blasts in the local context. Using this study as a base, Artificial Intelligence techniques can be implemented to automatically distinguish quarry blasts from microearthquakes.
Description: B.Sc. (Hons)(Melit.)
URI: https://www.um.edu.mt/library/oar/handle/123456789/93847
Appears in Collections:Dissertations - InsES - 2021

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