Earthquake clusters in southern California I: Identification and stability

Abstract

[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.

A tentative model for the explanation of Båth law using the order parameter of seismicity in natural time

“…(1). We note that Zaliapin and Ben-Zion (Zaliapin and Ben-Zion 2013a) have observed that for earthquake sequences in southern California DM follows an almost uniform distribution in the range [0,2] with an exponential tail for larger values of DM, thus supporting the value hDMi = 1.1 which has been found (Zaliapin and Ben-Zion 2013a) in the observed seismicity of southern California. They have also identified (Zaliapin and Ben-Zion 2013b) that there exist two basic types of earthquake clustering: the burst-like sequences which are consistent with highly brittle behaviour and the swarm-like sequences consistent with mixed brittle-ductile behaviour.…”

“…The nearest-neighbour, which is called the parent, can be the parent of multiple events which are called offsprings. We should note that each possible parent-offspring pair is assigned a corresponding distance n but the nearestneighbour is defined as the pair with minimum n ij (Zaliapin et al 2008;Zaliapin and Ben-Zion 2013a). Normalizing the time and space distances by the magnitude of the parent, we have (Zaliapin et al 2008;Zaliapin and Ben-Zion 2015) …”

“…By discarding all n ij [ 10 À5 , the events in a catalogue are divided into single events and families that contain multiple events connected to their parents with nearest-neighbour distances n ij \10 À5 , i.e. strong links (Zaliapin and Ben-Zion 2013a). The largest event in a family is considered to be the mainshock.…”

“…The largest event in a family is considered to be the mainshock. The events before it are considered as foreshocks, while the events after it are considered to be the aftershocks (Zaliapin and Ben-Zion 2013a). Analysing Global CMT catalogue by employing this technique, 3144 pairs of mainshock-largest aftershock were extracted, i.e.…”

Triggering Processes in Rock Fracture

“…These triggering processes by which one earthquake induces (dynamic or static) stress changes leading to potentially multiple other earthquakes [4][5][6][7] are at the core relaxation processes [8]. A specific example of triggering are aftershocks following a large earthquake [9][10][11][12][13]. These aftershocks obey certain empirical laws that characterize, for example, their spatio-temporal distribution, as is the case for the Omori-Utsu (OU) relation [14].…”