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Title:Statistična analiza pogostosti potresov na svetovni ravni : magistrska naloga
Authors:ID Žagar, Jernej (Author)
ID Levnajić, Zoran (Mentor) More about this mentor... New window
Files:.pdf MAG_2023_Jernej_Zagar.pdf (3,83 MB)
MD5: F49D9898F04D6EE64F1F089A3F3CE948
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FIŠ - Faculty of Information Studies in Novo mesto
Abstract:Magistrska naloga vsebuje statistično analizo pogostosti potresov v svetovnem merilu. V nalogi predstavimo zbiranje podatkov o potresnih dejavnostih, ki se nahajajo na spletni strani ameriške znanstvene agencije. Z uporabo programskega jezika Python zbrane podatke očistimo, jih analiziramo in predstavimo grafično. Z uporabo knjižnice GeoPandas posamezne lokacije potresov prestavimo tudi na zemljevidu. Za vodilo analize uporabimo zastavljene hipoteze s katerimi skušamo preveriti korelacijo med magnitudo, globino, posamezno lokacijo in časovno komponento potresov. Pri tem se osredotočimo na potresno zanimive lokacije, obravnavamo tudi Slovenijo.
Keywords:potresi, seizmologija, analiza velikih količin podatkov, magnituda, globina, gručenje
Place of publishing:Novo mesto
Place of performance:Novo mesto
Publisher:[J. Žagar]
Year of publishing:2023
Year of performance:2023
Number of pages:XIII, 55 str.
PID:20.500.12556/ReVIS-9849 New window
COBISS.SI-ID:159220227 New window
UDC:519.233.5:550.34(043.2)
Note:Na ov.: Magistrska naloga : študijskega programa druge stopnje;
Publication date in ReVIS:19.07.2023
Views:1010
Downloads:88
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:19.07.2023

Secondary language

Language:English
Abstract:The master's thesis contains statistical analysis of earthquake frequency at global level. In the master's thesis we present data mining on the website of United state Geological Survey to create earthquakes database. Using the Python programming laguange, we clean data, analyze it and present it graphically. Using the GeoPandas library we present earthquakes location on world map. As a guide for analysis, we use established hypotheses to analyze correlation between the magnitude, depth, specific location and time dependancy of earthquakes. During the analysis we focus on seismically interesting locations, we also consider location of Slovenia.
Keywords:earthquakes, seismology, big data analysis, magnitude, depth, clustering


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