Εξώφυλλο

Εφαρμογή της αντίστροφης κατανομής Gauss και της λογαριθμοκανονικής κατανομής για την μελέτη των σεισμών στην Κεντρική και Νότια Αμερική = Application of inverse Gaussian and lognormal distribution to study earthquakes in Central and South America.

Ελένη Στέργιος Μπάπκα

Περίληψη


Η Περί-Ειρηνική ζώνη αποτελεί την πλέον σεισμογενή περιοχή της Γης. Στη ζώνη αυτή βρίσκονται οι περισσότερες νέες οροσειρές, τα νησιωτικά τόξα, το σύνολο των ανδεσιτικών ηφαιστείων, η συντριπτική πλειοψηφία των σεισμών βάθους και το μεγαλύτερο μέρος των εστιών των επιφανειακών σεισμών. Η εκλυόμενη ενέργεια σε όλες τις περιοχές γύρω από τον Ειρηνικό ωκεανό αποτελεί το 77% του συνόλου της κατ’ έτος εκλυόμενης σεισμικής ενέργειας της Γης.
Στην συγκεκριμένη εργασία εξετάζονται δύο περιοχές της Περί-Ειρηνικής ζώνης, με πολύ υψηλή σεισμικότητα και σεισμού τεραστίου μεγέθους, την Κεντρική και την Νότια Αμερική. Σε ένα δείγμα μεγάλων σεισμών με μέγεθος Μw≥6.5 εφαρμόστηκε έλεγχος καλής προσαρμογής σε δύο κατανομές, με στόχο εντέλει να υπολογιστούν οι υπό συνθήκη πιθανότητες για να εκτιμηθεί η σεισμική επικινδυνότητα. Η συνολική περιοχή χωρίσθηκε σε δέκα σεισμικές ζώνες. Στις ζώνες αυτές έχουν συμβεί μεγάλοι και καταστροφικοί σεισμοί. Ο πλέον χαρακτηριστικός ο σεισμός του 1960 με μέγεθος Μw=9.5. Είναι ο μεγαλύτερος καταγραμμένος σεισμός που γνωρίζουμε. Υπό αυτές τις συνθήκες η ανάγκη για μετριασμό της σεισμικής επικινδυνότητας και του σεισμικού κινδύνου κρίνεται αναγκαία. Έγινε εφαρμογή δύο στατιστικών κατανομών στα δεδομένα: α) της λογαριθμοκανονικής και β) της αντίστροφης Gauss. Εφαρμόσθηκαν διάφορα κριτήρια για να ελεγχθεί ποια κατανομή ταίριαζε καλύτερα στους σεισμούς που επιλέχθηκαν. Όλοι οι υπολογισμοί και οι τροποποιήσεις των δεδομένων έγιναν μέσω της γλώσσας προγραμματισμού R. Αναφορικά, στους υπολογισμούς συγκαταλέγονται οι υπολογισμοί των εκτιμητών για τις δύο κατανομές, ο έλεγχος καλής προσαρμογής τόσο για το Kolmogov-Smirnov όσο και για το Anderson-Darling καθώς και οι δεσμευμένες πιθανότητες. Τελικά δημιουργήθηκε ένας οδηγός στον οποίο μπορεί κάποιος να στηριχθεί και να λάβει μέτρα για την μείωση του σεισμικού κινδύνου στις περιοχές αυτές.

The Peri-Pacific zone is the most seismic region on Earth. In this zone one can locate all the new mountain ranges, island arcs, all the Andesitic volcanoes, all the hotbeds of depth earthquakes and most of the foci of surface earthquakes. The energy released in all areas around the Pacific Ocean makes up 77% of the Earth's total annual seismic energy.
This paper examines two regions of the Peri-Pacific zone, with very high seismicity and earthquakes of enormous magnitude, Central and South America. In a sample of large earthquakes with magnitude Mw≥6.5, a goodness of fit test was applied to two distributions, in order to calculate the conditional probabilities and to eventually estimate the seismic hazard. The total area was divided into ten seismic zones. Large and destructive earthquakes have occurred in these zones.  The most characteristic being the earthquake of 1960 with magnitude Mw=9.5. It is the largest recorded earthquake known to us. Under these circumstances, the need to mitigate seismic hazard and seismic risk is deemed necessary. Two statistical distributions were applied to the data: a) the lognormal and b) the inverse Gauss. Various criteria were applied to check which distribution best suited the earthquakes selected. All calculations and modifications of the data were done through the R programming language. The calculations include the parameters calculations for the two distributions, the goodness of fit test, both Kolmogov-Smirnov and Anderson-Darling, and the conditional probabilities. Eventually, a guide was created on which one can rely and take measures to reduce seismic risk in these areas.

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Αναφορές


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Διαδικτυακές πηγές

https://en.wikipedia.org/wiki/File:Inverse_Gaussian_Probability_Densitiy_Function.svg

https://en.wikipedia.org/wiki/File:Inverse_Gaussian_Cumulative_Distribution_Function.svg

National Earthquake Information Center, (NEIC); 2011: Earthquake Hazards Program, URL: http://neic.usgs.gov/neis/epic/index.html.


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