A way to synchronize models with seismic faults for earthquake forecasting: Insights from a simple stochastic model

Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example, meteorologists synchronize their models with the atmosphere by incorporating current atmospheric data in them). However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the synchronization of the models.The rupture area is one of the measurable parameters of earthquakes. Here we explore how it can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. Our purpose here is to forecast synthetic earthquakes in a simple but stochastic (random) fault model. By imposing the rupture area of the synthetic earthquakes of this model on other models, the latter become partially synchronized with the first one. We use these partially synchronized models to successfully forecast most of the largest earthquakes generated by the first model. This forecasting strategy outperforms others that only take into account the earthquake series. Our results suggest that probably a good way to synchronize more detailed models with real faults is to force them to reproduce the sequence of previous earthquake ruptures on the faults. This hypothesis could be tested in the future with more detailed models and actual seismic data.     

Improving the level of seismic hazard parameters in Saudi Arabia using earthquake location

Saudi Arabia is characterized as largely aseismic; however, the tectonic plate boundaries that surround it are very active. To improve characterization of seismicity and ground motion hazard, the Saudi Arabian Digital Seismic Network (SANDSN) was installed in 1998 and continues to be operated by the Saudi Geological Survey (SGS) and King Abdulaziz City for Science and Technology (KACST). This article describes research performed to improve seismic hazard parameters using earthquake location and magnitude calibration of the high-quality SANDSN data. The SANDSN consists of 38 seismic stations, 27 broadband, and 11 short period. All data are telemetered in real time to a central facility at KACST in Riyadh. The SANDSN stations show low background noise levels and have good signal detection capabilities; however, some stations show cultural noise at frequencies above 1.0 Hz. We assessed the SANDSN event location capabilities by comparing KACST locations with well-determined locations derived from ground truth or global observations. While a clear location bias exists when using the global average iasp91 earth model, the locations can be improved by using regional models optimized for different tectonic source regions. The article presents detailed analysis of some events and Dead Sea explosions where we found gross errors in estimated locations. New velocity models we calculated that should improve estimated locations of regional events in three specific regions include (1) Gulf of Aqabah—Dead Sea region, (2) Arabian Shield, and (3) Arabian Platform. Recently, these models were applied to the SANDSN to improve local and teleseismic event locations and to develop an accurate magnitude scale for Saudi Arabia. The Zagros Thrust presents the most seismic hazard to eastern Saudi Arabia because of the frequent occurrence of earthquakes. Although these events are 200 km or further from the Arabian coast, wave propagation through sedimentary structure of the Gulf causes long-duration ground motions for periods between 3 and 10 s. Such ground motions could excite response in large engineered structures (e.g., tall buildings and long bridges) such as was experienced after the November 22, 2005 Qeshm Island earthquake off the southern coast of Iran.     

A Moore's cellular automaton model to get probabilistic seismic hazard maps for different magnitude releases: A case study for Greece

Cellular automata are simple mathematical idealizations of natural systems and they supply useful models for many investigations in natural science. Examples include sandpile models, forest fire models, and slider block models used in seismology. In the present paper, they have been used for establishing temporal relations between the energy releases of the seismic events that occurred in neighboring parts of the crust. The catalogue is divided into time intervals, and the region is divided into cells which are declared active or inactive by means of a threshold energy release criterion. Thus, a pattern of active and inactive cells which evolves over time is determined. A stochastic cellular automaton is constructed starting with these patterns, in order to simulate their spatio-temporal evolution, by supposing a Moore's neighborhood interaction between the cells. The best model is chosen by maximizing the mutual information between the past and the future states. Finally, a Probabilistic Seismic Hazard Map is given for the different energy releases considered. The method has been applied to the Greece catalogue from 1900 to 1999. The Probabilistic Seismic Hazard Maps for energies corresponding to m = 4 and m = 5 are close to the real seismicity after the data in that area, and they correspond to a background seismicity in the whole area. This background seismicity seems to cover the whole area in periods of around 25–50 years. The optimum cell size is in agreement with other studies; for m > 6 the optimum area increases according to the threshold of clear spatial resolution, and the active cells are not so clustered. The results are coherent with other hazard studies in the zone and with the seismicity recorded after the data set, as well as provide an interaction model which points out the large scale nature of the earthquake occurrence.     

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban areas.Therefore,earthquake risk assessment(ERA)is indispensable in disaster management.ERA is an integration of the extent of probability and vulnerability of assets.This study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA map.The aim of the study is to quantify urban population risk that may be caused by impending earthquakes.The model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating earthquakes.ANN is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering.The risk map is subsequently created by combining the probability,hazard,and vulnerability maps.Then,the risk levels of various zones are obtained.The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk classifications.The findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.