Multiple-Time Scaling and Universal Behavior of the Earthquake Interevent Time Distribution

Bottiglieri, M.; Arcangelis, L. de; Godano, C.; Lippiello, Eugenio

doi:10.1103/physrevlett.104.158501

Cited by 49 publications

(40 citation statements)

References 35 publications

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“…While earlier approaches captured the dynamic nature of an earthquake network, they did not incorporate the characteristic properties of each particular location along the fault. Various studies have shown [4,[24][25][26]28] that the interval times between earthquake events for localized areas within a catalog have distributions not well described by a Poisson distribution [29], even within aftershock sequences [28]. This demonstrates that each area not only has its own statistical characteristics [30], but also retains a memory of its events [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the underlying complexities of earthquake dynamics and their complex spatiotemporal behavior [1,2], celebrated statistical scaling laws have emerged describing the number of events of a given magnitude (Gutenberg-Richter law) [3], the decaying rate of aftershocks after a main event (Omori law) [4][5][6], the magnitude difference between the main shock and its largest aftershock (Bath law) [7], as well as the fractal spatial occurrence of events [8][9][10][11]. Recent work has shown that scaling recurrence times according to the above laws results in the distribution collapsing onto a single curve [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Earthquake networks based on similar activity patterns

2012

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Earthquakes are a complex spatiotemporal phenomenon, the underlying mechanism for which is still not fully understood despite decades of research and analysis. We propose and develop a network approach to earthquake events. In this network, a node represents a spatial location while a link between two nodes represents similar activity patterns in the two different locations. The strength of a link is proportional to the strength of the cross correlation in activities of two nodes joined by the link. We apply our network approach to a Japanese earthquake catalog spanning the 14-year period 1985-1998. We find strong links representing large correlations between patterns in locations separated by more than 1000 kilometers, corroborating prior observations that earthquake interactions have no characteristic length scale. We find network characteristics not attributable to chance alone, including a large number of network links, high node assortativity, and strong stability over time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Earthquake networks based on similar activity patterns

2012

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“…Hence, b ≈ 1 means that the exponent γ is around γ = 1.6-1.7. The above are some of the reasons why the OFC model is considered to be the prime example [106] for a supposedly SOC system for earthquakes, but the question of whether real earthquakes are described or not by SOC models of this type, or whether other kinds of mechanisms, e.g., [107][108][109], need to be involved, still remains unsolved [82,86,100,[110][111][112][113][114].…”

Section: Olami-feder-christensen Earthquake Modelmentioning

confidence: 99%

Entropy in Natural Time and the Associated Complexity Measures

Sarlis

2017

Entropy

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Natural time is a new time domain introduced in 2001. The analysis of time series associated with a complex system in natural time may provide useful information and may reveal properties that are usually hidden when studying the system in conventional time. In this new time domain, an entropy has been defined, and complexity measures based on this entropy, as well as its value under time-reversal have been introduced and found applications in various complex systems. Here, we review these applications in the electric signals that precede rupture, e.g., earthquakes, in the analysis of electrocardiograms, as well as in global atmospheric phenomena, like the El Niño/La Niña Southern Oscillation.

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“…At present, it remains an unresolved problem to correctly understand the details of damage propagation in models of earthquakes and experimental data, e.g. the statistics of intershock sequences [28,29], and the recurrence of events and record breakings [30]. The possibility of predicting individual earthquakes remains, however, a subject with an unclear outlook [31,32,33,34].…”

Section: Big Data For World-wide and Multiscale Problemsmentioning

confidence: 99%

From human mobility to renewable energies

Raischel

Moreira

Lind

2014

Eur. Phys. J. Spec. Top.

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Abstract.We address and discuss recent trends in the analysis of big data sets, with the emphasis on studying multiscale phenomena. Applications of big data analysis in different scientific fields are described and two particular examples of multiscale phenomena are explored in more detail. The first one deals with wind power production at the scale of single wind turbines, the scale of entire wind farms and also at the scale of a whole country. Using open source data we show that the wind power production has an intermittent character at all those three scales, with implications for defining adequate strategies for stable energy production. The second example concerns the dynamics underlying human mobility, which presents different features at different scales.For that end, we analyze 12-month data of the Eduroam database within Portuguese universities, and find that, at the smallest scales, typically within a set of a few adjacent buildings, the characteristic exponents of average displacements are different from the ones found at the scale of one country or one continent. Big data: the emergence of a new paradigmIn some sense, the notion of Information Age is slowly fading from the perception of our society. For decades, national and international research has been creating impressive amounts of data, and often from various unrelated measurement sources [1]. Computational methods have been serving the needs of the scientific community and the need for higher computational power has driven the invention of more efficient computational codes and triggered the development of new hardware. Simultaneously, the internet has been instrumental in fostering international research collaborations and divulging scientific knowledge.Today, obtaining data is no longer a problem. Information is there, available for everyone at any time. Given the computational power of today's computers and clusters, even single research groups can create and store large data volumes. The challenge in today's research is more often what to do with the data we have. How to manage all the information we have in such large data sources? Which phenomena can we now study? The recent technological progress together with the new challenges naturally lead to a new paradigm in computational sciences, which is partially described by the term big data.In this paper we discuss the utility of big data for approaching the empirical study of phenomena up to now unreachable. We discuss the usage of big data in the study of two specific phenomena, that up to now were unreachable, namely wind energy production and human

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Multiple-Time Scaling and Universal Behavior of the Earthquake Interevent Time Distribution

Cited by 49 publications

References 35 publications

Earthquake networks based on similar activity patterns

Earthquake networks based on similar activity patterns

Entropy in Natural Time and the Associated Complexity Measures

From human mobility to renewable energies

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