ALBERT

Description: Journal of Physical Oceanography, Ahead of Print. 〈br/〉

Description: The Regional Earthquake Likelihood Models experiment in California tested the performance of earthquake likelihood models over a five-year period. First-order analysis showed a smoothed-seismicity model by Helmstetter et al. (2007) to be the best model. We construct optimal multiplicative hybrids involving the best individual model as a baseline and one or more conjugate models. Conjugate models are transformed using an order-preserving function. Two parameters for each conjugate model and an overall normalizing constant are fitted to optimize the hybrid model. Many two-model hybrids have an appreciable information gain (log probability gain) per earthquake relative to the best individual model. For the whole of California, the Bird and Liu (2007) Neokinema and Holliday et al. (2007) pattern informatics (PI) models both give gains close to 0.25. For southern California, the Shen et al. (2007) geodetic model gives a gain of more than 0.5, and several others give gains of about 0.2. The best three-model hybrid for the whole region has the Neokinema and PI models as conjugates. The best three-model hybrid for southern California has the Shen et al. (2007) and PI models as conjugates. The information gains of the best multiplicative hybrids are greater than those of additive hybrids constructed from the same set of models. The gains tend to be larger when the contributing models involve markedly different concepts or data. These results need to be confirmed by further prospective tests. Multiplicative hybrids will be useful for assimilating other earthquake-related observations into forecasting models and for combining forecasting models at all timescales.

Description: Assessing the completeness magnitude Mc of earthquake catalogs is an essential prerequisite for any seismicity analysis. We employ a simple model to compute Mc in space based on the proximity to seismic stations in a network. We show that a relationship of the form [IMG]/medium/1371eq1.gif" ALT="Formula "〉, with d the distance to the kth nearest seismic station, fits the observations well, k depending on the minimum number of stations being required to trigger an event declaration in a catalog. We then propose a new Mc mapping approach, the Bayesian magnitude of completeness (BMC) method, based on a two-step procedure: (1) a spatial resolution optimization to minimize spatial heterogeneities and uncertainties in Mc estimates and (2) a Bayesian approach that merges prior information about Mc based on the proximity to seismic stations with locally observed values weighted by their respective uncertainties. Contrary to the current Mc mapping procedures, the radius that defines which earthquakes to include in the local magnitude distribution is chosen according to an objective criterion, and there are no gaps in the spatial estimation of Mc. The method solely requires the coordinates of seismic stations. Here, we investigate the Taiwan Central Weather Bureau (CWB) seismic network and earthquake catalog over the period 1994-2010.

Description: We present new methods for short-term earthquake forecasting that employ space, time, and magnitude kernels to smooth seismicity. These methods are purely statistical and rely on very few assumptions about seismicity. In particular, we do not use Omori–Utsu law, and only one of our two new models assumes a Gutenberg–Richter law to model the magnitude distribution; the second model estimates the magnitude distribution nonparametrically with kernels. We employ adaptive kernels of variable bandwidths to estimate seismicity in space, time, and magnitude bins. To project rates over short time scales into the future, we simply assume persistence, that is, a constant rate over short time windows. The resulting forecasts from the two new kernel models are compared with those of the epidemic-type aftershock sequence (ETAS) model generated by Werner et al. (2011) . Although our new methods are simpler and require fewer parameters than ETAS, the obtained probability gains are surprisingly close. Nonetheless, ETAS performs significantly better in most comparisons, and the kernel model with a Gutenberg–Richter law attains larger gains than the kernel model that nonparametrically estimates the magnitude distribution. Finally, we show that combining ETAS and kernel model forecasts, by simply averaging the expected rate in each bin, can provide greater predictive skill than ETAS or the kernel models can achieve individually.

Description: We present new methods for time-independent earthquake forecasting that employ space–time kernels to smooth seismicity. The major advantage of the methods is that they do not require prior declustering of the catalog, circumventing the relatively subjective choice of a declustering algorithm. Past earthquakes are smoothed in space and time using adaptive Gaussian kernels. The bandwidths in space and time associated with each event are a decreasing function of the seismicity rate at the time and location of each earthquake. This yields a better resolution in space–time volumes of intense seismicity and a smoother density in volumes of sparse seismicity. The long-term rate in each spatial cell is then defined as the median value of the temporal history of the smoothed seismicity rate in this cell. To calibrate the model, the earthquake catalog is divided into two parts: the early part (the learning catalog) is used to estimate the model, and the latter one (the target catalog) is used to compute the likelihood of the model’s forecast. We optimize the model’s parameters by maximizing the likelihood of the target catalog. To estimate the kernel bandwidths in space and time, we compared two approaches: a coupled near-neighbor method and an iterative method based on a pilot density. We applied these methods to Californian seismicity and compared the resulting forecasts with our previous method based on spatially smoothing a declustered catalog ( Werner et al. , 2011 ). All models use small M ≥2 earthquakes to forecast the rate of larger earthquakes and use the same learning catalog. Our new preferred model slightly outperforms our previous forecast, providing a probability gain per earthquake of about 5 relative to a spatially uniform forecast.

