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We analyze nearest‐neighbor proximities of earthquakes in California based on the joint distribution (T,R) of rescaled timeTand rescaled distanceRbetween pairs of earthquakes (Zaliapin & Ben‐Zion, 2013a,https://doi.org/10.1002/jgrb.50179), using seismic catalogs from several regions and several catalogs for the San Jacinto Fault Zone (SJFZ). The study aims to identify informative modes in nearest‐neighbor diagrams beyond the general background and clustered modes, and to assess seismic catalogs derived by different methods. The results show that earthquake clusters with large and small‐to‐medium mainshocks have approximately diagonal and horizontal (T,R) distributions of the clustered mode, respectively, reflecting different triggering distances of mainshocks. Earthquakes in the creeping section of San Andreas Fault have a distinct “repeaters mode” characterized by very large rescaled timesTand very small rescaled distancesR, due to nearly identical locations of repeating events. Induced seismicity in the Geysers and Coso geothermal fields follow mostly the background mode, but with larger rescaled timesTand smaller rescaled distancesRcompared to tectonic background seismicity. We also document differences in (T,R) distributions of catalogs constructed by different techniques (analyst‐picks, template‐matching and deep‐learning) for the SJFZ, and detect a mode with very largeRand smallTin the template‐matching and deep‐learning based catalogs. This mode may reflect dynamic triggering by passing waves and/or catalog artifacts.

 

The spatial organization of deformation may provide key information about the timing of catastrophic failure in the brittle regime. In an ideal homogenous system, deformation may continually localize toward macroscopic failure, and so increasing localization unambiguously signals approaching failure. However, recent analyses demonstrate that deformation, including low‐magnitude seismicity, and fractures and strain in triaxial compression experiments, experience temporary phases of delocalization superposed on an overall trend of localization toward large failure events. To constrain the conditions that promote delocalization, we perform a series of X‐ray tomography experiments at varying confining pressures (5–20 MPa) and fluid pressures (0–10 MPa) on Westerly granite cores with varying amounts of preexisting damage. We track the spatial distribution of the strain events with the highest magnitudes of the population within a given time step. The results show that larger confining pressure promotes more dilation, and promotes greater localization of the high strain events approaching macroscopic failure. In contrast, greater amounts of preexisting damage promote delocalization. Importantly, the dilative strain experiences more systematic localization than the shear strain, and so may provide more reliable information about the timing of catastrophic failure than the shear strain.

 

We propose a theoretical modelling framework for earthquake occurrence and clustering based on a family of invariant Galton–Watson (IGW) stochastic branching processes. The IGW process is a rigorously defined approximation to imprecisely observed or incorrectly estimated earthquake clusters modelled by Galton–Watson branching processes, including the Epidemic Type Aftershock Sequence (ETAS) model. The theory of IGW processes yields explicit distributions for multiple cluster attributes, including magnitude-dependent and magnitude-independent offspring number, cluster size and cluster combinatorial depth. Analysis of the observed seismicity in southern California demonstrates that the IGW model provides a close fit to the observed earthquake clusters. The estimated IGW parameters and derived statistics are robust with respect to the catalogue lower cut-off magnitude. The proposed model facilitates analyses of multiple quantities of seismicity based on self-similar tree attributes, and may be used to assess the proximity of seismicity to criticality.

 

The spatial geometry of seismicity encodes information about loading and failure processes, as well as properties of the underlying fault structure. Traditional approaches to characterizing geometrical attributes of seismicity rely on assumed locations and geometry of fault surfaces, particularly at depth, where resolution is overall quite poor. In this study, we develop an alternative approach to quantifying geometrical properties of seismicity using techniques from anisotropic point process theory. Our approach does not require prior knowledge about the underlying fault properties. We characterize the geometrical attributes of 32 distinct seismicity regions in California and introduce a simple four class classification scheme that covers the range of geometrical properties observed. Most of the regions classified as having localized seismicity are within northern California, while nearly all of the regions classified as having distributed seismicity are within southern California. In addition, we find that roughly 1 out of 4 regions exhibit orthogonal seismicity structures. The results of this study provide a foundation for future analyses of geometrical properties of seismicity and new observables to compare with numerical modelling studies.

 

Abstract The development of new earthquake forecasting models is often motivated by one of the following complementary goals: to gain new insights into the governing physics and to produce improved forecasts quantified by objective metrics. Often, one comes at the cost of the other. Here, we propose a question-driven ensemble (QDE) modeling approach to address both goals. We first describe flexible epidemic-type aftershock sequence (ETAS) models in which we relax the assumptions of parametrically defined aftershock productivity and background earthquake rates during model calibration. Instead, both productivity and background rates are calibrated with data such that their variability is optimally represented by the model. Then we consider 64 QDE models in pseudoprospective forecasting experiments for southern California and Italy. QDE models are constructed by combining model parameters of different ingredient models, in which the rules for how to combine parameters are defined by questions about the future seismicity. The QDE models can be interpreted as models that address different questions with different ingredient models. We find that certain models best address the same issues in both regions, and that QDE models can substantially outperform the standard ETAS and all ingredient models. The best performing QDE model is obtained through the combination of models allowing flexible background seismicity and flexible aftershock productivity, respectively, in which the former parameterizes the spatial distribution of background earthquakes and the partitioning of seismicity into background events and aftershocks, and the latter is used to parameterize the spatiotemporal occurrence of aftershocks. 

Abstract We review properties and processes of earthquake rupture zones based on field studies, laboratory observations, theoretical models and simulations, with the goal of assessing the possible dominance of different processes in different parts of the rupture and validity of commonly used models. Rupture zones may be divided into front , intermediate , and tail regions that interact to different extents. The rupture front is dominated by fracturing and granulation processes and strong dilatation, producing faulting products that are reworked by subsequent sliding behind. The intermediate region sustains primarily frictional sliding with relatively high slip rates that produce appreciable stress transfer to the propagating front. The tail region further behind is characterized by low slip rates that effectively do not influence the propagating front, although it (and the intermediate region) can spawn small offspring rupture fronts. Wave-mediated stress transfer can also trigger failures ahead of the rupture front. Earthquake ruptures are often spatially discontinuous and intermittent with a hierarchy of asperity and segment sizes that radiate waves with different tensorial compositions and frequency bands. While different deformation processes dominating parts of the rupture zones can be treated effectively with existing constitutive relations, a more appropriate analysis of earthquake processes would require a model that combines aspects of fracture, damage-breakage, and frictional frameworks. 

Abstract 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.