2017
DOI: 10.1007/s11600-017-0100-3
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Incorporating space, time, and magnitude measures in a network characterization of earthquake events

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Cited by 9 publications
(6 citation statements)
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“…[35]) are best represented by models with the range of p ∈ [6 • 10 −3 , 4 • 10 −2 ] for trigger magnitudes from ν ∈ [10 −4 , 10 −3 ]. Remarkably, this range is in agreement with previous results based on simple inter-occurrence distances and times [7,30] and "recurrence" networks [31] that identified p ∈ [0.004, 0.007] as the range that best recovers the statistical properties of earthquakes from various regions.…”
supporting
confidence: 91%
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“…[35]) are best represented by models with the range of p ∈ [6 • 10 −3 , 4 • 10 −2 ] for trigger magnitudes from ν ∈ [10 −4 , 10 −3 ]. Remarkably, this range is in agreement with previous results based on simple inter-occurrence distances and times [7,30] and "recurrence" networks [31] that identified p ∈ [0.004, 0.007] as the range that best recovers the statistical properties of earthquakes from various regions.…”
supporting
confidence: 91%
“…Statistical tests based on the modal and crossover values show that the model best replicates the PH data within the range p = 10 −2 ± 4 • 10 −3 . Incidentally, this range of values have an overlap with previous works that also studied the same data sets but used simple successive event space-time characterisations [30] and spatiotemporal network approaches [31]. The seismogenic region near PH is dominated by the regime of longer η values, as shown in fig.…”
mentioning
confidence: 75%
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“…The energy accumulation in the crust take years or decades due to the very slow mechanism of crustal motion; in contrast, the energy release take a few minutes for individual quakes, and just minutes or hours for correlated activities [14,15]. This disparity in the scales of system driving and response allow the system to self-organize into a continuously critical state, which, in turn, produces the heavy-tailed distributions in space, time, energy, and network dimensions [16]. Interestingly, the approach to this statistical regularity is achieved without the need for a set of finely-tuned parameters [2].…”
Section: Introductionmentioning
confidence: 99%
“…Ζ ηδηφηεηα ηεο ειεχζεξεο θιίκαθαο κειεηήζεθε ζηελ εηζκνινγία, πξψηα, απφ ηνπο Abe θαη Suzuki (2004b) θαη ηνπο Baiesi θαη Paczuski (2004) γηα ηελ πεξηνρή ηεο λφηηαο Καιηθφξληαο. ηελ ζπλέρεηα, απφ ηνπο Baek et al (2011) γηα ηελ πεξηνρή ηεο Κνξέαο, απφ ηνλ Pastén (2016) γηα ηελ πεξηνρή ηεο Υηιήο θαη απφ ηνπο Janer et al (2017) γηα ηελ πεξηνρή ησλ Φηιηππίλσλ θαη ηεο λφηηαο Καιηθφξληαο. Dunn θαη Doeksen (1977) θαη Masters θαη Wallston (2005) νη νπνίνη κειέηεζαλ ηελ ζπζρέηηζε κεηαμχ δεκνγξαθηθψλ κεηαβιεηψλ θαη κεηαβιεηψλ πγείαο.…”
Section: αναθοπά επεςνηηικήρ δπαζηηπιόηηηαρ ζηιρ μη-ηεηπιμμένερ ιδιόηηηερ δικηύοςunclassified