Description: Probabilistic forecasting of earthquake-producing fault ruptures informs all major decisions aimed at reducing seismic risk and improving earthquake resilience. Earthquake forecasting models rely on two scales of hazard evolution: long-term (decades to centuries) probabilities of fault rupture, constrained by stress renewal statistics, and short-term (hours to years) probabilities of distributed seismicity, constrained by earthquake-clustering statistics. Comprehensive datasets on both hazard scales have been integrated into the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3). UCERF3 is the first model to provide self-consistent rupture probabilities over forecasting intervals from less than an hour to more than a century, and it is the first capable of evaluating the short-term hazards that result from multievent sequences of complex faulting. This article gives an overview of UCERF3, illustrates the short-term probabilities with aftershock scenarios, and draws some valuable scientific conclusions from the modeling results. In particular, seismic, geologic, and geodetic data, when combined in the UCERF3 framework, reject two types of fault-based models: long-term forecasts constrained to have local Gutenberg–Richter scaling, and short-term forecasts that lack stress relaxation by elastic rebound.

Description: We, the ongoing Working Group on California Earthquake Probabilities, present a spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3), with the goal being to represent aftershocks, induced seismicity, and otherwise triggered events as a potential basis for operational earthquake forecasting (OEF). Specifically, we add an epidemic-type aftershock sequence (ETAS) component to the previously published time-independent and long-term time-dependent forecasts. This combined model, referred to as UCERF3-ETAS, collectively represents a relaxation of segmentation assumptions, the inclusion of multifault ruptures, an elastic-rebound model for fault-based ruptures, and a state-of-the-art spatiotemporal clustering component. It also represents an attempt to merge fault-based forecasts with statistical seismology models, such that information on fault proximity, activity rate, and time since last event are considered in OEF. We describe several unanticipated challenges that were encountered, including a need for elastic rebound and characteristic magnitude–frequency distributions (MFDs) on faults, both of which are required to get realistic triggering behavior. UCERF3-ETAS produces synthetic catalogs of M ≥2.5 events, conditioned on any prior M ≥2.5 events that are input to the model. We evaluate results with respect to both long-term (1000 year) simulations as well as for 10-year time periods following a variety of hypothetical scenario mainshocks. Although the results are very plausible, they are not always consistent with the simple notion that triggering probabilities should be greater if a mainshock is located near a fault. Important factors include whether the MFD near faults includes a significant characteristic earthquake component, as well as whether large triggered events can nucleate from within the rupture zone of the mainshock. Because UCERF3-ETAS has many sources of uncertainty, as will any subsequent version or competing model, potential usefulness needs to be considered in the context of actual applications. Electronic Supplement: Figures showing discretization, verification of the DistanceDecayCubeSampler , average simulated participation rate, and average cumulative magnitude–frequency distributions (MFDs).

Description: To date little is known about seismic hazard in Eritrea, despite its location in a volcanically and tectonically active region, and the gathering pace of major infrastructure projects. In response, we report the findings of a comprehensive probabilistic seismic-hazard assessment for Eritrea and adjacent areas. Seismic source and ground-motion models are constructed separately; we use an adaptive spatiotemporal smoothing method to map expected patterns of seismicity. To construct a consistent earthquake catalog from different data sets, we use orthogonal regression to convert and unify different magnitude scales. A sensitivity analysis of the different input parameters helps constrain them and disaggregation of site-specific hazard estimates yields insights into the relative contribution from seismic sources of different magnitudes and distances. The results highlight seismic hazard in proximity to the Red Sea, Gulf of Aden, Afar depression, and along the boundaries of the Danakil microplate. We estimate a 10% chance over 50 years of observing pseudospectral accelerations (PSAs) at 0.2 s exceeding 0.16 g in the port city of Massawa (population ~32,000) and the town of Bada (population ~4000). For the capital, Asmara (population ~520,000), we calculate a PSA of 0.11 g at 0.2 s. Compared with previous studies, our results provide greater spatial resolution, use more recent ground-motion models, and benefit from a smoothed seismicity method. Our aims are to stimulate further studies and contribute to the safe development of the region in light of its exposure to seismic hazards.