Search

- An algorithm recently developed by RUNDLEet al. (2002) to find regions of anomalous seismic activity associated with large earthquakes identified the location of anM w = 5.6 earthquake near Calexico, Mexico. In this paper we analyze the regional seismicity before this event, and a nearbyM w = 5.7 event, using time-to-failure algorithms developed by BOWMANet al. (1998) and BOWMAN and KING (2001a,b). The former finds the radius of a circular region surrounding the epicenter that optimizes the time-to-failure acceleration of seismic release. The latter optimizes acceleration based on the expected stress accumulation pattern for a dislocation source. Both methods found a period of accelerating seismicity in an optimal region, the size of which agrees with previously proposed scaling relations. This positive result suggests that the Rundle algorithm may provide a useful technique to identify regions of accelerating seismicity, which can then be analyzed using signal optimization time-to-failure techniques. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to discuss the statistical distributions of recurrence times of earthquakes. Recurrence times are the time intervals between successive earthquakes at a specified location on a specified fault. Although a number of statistical distributions have been proposed for recurrence times, we argue in favor of the Weibull distribution. The Weibull distribution is the only distribution that has a scale-invariant hazard function. We consider three sets of characteristic earthquakes on the San Andreas fault: (1) The Parkfield earthquakes, (2) the sequence of earthquakes identified by paleoseismic studies at the Wrightwood site, and (3) an example of a sequence of micro-repeating earthquakes at a site near San Juan Bautista. In each case we make a comparison with the applicable Weibull distribution. The number of earthquakes in each of these sequences is too small to make definitive conclusions. To overcome this difficulty we consider a sequence of earthquakes obtained from a one million year “Virtual California” simulation of San Andreas earthquakes. Very good agreement with a Weibull distribution is found. We also obtain recurrence statistics for two other model studies. The first is a modified forest-fire model and the second is a slider-block model. In both cases good agreements with Weibull distributions are obtained. Our conclusion is that the Weibull distribution is the preferred distribution for estimating the risk of future earthquakes on the San Andreas fault and elsewhere. [ABSTRACT FROM AUTHOR]

Modern earthquake ground motion hazard mapping in California began following the 1971 San Fernando earthquake in the Los Angeles metropolitan area of southern California. Earthquake hazard assessment followed a traditional approach, later called Deterministic Seismic Hazard Analysis (DSHA) in order to distinguish it from the newer Probabilistic Seismic Hazard Analysis (PSHA). In DSHA, seismic hazard in the event of the Maximum Credible Earthquake (MCE) magnitude from each of the known seismogenic faults within and near the state are assessed. The likely occurrence of the MCE has been assumed qualitatively by using late Quaternary and younger faults that are presumed to be seismogenic, but not when or within what time intervals MCE may occur. MCE is the largest or upper-bound potential earthquake in moment magnitude, and it supersedes and automatically considers all other possible earthquakes on that fault. That moment magnitude is used for estimating ground motions by applying it to empirical attenuation relationships, and for calculating ground motions as in neo-DSHA (Z uccolo et al., ). The first deterministic California earthquake hazard map was published in 1974 by the California Division of Mines and Geology (CDMG) which has been called the California Geological Survey (CGS) since 2002, using the best available fault information and ground motion attenuation relationships at that time. The California Department of Transportation (Caltrans) later assumed responsibility for printing the refined and updated peak acceleration contour maps which were heavily utilized by geologists, seismologists, and engineers for many years. Some engineers involved in the siting process of large important projects, for example, dams and nuclear power plants, continued to challenge the map(s). The second edition map was completed in 1985 incorporating more faults, improving MCE's estimation method, and using new ground motion attenuation relationships from the latest published results at that time. CDMG eventually published the second edition map in 1992 following the Governor's Board of Inquiry on the 1989 Loma Prieta earthquake and at the demand of Caltrans. The third edition map was published by Caltrans in 1996 utilizing GIS technology to manage data that includes a simplified three-dimension geometry of faults and to facilitate efficient corrections and revisions of data and the map. The spatial relationship of fault hazards with highways, bridges or any other attribute can be efficiently managed and analyzed now in GIS at Caltrans. There has been great confidence in using DSHA in bridge engineering and other applications in California, and it can be confidently applied in any other earthquake-prone region. Earthquake hazards defined by DSHA are: (1) transparent and stable with robust MCE moment magnitudes; (2) flexible in their application to design considerations; (3) can easily incorporate advances in ground motion simulations; and (4) economical. DSHA and neo-DSHA have the same approach and applicability. The accuracy of DSHA has proven to be quite reasonable for practical applications within engineering design and always done with professional judgment. In the final analysis, DSHA is a reality-check for public safety and PSHA results. Although PSHA has been acclaimed as a better approach for seismic hazard assessment, it is DSHA, not PSHA, that has actually been used in seismic hazard assessment for building and bridge engineering, particularly in California. [ABSTRACT FROM AUTHOR]

