Neural Network Models for Earthquake Magnitude Prediction Using Multiple Seismicity Indicators

Panakkat, Ashif; Adeli, Hojjat

doi:10.1142/s0129065707000890

Cited by 221 publications

(179 citation statements)

References 40 publications

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“…The analysis of the aftershocks aimed at one hand the spatiotemporal characterization [22] by reviewing and refining the hypocentral locations, the determination of the seismic generation mechanisms and identification of the main active structures. A methodology to predict magnitude of the earthquake [49] using neural network has been described. They have taken seismicity indicators inputs for the ANN model.…”

Section: Proposed Methodsmentioning

confidence: 99%

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Dutta¹,

Mishra²,

Naskar³

2013

Open Geosciences

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Abstract:In the proposed study, non-linear behavioral patterns in the seismic regime for earthquakes in the Himalayan basin have been studied using a complete, verified EQ catalogue comprised of all major events and their aftershock sequences in the Himalayan basin for the past 110 years . The dataset has been analyzed to give better decision making criteria for impending earthquakes. A series of statistical tests based on multi-dimensional rigorous statistical studies, inter-event distance analyses, and statistical time analyses have been used to obtain correlation dimensions. The time intervals of earthquakes within a seismic regime have been used to train the neural network to analyze the nature of earthquake patterns in the different clusters. The results obtained from descriptive statistics show high correlation with previously conducted gravity studies and radon anomaly variation. A study of the time of recurrence of the numerical properties of the regime for 60 years from 1950 to 2010 for the Himalayan belt for analysis of significant EQ failure events has been done to find the best fit for an empirical data probability distribution. The distribution of waiting time of swarm events occurring in the Himalayan basin follows a power-law model, while independent events do not fit the power-law distribution. This suggests that probability of the occurrence of swarm events [M 6.0] with frequent shaking may be more frequent than that of the occurrence of independent events of magnitude [M >6.0] in the Himalayan belt. We propose a three-layer feed forward neural network model to identify factors, with the actual occurrence of the maximum earthquake level M as input and target vectors in Himalayan basin area. We infer through a series of statistical results and evaluations that probabilistic forecasting of earthquakes can be achieved by finding the meta-stable cluster zones of the Himalayan clusters for the spatio-temporal distribution of earthquakes in the area.

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Section: Proposed Methodsmentioning

confidence: 99%

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Dutta¹,

Mishra²,

Naskar³

2013

Open Geosciences

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“…Panakkat and Adeli [102] presented neural network models for earthquake magnitude prediction using multiple seismicity indicators. They [103] also presented an RNN for approximate earthquake time and location prediction.…”

Section: Prediction Applicationsmentioning

confidence: 99%

Neurocomputing in Civil Infrastructure

et al. 2016

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Abstract. This article presents a review of the recent applications of Arti cial Neural Networks (ANN) for civil infrastructure including structural system identi cation, structural health monitoring, structural vibration control, structural design and optimization, prediction applications, construction engineering, and geotechnical engineering. The most common ANN used in structural engineering is the backpropagation neural network followed by recurrent neural networks and radial basis function neural networks. In recent years, a number of researchers have used newer hybrid techniques in structural engineering such as the neuro-fuzzy inference system, time-delayed neuro-fuzzy inference system, and wavelet neural networks. Deep machine learning techniques are among the newest techniques to nd applications in civil infrastructure systems.

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“…Three different ANN models used a novel set of seismicity indicators which were applied to forecast the earthquake magnitude for southern California and San Francisco bay. These ANN models achieved acceptable results for the earthquake of a magnitude between 6.0 and 7.5 [11]. What is remarkable about their work is that they compared the prediction accuracies with a recurrent neural network, a radial basis function neural network and a Levenberg-Marquardt back-propagation neural network.…”

Section: Introductionmentioning

confidence: 99%

“…Many kinds of ANN methods were compared to search for the rational method to build the earthquake model [5,12,13,19]. Seismicity indicators were analyzed to choose the rational input parameters [11,16] and to consider the monitoring data [18,14,20]. The best set of seismicity indicators to predict earthquakes were also discussed [16].…”

Section: Introductionmentioning

confidence: 99%

“…This work is based on the three previous works [11,16,17] and uses Recurrent Neural Network (RNN) to predict earthquakes in the northeast seismic region of China (NSRC in brief). This work will apply feature selection techniques to obtain a better set of features for NSRC from 16 seismicity indicators which were discussed by Panakkat&Adeli (2007) [11] [16].…”

Section: Introductionmentioning

confidence: 99%

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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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Neural Network Models for Earthquake Magnitude Prediction Using Multiple Seismicity Indicators

Cited by 221 publications

References 40 publications

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Neurocomputing in Civil Infrastructure

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

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