Abstract:S U M M A R YThe stable estimation of multifractal characteristics of seismicity is considered. The data are world and accessible regional catalogues of m ≥ 2-4 events. Our attention is focused on the range of scales in which the Renyi functionals admit of scale-invariant behaviour. We find that the stable fractal analysis of hypocentres is generally difficult. As to epicentres, we have carried out a stable analysis for seven regions worldwide in the range of scales 1-1.7 decades. The estimates of generalized … Show more
“…The algorithm is completely parameterized by the triplet ( b , d f , η 0 ) whose values are estimated from the observations, so it does not involves any ad‐hoc choices or tuning parameters. Nevertheless, there exist statistical variability in the estimation of each parameter— Marzocchi and Sandri [2003] give a review of b ‐value estimation with numerous references; the results on estimating fractal distribution of epicenters are reviewed by Harte [], Kagan [], and Molchan and Kronrod []; the estimation of the threshold η 0 is discussed by Hicks []. The results of the cluster detection might be also affected by the catalog completeness magnitude and earthquake location errors.…”
[1] We use recent results on statistical analysis of seismicity to present a robust method for comprehensive detection and analysis of earthquake clusters. The method is based on nearest-neighbor distances of events in space-time-energy domain. The method is applied to a 1981-2011 relocated seismicity catalog of southern California having 111,981 events with magnitudes m ≥ 2 and corresponding synthetic catalogs produced by the Epidemic Type Aftershock Sequence (ETAS) model. Analysis of the ETAS model demonstrates that the cluster detection results are accurate and stable with respect to (1) three numerical parameters of the method, (2) variations of the minimal reported magnitude, (3) catalog incompleteness, and (4) location errors. Application of the method to the observed catalog separates the 111,981 examined earthquakes into 41,393 statistically significant clusters comprised of foreshocks, mainshocks, and aftershocks. The results reproduce the essential known statistical properties of earthquake clusters, which provide overall support for the proposed technique. In addition, systematic analysis with our method allows us to detect several new features of seismicity that include (1) existence of a significant population of single-event clusters, (2) existence of foreshock activity in natural seismicity that exceeds expectation based on the ETAS model, and (3) dependence of all cluster properties, except area, on the magnitude difference of events from mainshocks but not on their absolute values. The classification of detected clusters into several major types, generally corresponding to singles, burst-like and swarm-like sequences, and correlations between different cluster types and geographic locations is addressed in a companion paper.
“…The algorithm is completely parameterized by the triplet ( b , d f , η 0 ) whose values are estimated from the observations, so it does not involves any ad‐hoc choices or tuning parameters. Nevertheless, there exist statistical variability in the estimation of each parameter— Marzocchi and Sandri [2003] give a review of b ‐value estimation with numerous references; the results on estimating fractal distribution of epicenters are reviewed by Harte [], Kagan [], and Molchan and Kronrod []; the estimation of the threshold η 0 is discussed by Hicks []. The results of the cluster detection might be also affected by the catalog completeness magnitude and earthquake location errors.…”
[1] We use recent results on statistical analysis of seismicity to present a robust method for comprehensive detection and analysis of earthquake clusters. The method is based on nearest-neighbor distances of events in space-time-energy domain. The method is applied to a 1981-2011 relocated seismicity catalog of southern California having 111,981 events with magnitudes m ≥ 2 and corresponding synthetic catalogs produced by the Epidemic Type Aftershock Sequence (ETAS) model. Analysis of the ETAS model demonstrates that the cluster detection results are accurate and stable with respect to (1) three numerical parameters of the method, (2) variations of the minimal reported magnitude, (3) catalog incompleteness, and (4) location errors. Application of the method to the observed catalog separates the 111,981 examined earthquakes into 41,393 statistically significant clusters comprised of foreshocks, mainshocks, and aftershocks. The results reproduce the essential known statistical properties of earthquake clusters, which provide overall support for the proposed technique. In addition, systematic analysis with our method allows us to detect several new features of seismicity that include (1) existence of a significant population of single-event clusters, (2) existence of foreshock activity in natural seismicity that exceeds expectation based on the ETAS model, and (3) dependence of all cluster properties, except area, on the magnitude difference of events from mainshocks but not on their absolute values. The classification of detected clusters into several major types, generally corresponding to singles, burst-like and swarm-like sequences, and correlations between different cluster types and geographic locations is addressed in a companion paper.
“…The multifractal structure of the interevent periods between successive earthquakes has been also explored, showing a gradual increment of multifractality prior to the major seismic activity (Dimitriu et al, 2000) and a loss of multifractality during aftershocks (Telesca and Lapenna, 2006;Zamani and AghAtabai, 2009). Remarkably, the estimates of generalized dimensions from multifractal features of interevent periods allow a tectonic interpretation (Molchan and Kronrod, 2009). It was also suggested that the combined characterization of the fractal properties of earthquake sequences and faults provides a better view of the seismic risk in a given geographic region (Henares-Romero et al, 2010).…”
“…is typical of discussions on nontriviality of similarity in physics literature [Malkai et al, 1997]. Under conditions (11) The above requirements, in particular, the stability and the condition (14), have proved rather stringent on the world and regional catalogs available at the time of [Molchan and Kronrod, 2009] work. We analyzed world seismicity to identify only six regions (southern California, M>2; Kamchatka, M>3.5; New Zealand, M>2.5; Central American arc, M>4; Costa Rica, M>3.2; Greece, M>3 ;…”
Section: Multifractality and The Parameter сmentioning
confidence: 99%
“…Unfortunately, rigorous analyses of fractality in seismicity [see e.g. Goltz, 1997;Harte, 2001;Molchan and Kronrod, 2009] are still few. Summary.…”
Section: Multifractality and The Parameter сmentioning
Recently, attempts have been made to take into account the fractal properties of seismicity when mapping the long-term rate of earthquakes. The paper touches upon the theoretical aspects of fractality and provides a critical analysis of its applications to the problems of seismic risk
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