Perspectives on Clustering and Declustering of Earthquakes

Clustering is a fundamental feature of earthquakes that impacts basic and applied analyses of seismicity. Events included in the existing short-duration instrumental catalogs are concentrated strongly within a very small fraction of the space–time volume, which is highly amplified by activity associated with the largest recorded events. The earthquakes that are included in instrumental catalogs are unlikely to be fully representative of the long-term behavior of regional seismicity. We illustrate this and other aspects of space–time earthquake clustering, and propose a quantitative clustering measure based on the receiver operating characteristic diagram. The proposed approach allows eliminating effects of marginal space and time inhomogeneities related to the geometry of the fault network and regionwide changes in earthquake rates, and quantifying coupled space–time variations that include aftershocks, swarms, and other forms of clusters. The proposed measure is used to quantify and compare earthquake clustering in southern California, western United States, central and eastern United States, Alaska, Japan, and epidemic-type aftershock sequence model results. All examined cases show a high degree of coupled space–time clustering, with the marginal space clustering dominating the marginal time clustering. Declustering earthquake catalogs can help clarify long-term aspects of regional seismicity and increase the signal-to-noise ratio of effects that are subtler than the strong clustering signatures. We illustrate how the high coupled space–time clustering can be decreased or removed using a data-adaptive parsimonious nearest-neighbor declustering approach, and emphasize basic unresolved issues on the proper outcome and quality metrics of declustering. At present, declustering remains an exploratory tool, rather than a rigorous optimization problem, and selecting an appropriate declustering method should depend on the data and problem at hand.

Earthquake productivity law

SUMMARY Mechanisms of stress transfer and probabilistic models have been widely investigated to explain earthquake clustering features. However, these approaches are still far from being able to link individual events and to determine the number of earthquakes caused by a single event. An alternative approach based on proximity functions allows us to generate hierarchical clustering trees and to identify pairs of nearest-neighbours between consecutive levels of hierarchy. Then, the productivity of an earthquake is the number of events of the next level to which it is linked. Using a relative magnitude threshold ΔM to account for scale invariance in the triggering process, we show that the ΔM-productivity attached to each event is a random variable that follows an exponential distribution. The exponential rate of this distribution does not depend on the magnitude of triggering events and systematically decreases with depth. These results could now be used to characterize active fault systems and improve epidemic models of seismicity.

Quantitative analyses of background seismicity and earthquake clustering in extensional seismogenic settings - case studies from the Central-Southern Apennines of Italy

<p>The Central-Southern Apennines of Italy are a region with high seismic risk zone and experienced destructive earthquakes both in historical and in instrumental time. Geological data and historical seismicity indicate that the fault structures in this area are able to produce earthquakes with magnitude greater than 6.5. In fact the sector, stretching between the Irpinia 1980 (Mw 6.9) and the Accumuli-Visso-Norcia 2016 (Mw 6.5) seismic sequences, was struck by eleven events (MW ≥ 6.5) among the largest historical and early-instrumental earthquakes  since 1349. On the contrary, if we exclude the Barrea seismic sequence occurred in 1984 (Mw 5.9), the instrumental catalogue shows that this area is predominantly characterized by a low background level of seismicity and by earthquake clustering characterized by low release of strain energy.</p><p>We analyzed the seismicity occurring in this area from 1985 to 2018 (0.0 ≤ ML ≤ 5.0)  and  by a declustering algorithm  we indentified a set of 45 spatio-temporal clusters where the earthquake number stem out significantly from the background seismicity rate. The background seismicity (6196 events, 0.0≤ML≤4.1) is characterized by a b value of 0.96 ± 0.4, a magnitude of completeness of 1.4 and it is strictly controlled by known fault patterns. The earthquake clusters accounts for a non-negligible (45%) part of the total seismicity. A close inspection to the individual clusters allowed us to identify 4 seismic sequences characterized by isolated mainshock-aftershocks behaviour and 41 tectonic earthquake swarms (TESs). TESs have a duration ranging 2-12 days, 2.5-3.0 characteristic magnitude and 1.2 km/d migration rate. They are constituted by mono and/or polyphase episodes and they do not show a spatial complementary along the system of activated fault rather they are often spatially overlaid occupying the same fault segment. The latter behavior seems to indicate TES occurrences be driven by an underlying transients loading of the fault faster than the few mm/yr long-term extension active along the Apennines chain. The best candidates to explain these transients are likely presence of pressurized fluid abundant and/or  possible small scale creeping. The focal mechanisms and the depth of foci well correlate with the mapped normal fault systems and TESs illuminate regions of these faults adjoining ruptures of past large earthquakes. The spatio-temporal distribution of TESs suggests that the system of faults in the southern and central Apennines is characterized by heterogeneous rheology where small fault patches systematically release strain through TESs and other parts are to date locked. These findings are of great importance to better improve models for the assessment of seismic risk in the area.</p>