scholarly journals HAZGRIDX: earthquake forecasting model for ML≥ 5.0 earthquakes in Italy based on spatially smoothed seismicity

2010 ◽  
Vol 53 (3) ◽  
Author(s):  
Aybige Akinci
Keyword(s):  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Spatially Smoothed Seismicity ◽  
Smoothed Seismicity

scholarly journals Effect of tectonic setting on the fit and performance of a long-range earthquake forecasting model

2012 ◽  
Vol 2 (1) ◽  
pp. 3
Author(s):  
David Alan Rhoades ◽  
Paul G. Somerville ◽  
Felipe Dimer de Oliveira ◽  
Hong Kie Thio
Keyword(s):  
Long Range ◽  
Information Gain ◽  
Tectonic Setting ◽  
Depth Resolution ◽  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Testing Period ◽  
Crustal Earthquakes ◽  
Average Information ◽  
Better Than

The Every Earthquake a Precursor According to Scale (EEPAS) long-range earthquake forecasting model has been shown to be informative in several seismically active regions, including New Zealand, California and Japan. In previous applications of the model, the tectonic setting of earthquakes has been ignored. Here we distinguish crustal, plate interface, and slab earthquakes and apply the model to earthquakes with magnitude M≥4 in the Japan region from 1926 onwards. The target magnitude range is M≥ 6; the fitting period is 1966-1995; and the testing period is 1996-2005. In forecasting major slab earthquakes, it is optimal to use only slab and interface events as precursors. In forecasting major interface events, it is optimal to use only interface events as precursors. In forecasting major crustal events, it is optimal to use only crustal events as precursors. For the smoothed-seismicity component of the EEPAS model, it is optimal to use slab and interface events for earthquakes in the slab, interface events only for earthquakes on the interface, and crustal and interface events for crustal earthquakes. The optimal model parameters indicate that the precursor areas for slab earthquakes are relatively small compared to those for earthquakes in other tectonic categories, and that the precursor times and precursory earthquake magnitudes for crustal earthquakes are relatively large. The optimal models fit the learning data sets better than the raw EEPAS model, with an average information gain per earthquake of about 0.4. The average information gain is similar in the testing period, although it is higher for crustal earthquakes and lower for slab and interface earthquakes than in the learning period. These results show that earthquake interactions are stronger between earthquakes of similar tectonic types and that distinguishing tectonic types improves forecasts by enhancing the depth resolution where tectonic categories of earthquakes are vertically separated. However, when depth resolution is ignored, the model formed by aggregating the optimal forecasts for each tectonic category performs no better than the raw EEPAS model.

scholarly journals Earthquake forecasting model for Nepal to improve prevention

2018 ◽  
Vol 7 (4) ◽  
pp. 700-701
Author(s):  
Brijesh Sathian ◽  
Edwin R Van Teijlingen
Keyword(s):  
Government Policy ◽  
Service Providers ◽  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Policy Makers ◽  
International Agencies ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
Current Scenario ◽  
Per Se

There is an urgent need of earthquake forecasting model for Nepal in this current scenario. It can be developed by the scientists of Nepal with the help of experienced international scientists. This will help the Nepalese to take timely and necessary precautions. We would argue that above all we need to use earthquake prediction knowledge to improve the disaster prepardness in local communities, service providers (hospitals, Non-Governmental Organizations, police, etc.), government policy-makers and international agencies. On the whole, both seismology and public health are most successful when focusing on  prevention not on prediction per se. J Epidemiol. 2017;7(4); 700-701.

