Investigation of the Spatial Clustering Properties of Seismic Time Series: A Comparative Study from Shallow to Intermediate‐Depth Earthquakes

Abstract

In this paper, a size-independent modification of the general detrended fluctuation analysis (DFA) method is introduced. With this modified DFA, seismic time series (m≥4.5) pertaining to most seismically active regions of the world from the year 1972 up to the year 2016 are comparatively analyzed. An eminent homogeneity of spatial clustering behaviors in worldwide range is detected and DFA scaling exponents coincide with previous results for local regions. Furthermore, universal nontrivial spatial clustering behaviors are revealed from shallow to intermediate-depth earthquakes by varying the depth of the hypocenter: in shallow earthquakes, the depth range corresponding to the minimum spatial clustering is first verified to be spatiotemporal and identical; in intermediate-depth earthquakes, strong weakening long-range correlations compared to shallow earthquakes are unveiled to strengthen the recent findings in seismology. Our work aims to suggest a possible statistical approach to explore the dynamic mechanisms implied in the seismicity as well as in other analogous dynamic evolution processes.

Memory Effect in the Spatial Series Based on Diamond and Graphite Crystals

“…Here, the objects of inquiry can be time or spatial series formed during experimental observations or theoretical calculations. This approach is applied in areas such as astronomy (sun spots) [ 4 ], seismology (Earth crust vibrations) [ 5 ], physiology (electroencephalograms) [ 6 ], meteorology (weather observation data) [ 7 ], econophysics (stock quotation) [ 8 ], bioinformatics (protein dynamics) [ 9 ], and chemistry (concentration changes) [ 10 ]. The following two basic problems are solved here: the classification of series and the prediction of their behavior.…”

Ανάπτυξη Μερικώς Επιβλεπόμενων Αλγορίθμων Μηχανικής Μάθησης

“…κατανάλωση ενέργειας. Χαρακτηριστικό παράδειγμα η έρευνα στην εργασία [35] σχετικά με την μόλυνση του εδάφους, καθώς και οι εργασίες σχετικά με την ανίχνευση και κατηγοριοποίηση σεισμών [36], [37]. Ένα επίσης από τα σημαντικότερα επιτεύγματα της Μηχανικής Μάθησης στο πεδίο των Γεωεπιστημών είναι η ελάφρυνση της επέμβασης του ανθρώπινου παράγοντα, απαιτώντας έως και 100 φορές μικρότερη πληροφορία κατά την σύνθεση των δεδομένων, επιτρέποντας στους ειδικούς να αφιερώσουν περισσότερο χρόνο σε ποιοτικότερες αναλύσεις.…”