Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence

Morales‐Esteban, Antonio; Martínez‐Álvarez, Francisco; Reyes, J.

doi:10.1016/j.tecto.2013.02.036

Cited by 55 publications

(47 citation statements)

References 73 publications

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“…The authors follow the previous researches [11,12,16,17,19,26] to promote the earthquake prediction accuracy of the ANN model, and to find out the seismotectonics model considered to generate better results because earthquakes are a regional geological event and strongly depend on local geological circumstance. In general, the following conclusions can be drawn from this work:…”

Section: Resultsmentioning

confidence: 99%

Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

Sheng¹,

Mu²,

Zhang³

et al. 2015

TOCIEJ

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Abstract:It is well known that earthquakes are a regional event, strongly controlled by local geological structures and circumstances. Reducing the research area can reduce the influence of other irrelevant seismotectonics. A new sub regiondividing scheme, considering the seismotectonics influence, was applied for the artificial neural network (ANN) earthquake prediction model in the northeast seismic region of China (NSRC). The improved set of input parameters and prediction time duration are also discussed in this work. The new dividing scheme improved the prediction accuracy for different prediction time frames. Three different research regions were analyzed as an earthquake data source for the ANN model under different prediction time duration frames. The results show: (1) dividing the research region into smaller subregions can improve the prediction accuracies in NSRC, (2) larger research regions need shorter prediction durations to obtain better performance, (3) different areas have different sets of input parameters in NSRC, and (4) the dividing scheme, considering the seismotectonics frame of the region, yields better results.

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Section: Resultsmentioning

confidence: 99%

Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

Sheng¹,

Mu²,

Zhang³

et al. 2015

TOCIEJ

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show abstract

“…The b parameter characterizes the relative size of distribution of events and it depends on the stress regime and seism tectonic behaviour of the region. In this paper, it is calculated using the last 50 recorded quakes [15]. The b parameter value is determined as: (1) Where Mi is the magnitude for the i-th earthquake in the clustering dataset.…”

Section: Proposed Neural Networkmentioning

confidence: 99%

“…The sixth input variable x6i is the maximum magnitude Ms from the recorded quakes during the last week in the analyzed area [15]. It is defined as: when (7) where the time t is measured in days.…”

Section: Proposed Neural Networkmentioning

confidence: 99%

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Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Shodiq

Kusuma

Rifqi

et al. 2018

JOIV : Int. J. Inform. Visualization

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Abstract-A model of artificial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Six cluster was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter's scale magnitudes.

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“…Morales-Esteban et al [5] studied the seismicity of the Iberian Peninsula (IP). They concluded that its seismicity is moderate, generally characterized by earthquakes less than 5.0.…”

Section: Introductionmentioning

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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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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Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence

Cited by 55 publications

References 73 publications

Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

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

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