In weather forecasting, current and past observational data are routinely assimilated into numerical simulations to produce ensemble forecasts of future events in a process termed “model steering”. Here we describe a similar approach that is motivated by analyses of previous forecasts of the Working Group on California Earthquake Probabilities (WGCEP). Our approach is adapted to the problem of earthquake forecasting using topologically realistic numerical simulations for the strike-slip fault system in California. By systematically comparing simulation data to observed paleoseismic data, a series of spatial probability density functions (PDFs) can be computed that describe the probable locations of future large earthquakes. We develop this approach and show examples of PDFs associated with magnitude M > 6.5 and M > 7.0 earthquakes in California. [ABSTRACT FROM AUTHOR]

We present estimates of future earthquake rate density (probability per unit area, time, and magnitude) on a 0.1-degree grid for a region including California and Nevada, based only on data from past earthquakes. Our long-term forecast is not explicitly time-dependent, but it can be updated at any time to incorporate information from recent earthquakes. The present version, founded on several decades worth of data, is suitable for testing without updating over a five-year period as part of the experiment conducted by the Collaboratory for Study of Earthquake Predictability (CSEP). The short-term forecast is meant to be updated daily and tested against similar models by CSEP. The short-term forecast includes a fraction of our long-term one plus time-dependent contributions from all previous earthquakes. Those contributions decrease with time according to the Omori law: proportional to the reciprocal of the elapsed time. Both forecasts estimate rate density using a radially symmetric spatial smoothing kernel decreasing approximately as the reciprocal of the square of epicentral distance, weighted according to the magnitude of each past earthquake. We made two versions of both the long- and short-term forecasts, based on the Advanced National Seismic System (ANSS) and Preliminary Determinations of Epicenters (PDE) catalogs, respectively. The two versions are quite consistent, but for testing purposes we prefer those based on the ANSS catalog since it covers a longer time interval, is complete to a lower magnitude threshold and has more precise locations. Both forecasts apply to shallow earthquakes only (depth 25 km or less) and assume a tapered Gutenberg-Richter magnitude distribution extending to a lower threshold of 4.0. [ABSTRACT FROM AUTHOR]

