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Martinez-Alvarez F.,Pablo De Olavide University | Gutierrez-Aviles D.,University of Seville | Morales-Esteban A.,University of Seville | Reyes J.,TGT NT2 Labs | And 2 more authors.

A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas of spread and low seismic activity. Traditional zoning methods are based on the available seismic catalog and the geological structures. It is admitted that thermal and resistant parameters of the crust provide better criteria for zoning. Nonetheless, the working out of the rheological profiles causes a great uncertainty. This has generated inconsistencies, as different zones have been proposed for the same area. A new method for seismogenic zoning by means of triclustering is proposed in this research. The main advantage is that it is solely based on seismic data. Almost no human decision is made, and therefore, the method is nearly non-biased. To assess its performance, the method has been applied to the Iberian Peninsula, which is characterized by the occurrence of small to moderate magnitude earthquakes. The catalog of the National Geographic Institute of Spain has been used. The output map is checked for validity with the geology. Moreover, a geographic information system has been used for two purposes. First, the obtained zones have been depicted within it. Second, the data have been used to calculate the seismic parameters (b-value, annual rate). Finally, the results have been compared to Kohonen's self-organizing maps. © 2015 by the authors; licensee MDPI, Basel, Switzerland. Source

Reyes J.,University of Santiago de Chile | Morales-Esteban A.,University of Seville | Gonzalez E.,TGT NT2 Labs | Martinez-Alvarez F.,Pablo De Olavide University

In this research, a new algorithm for generating a stochastic earthquake catalog is presented. The algorithm is based on the acceptance-rejection sampling of von Neumann. The result is a computer simulation of earthquakes based on the calculated statistical properties of each zone. Vere-Jones states that an earthquake sequence can be modeled as a series of random events. This is the model used in the proposed simulation. Contrariwise, Utsu indicates that the mainshocks are special geophysical events. The algorithm has been applied to zones of Chile, China, Spain, Japan, and the USA. This allows classifying the zones according to Vere-Jones' or Utsu's model. The results have been quantified relating the mainshock with the largest aftershock within the next 5 days (which has been named as Bath event). The results show that some zones fit Utsu's model and others Vere-Jones'. Finally, the fraction of seismic events that satisfy certain properties of magnitude and occurrence is analyzed. © 2016 Elsevier B.V. Source

Florido E.,Spanish University for Distance Education (UNED) | Florido E.,Pablo De Olavide University | Martinez-Alvarez F.,Pablo De Olavide University | Morales-Esteban A.,University of Seville | And 2 more authors.
Computers and Geosciences

The prediction of earthquakes is a task of utmost difficulty that has been widely addressed by using many different strategies, with no particular good results thus far. Seismic time series of the four most active Chilean zones, the country with largest seismic activity, are analyzed in this study in order to discover precursory patterns for large earthquakes. First, raw data are transformed by removing aftershocks and foreshocks, since the goal is to only predict main shocks. New attributes, based on the well-known b-value, are also generated. Later, these data are labeled, and consequently discretized, by the application of a clustering algorithm, following the suggestions found in recent literature. Earthquakes with magnitude larger than 4.4 are identified in the time series. Finally, the sequence of labels acting as precursory patterns for such earthquakes are searched for within the datasets. Results verging on 70% on average are reported, leading to conclude that the methodology proposed is suitable to be applied in other zones with similar seismicity. © 2014 Elsevier Ltd. Source

Martinez-Alvarez F.,Pablo De Olavide University | Reyes J.,TGT NT2 Labs | Morales-Esteban A.,University of Seville | Rubio-Escudero C.,University of Seville
Knowledge-Based Systems

This work explores the use of different seismicity indicators as inputs for artificial neural networks. The combination of multiple indicators that have already been successfully used in different seismic zones by the application of feature selection techniques is proposed. These techniques evaluate every input and propose the best combination of them in terms of information gain. Once these sets have been obtained, artificial neural networks are applied to four Chilean zones (the most seismic country in the world) and to two zones of the Iberian Peninsula (a moderate seismicity area). To make the comparison to other models possible, the prediction problem has been turned into one of classification, thus allowing the application of other machine learning classifiers. Comparisons with original sets of inputs and different classifiers are reported to support the degree of success achieved. Statistical tests have also been applied to confirm that the results are significantly different than those of other classifiers. The main novelty of this work stems from the use of feature selection techniques for improving earthquake prediction methods. So, the information gain of different seismic indicators has been determined. Low ranked or null contribution seismic indicators have been removed, optimizing the method. The optimized prediction method proposed has a high performance. Finally, four Chilean zones and two zones of the Iberian Peninsula have been characterized by means of an information gain analysis obtained from different seismic indicators. The results confirm the methodology proposed as the best features in terms of information gain are the same for both regions. © 2013 Elsevier B.V. All rights reserved. Source

Morales-Esteban A.,University of Seville | Martinez-Alvarez F.,Pablo De Olavide University | Reyes J.,TGT NT2 Labs

A method to predict earthquakes in two of the seismogenic areas of the Iberian Peninsula, based on Artificial Neural Networks (ANNs), is presented in this paper. ANNs have been widely used in many fields but only very few and very recent studies have been conducted on earthquake prediction. Two kinds of predictions are provided in this study: a) the probability of an earthquake, of magnitude equal or larger than a preset threshold magnitude, within the next 7. days, to happen; b) the probability of an earthquake of a limited magnitude interval to happen, during the next 7. days. First, the physical fundamentals related to earthquake occurrence are explained. Second, the mathematical model underlying ANNs is explained and the configuration chosen is justified. Then, the ANNs have been trained in both areas: The Alborán Sea and the Western Azores-Gibraltar fault. Later, the ANNs have been tested in both areas for a period of time immediately subsequent to the training period. Statistical tests are provided showing meaningful results. Finally, ANNs were compared to other well known classifiers showing quantitatively and qualitatively better results. The authors expect that the results obtained will encourage researchers to conduct further research on this topic. © 2013 Elsevier B.V. Source

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