Seismic Hazard Map for Cuba and Adjacent Areas Using the Spatially Smoothed Seismicity Approach

2008 ◽  
Vol 12 (2) ◽  
pp. 173-196 ◽  
Author(s):  
Julio Garcia ◽  
Dario Slejko ◽  
Alessandro Rebez ◽  
Marco Santulin ◽  
Leonardo Alvarez
Keyword(s):  
Seismic Hazard ◽  
Hazard Map ◽  
Spatially Smoothed Seismicity ◽  
Smoothed Seismicity ◽  
Seismic Hazard Map

Seismicity Parameters and Spatially Smoothed Seismicity Model for Iran

2016 ◽  
Vol 106 (3) ◽  
pp. 1133-1150 ◽  
Author(s):  
A. Khodaverdian ◽  
H. Zafarani ◽  
M. Rahimian ◽  
V. Dehnamaki
Keyword(s):  
Seismicity Parameters ◽  
Spatially Smoothed Seismicity ◽  
Smoothed Seismicity ◽  
Seismicity Model

scholarly journals Earthquake forecasting probability charts

1979 ◽  
Vol 12 (4) ◽  
pp. 324-327
Author(s):  
D. A. Rhoades
Keyword(s):  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Minimum Requirements

Earthquake forecasts can be expressed in a useful form for practical purposes by mapping the probability that specific strengths of shaking will occur within specified timespans. The minimum requirements for a forecasting model to allow this form of presentation are discussed and an illustration based on the precursory swarm hypothesis is given.

A physical constraint on smoothed-seismicity models and the stationary seismicity assumption in long-term forecasting

2020 ◽  
Author(s):  
Pablo Iturrieta ◽  
Danijel Schorlemmer ◽  
Fabrice Cotton ◽  
José Bayona ◽  
Karina Loviknes
Keyword(s):  
Time Window ◽  
Earthquake Forecasting ◽  
Stress And Strain ◽  
Balance Principle ◽  
Seismicity Models ◽  
Smoothed Seismicity ◽  
Spatio Temporal ◽  
The Moment ◽  
Seismic Catalog

<p>In earthquake forecasting, smoothed-seismicity models (SSM) are based on the assumption that previous earthquakes serve as a guideline for future events. Different kernels are used to spatially extrapolate each moment tensor from a seismic catalog into a moment-rate density field. Nevertheless, governing mechanical principles remain absent through the model conception, even though crustal stress is responsible for moment release mainly in pre-existent faults. Furthermore, a lately developed SSM by Hiemer et al., 2013 (SEIFA) incorporates active-fault characterization and deformation rates stochastically, so that a geological estimate of moment release could also be taken into account. Motivated by this innovative approach, we address the question: How representative is the stochastic temporal/spatial averaging of SEIFA, of the long-term crustal deformation and stress? In this context, physics-based modeling provides insights about the energy, stress, and strain-rate fields within the crust due to discontinuities found therein. In this work, we aim to understand the required temporal window of SEIFA to satisfy mechanically its underlying assumption of stationarity. We build various SEIFA models within different spatio-temporal subsets of a catalog and confront them with a physics-based model of long-term seismic energy/moment rate. Following, we develop a method based on the moment-balance principle and information theory to compare the spatial similarity between these two types of models. These models are built from two spatially conforming layers of information: a complete seismic catalog and a computerized 3-D geometry of mapped faults along with their long-term slip rate. SEIFA uses both datasets to produce a moment-density rate field, from which later a forecast could be derived. A simple physics-based model is used as proof of concept, such as the steady-state Boundary Element Method (BEM). It uses the fault 3D geometry and slip rates to calculate the long-term interseismic energy rate and elastic stress and strain tensors, accumulated both along the faults and within the crust. The SHARE European Earthquake Catalog and the European Database of Seismogenic Faults are used as a case study, constrained to crustal faults and different spatio-temporal subsets of the Italy region in the 1000-2006 time window. The moment-balance principle is analyzed in terms of its spatial distribution calculating the spatial mutual information (SMI) between both models as a similarity measure. Finally, by using the SMI as a minimization function, we determine the catalog optimal time window for which the predicted moment rate by the SSM is closer to the geomechanical prediction. We emphasize that regardless of the stationarity assumption usefulness in seismicity forecasting, we determine a simple method that provides a physical boundary to data-driven seismicity models. This framework may be used in the future to combine seismicity data and geophysical modeling for earthquake forecasting.</p>

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