In 1976 Frank Evison identified the first examples of earthquake swarms as long-term precursors of main-shock events, and thereby discovered the predictive scaling relations of long-term seismogenesis. From this time on, forecasting became the main focus of his research. After learning from an early attempt to communicate forecasts confidentially to government, he recognised the importance of hypothesis testing, and the precursory swarm hypothesis was cast in a form similar to a regional likelihood model. Tests of its performance relative to a stationary Poisson model at M ≥ 5.8 in New Zealand were begun in 1977. The initial hypothesis was that of a 1–1 relation between swarms and main-shock events. Following a study of the Japan catalogue, the generalised swarm hypothesis, in which multiple swarms were precursory to multiple main-shock events, was formulated. Tests of this form of the hypothesis at M ≥ 6.8 were initiated in a region of surveillance east of Japan in 1983. Eventually the generalised hypothesis was adopted in New Zealand also. In 1999, tests were begun in a region of Greece. In 1994–1995, several main-shock events favourable to the swarm hypothesis occurred, however four main-shock events near Arthur’s Pass, New Zealand, occurred without precursory swarms. Subsequent analysis showed that events called “quarms”, which were similar to swarms but more protracted in time, had preceded these events. This led to the proposal of a qualitative physical process to account for swarms, quarms and the predictive relations: A three-stage faulting process, in which a major crack induces aftercracks in its neighbourhood, just as a main shock induces aftershocks. An inference from this process was that the most general long-term precursor should be an increase of seismicity at similar magnitudes to the eventual aftershocks. It turned out that such a precursory scale increase nearly always occurs before major earthquakes and conforms to the predictive scaling relations. Setting aside the problem of identifying the scale increase before the major earthquake, the EEPAS (Every Earthquake a Precursor According to Scale) forecasting model was formulated. The success of this relatively weak model in forecasting major events in New Zealand, California, Japan and Greece shows that the predictive scaling relations are ubiquitous in earthquake catalogues. Although none of the formal tests of the swarm hypothesis were successful in their own terms, they were beneficial in identifying shortcomings in its formulation, thereby leading to improved understanding of long-term seismogenesis and a better forecasting model. Some puzzling aspects of the scaling relations are whether they vary regionally, and why the precursor area and aftershock area scale differently with magnitude. A more practical question is whether the EEPAS model can be strengthened, by making use of the clustering of some precursors in swarms and quarms, to bring us nearer to the original goal of forecasting individual major earthquakes. [ABSTRACT FROM AUTHOR]

- Simulation of the complete earthquake generation process requires assimilation of observational data on long-term behavior of faults over multiple rupture cycles. Paleoseismology, the study of pre-instrumental earthquakes from geological field investigations, provides the only observations of long-term fault behavior. Paleoseismic data present challenges for assimilation into models because data sets are small, sparse, analog, and contain qualitative uncertainties. Observations can be categorized as primary measurements from field data, or non-primary extrapolations or interpretations of primary data. Assimilation of non-primary data could yield biased simulation results. We present a primary data set for the San Andreas and Imperial faults for comparison with non-primary data in existing databases, and propose standard formats and structure for paleoseismic data assimilation into numerical simulation models . [ABSTRACT FROM AUTHOR]

Scaling relations previously derived from examples of the precursory scale increase before major earthquakes show that the precursor is a long-term predictor of the time, magnitude, and location of the major earthquake. These relations are here taken as the basis of a stochastic forecasting model in which every earthquake is regarded as a precursor. The problem of identifying those earthquakes that are actually precursory is thus set aside, at the cost of limiting the strength of the resulting forecast. The contribution of an individual earthquake to the future distribution of hazard in time, magnitude and location is on a scale determined, through the scaling relations, by its magnitude. Provision is made for a contribution to be affected by other earthquakes close in time and location, e.g., an aftershock may be given low weight. Using the New Zealand catalogue, the model has been fitted to the forecasting of shallow earthquakes exceeding magnitude 5.75 over the period 1965–2000. It fits the data much better than a baseline Poisson model with a location distribution based on proximity to the epicenters of past earthquakes. Further, the model has been applied, with unchanged parameters, to the California region over the period 1975–2001. There also, it performs much better than the baseline model fitted to the same region over the period 1951–1974; the likelihood ratio is 1015 in favor of the present model. These results lend credence to the precursory scale increase phenomenon, and show that the scaling relations are pervasive in earthquake catalogues. The forecasting model provides a new baseline model against which future refinements, and other proposed models, can be tested. It may also prove to be useful in practice. Its applicability to other regions has still to be established. [ABSTRACT FROM AUTHOR]