A Chilean seismic regionalization through a Kohonen neural network

Reyes, J.; Cárdenas, Víctor H.

doi:10.1007/s00521-010-0373-9

Cited by 18 publications

(12 citation statements)

References 15 publications

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“…This proposal can also be applied to mine RNA-seq data repositories. Also, the seismic regionalization of the Iberian Peninsula is currently being addressed through TriGen, turning the 2D-dimensional problem proposed in [27] into a 3D one. In this case, the third component does not identify time stamps but features associated to every pair of geographical coordinates that represent the Iberian Peninsula.…”

Section: Discussionmentioning

confidence: 99%

TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

et al. 2014

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Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations extracted from the Gene Ontology.

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Section: Discussionmentioning

confidence: 99%

TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

et al. 2014

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“…According to Reyes and Cárdenas [46], Scitovski and Scitovski [47], who performed a Chilean and Croatian seismic zonification in 2010 and 2013, respectively, the features generated are:…”

Section: Data Transformationmentioning

confidence: 99%

“…In this way, SOM can find the best distribution of zones, based on these training vectors. Since SOM was successfully applied to generate seismic hazard maps in Chile [46], it has been applied to assess its performance for the Iberian Peninsula. The same input features used for TriGen (see Section 4.3) have been used.…”

Section: Comparison To Kohonen's Self-organizing Mapsmentioning

confidence: 99%

“…Three thousand epochs of 5400 iterations have been used. This number was empirically determined since the use of 1000, 2000, 3000, 5000 and 10,000 epochs did not substantially change the results, and 3000 epochs was advised in [46].…”

Section: Comparison To Kohonen's Self-organizing Mapsmentioning

confidence: 99%

“…Similar to [46], the coordinates of every cell have also been used as input features (see Figure 7). This new scenario provided a more compact zoning.…”

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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Improving Earthquake Prediction with Principal Component Analysis: Application to Chile

Asencio-Cortés

Martínez‐Álvarez

Morales‐Esteban

et al. 2015

Lecture Notes in Computer Science

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A Chilean seismic regionalization through a Kohonen neural network

Cited by 18 publications

References 15 publications

TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

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

Improving Earthquake Prediction with Principal Component Analysis: Application to Chile

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