Pattern recognition to forecast seismic time series

Morales‐Esteban, Antonio; Martínez‐Álvarez, Francisco; Troncoso, Alicia; Justo, J. L.; Rubio-Escudero, Cristina

doi:10.1016/j.eswa.2010.05.050

Cited by 77 publications

(41 citation statements)

References 41 publications

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“…It can be observed that Zone 5 coincides with the Alboran Sea, the southeast of the IP and the south of the Balearic Islands. The Pyrenees present a very complex structure represented by labels 2, 9 and 10, similarly to the zoning in [43]. The Azores-Gibraltar fault, Z24 in this study, has been characterized by labels 1, 5 and 8.…”

Section: Comparison To Kohonen's Self-organizing Mapsmentioning

confidence: 99%

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A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

Martínez‐Álvarez

Gutiérrez‐Avilés

Morales‐Esteban

et al. 2015

Entropy

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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 Entropy 2015, 17 5001 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.

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Section: Comparison To Kohonen's Self-organizing Mapsmentioning

confidence: 99%

“…This is the year from which all of the earthquakes of a magnitude larger to or equal to the minimum magnitude have been recorded. The works in [43,44] point out that the year of completeness of the catalog is 1978 and that the minimum magnitude is 3.0. Only earthquakes from 1978 and onwards are used in this study.…”

Section: Data Description and Retrievalmentioning

confidence: 99%

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

Martínez‐Álvarez

Gutiérrez‐Avilés

Morales‐Esteban

et al. 2015

Entropy

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“…Later in 2013, Reyes et al [26] presented a new method for earthquake prediction based on the ANN. The input for the ANN was based on the b-value [27], the Bath's law [28] and the Omori-Utsu's law [29].…”

Section: Earthquake Prediction By Means Of Annmentioning

confidence: 99%

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2016

CRORR

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Abstract. The occurrence of earthquakes has been studied from many aspects. Apparently, earthquakes occur without warning and can devastate entire cities in just a few seconds, causing numerous casualties and huge economic loss. Great eort has been directed towards being able to predict these natural disasters, and taking precautionary measures. However, simultaneously predicting when, where and the magnitude of the next earthquake, within a limited region and time, seems an almost impossible task. Techniques from the eld of data mining are providing new and important information to researchers. This article reviews the use of articial neural networks for earthquake prediction in response to the increasing amount of recently published works and presenting claims of being eective. Based on an analysis and discussion of recent results, data mining practitioners are encouraged to apply their own techniques in this emerging eld of research.

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“…The earthquake dataset [30,35] has been retrieved from the Spanish Geographical Institute (SGI). This dataset consists of four quantitative attributes and 873 instances related to the location and the magnitude of Spanish earthquakes from 1981 to 2008.…”

Section: • Public Datasetsmentioning

confidence: 99%

Selecting the best measures to discover quantitative association rules

et al. 2014

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The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly assess their quality due to some inherent drawbacks they present. A review of the literature reveals that there exist many measures to evaluate the quality of the rules, but that the simultaneous optimization of all measures is complex and might lead to poor results. In this work, a principal components analysis is applied to a set of measures that evaluate quantitative association rules' quality. From this analysis, a reduced subset of measures has been selected to be included in the fitness function in order to obtain better values for the whole set of quality measures, and not only for those included in the fitness function. This is a general-purpose methodology and can, therefore, be applied to the fitness function of any algorithm. To validate if better results are obtained when using the function fitness composed of the subset of measures proposed here, the existing QARGA algorithm has been applied to a wide variety of datasets. Finally, a comparative analysis of the results obtained by means of the application of QARGA with the original fitness function is provided, showing a remarkable improvement when the new one is used.

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Pattern recognition to forecast seismic time series

Cited by 77 publications

References 41 publications

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

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Selecting the best measures to discover quantitative association rules

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