A neural network scheme for Earthquake prediction based on the seismic electric signals

Lakkos, S.; Hadjiprocopis, Andreas; Comley, Richard; Smith, P.K.

doi:10.1109/nnsp.1994.365997

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…ANN exists in many configurations and can provide methods to solve problems involving complex systems. The decisive property of the ANN can be used for earthquake prediction studies (Lakkos et al 1994;Adeli and Hung 1995;Adeli and Park 1998;Negarestani et al 2002;Sharma and Arora 2005;Kerh and Chu 2002;Panakkat and Adeli 2008). Arora and Sharma (1998) demonstrated the use of ANN for earthquake prediction.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Characteristics earthquake distribution makes use of cyclic behavior of earthquake occurrence when earthquakes occur in a cyclic manner in each section of the fault boundary as a result of seismic energy built up in locked tectonic plates and its subsequent release when the stored energy reaches a threshold as postulated in the elastic rebound theory (Reid 1906). When such studies could not capture the trends using classical statistics/probabilistic, artificial neural networks or the Artificial Intelligence has been used very often (Lakkos et al 1994;Adeli and Hung 1995;Adeli and Park 1998;Negarestani et al 2002;Sharma and Arora 2005;Kerh and Chu 2002;Panakkat and Adeli 2008). Panakkat and Adeli (2008) presented three neural network models for earthquake magnitude prediction using eight seismicity indicators or parameters: A feed-forward back propagation neural network, a recurrent neural network, and a radial basis function neural network.…”

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confidence: 99%

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Cyclic behavior of seismogenic sources in India and use of ANN for its prediction

Sharma

Tyagi

2010

Nat Hazards

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Endeavors to realistically model physical processes responsible for earthquake occurrence and sustained large uncertainties in the results have lead to the application of techniques like artificial neural network for estimation of rate/probability of earthquake occurrence in future. The earthquake occurrence in India has been re-visited and artificial neural networks have been applied to learn the cyclic behavior of seismicity in the independent seismogenic sources to predict their future trends. As a prerequisite, the whole country has been divided into 24 seismogenic sources for which the seismicity cycles were studied. Their cyclic behavior has been captured in form of four stages of earthquake occurrence and the future trends have been predicted using ANN. To validate the trained ANN model, testing has been carried out in two ways: first, by giving the samples that are not used in training (NT) and second, by giving the total samples (T). As a method of testing, standard errors and correlation coefficients between the network output patterns and observed patterns of the testing sample given were considered. The outcome of the ANN is used to interpret the future seismicity of each of the 24 seismogenic zones in terms of various stages of the future seismicity cycles.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

mentioning

confidence: 99%

Cyclic behavior of seismogenic sources in India and use of ANN for its prediction

Sharma

Tyagi

2010

Nat Hazards

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“…There are also other methods for earthquake prediction based on the first theory, such as [6][7][8]. For example, the remarkable increase of atmospheric methane concentration in seismic areas could also be regarded as a kind of earthquake precursor.…”

Section: Related Workmentioning

confidence: 99%

The Analysis of Tempo-Spatial Correlation Characteristics between Strong Earthquakes Based on Parallel Computing

Gao

Zhang

et al. 2010

2010 2nd International Conference on Information Engineering and Computer Science

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A modified earthquake prediction model based on At-value is introduced in this paper, which is improved by using the parallel computing techniques. The At-value is employed to analyze and compare the strain energy that is released in an earthquake, and gives the tempo-spatial correlation characteristics between strong earthquakes. After calculation, a picture of At-value distribution can be drawn and provide us some visual information for earthquake monitoring. Experimental results have demonstrated that the parallel computing techniques can process tremendous amount of seismic data and reduce the calculation time of At-value greatly.

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“…The precursor phenomena includes groundwater level change, TEC change, seismic quiescence, anomalous electromagnetic field changes, and abnormal animal behavior [ 2 ]. Lakkos et al proposed a back-propagation (BP) neural network to predict earthquake magnitude [ 3 ]. Zhang et al predicted a large earthquake and a number of aftershocks with seismic precursors including seismic quiescence and change in the vertical component of geomagnetism [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Electromagnetic Signal for Earthquake Magnitude Prediction

Bao

Zhao

Huang

et al. 2021

Sensors

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The influence of earthquake disasters on human social life is positively related to the magnitude and intensity of the earthquake, and effectively avoiding casualties and property losses can be attributed to the accurate prediction of earthquakes. In this study, an electromagnetic sensor is investigated to assess earthquakes in advance by collecting earthquake signals. At present, the mainstream earthquake magnitude prediction comprises two methods. On the one hand, most geophysicists or data analysis experts extract a series of basic features from earthquake precursor signals for seismic classification. On the other hand, the obtained data related to earth activities by seismograph or space satellite are directly used in classification networks. This article proposes a CNN and designs a 3D feature-map which can be used to solve the problem of earthquake magnitude classification by combining the advantages of shallow features and high-dimensional information. In addition, noise simulation technology and SMOTE oversampling technology are applied to overcome the problem of seismic data imbalance. The signals collected by electromagnetic sensors are used to evaluate the method proposed in this article. The results show that the method proposed in this paper can classify earthquake magnitudes well.

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A neural network scheme for Earthquake prediction based on the seismic electric signals

Cited by 12 publications

References 9 publications

Cyclic behavior of seismogenic sources in India and use of ANN for its prediction

Cyclic behavior of seismogenic sources in India and use of ANN for its prediction

The Analysis of Tempo-Spatial Correlation Characteristics between Strong Earthquakes Based on Parallel Computing

A Deep Learning-Based Electromagnetic Signal for Earthquake Magnitude Prediction

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