Application of genetic BP network to discriminating earthquakes and explosions

Bian, Yinju

doi:10.1007/s11589-002-0023-0

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…The development of seismic big data and artificial intelligence has enabled the application of machine learning to non-NE identification, The model parameters are trained with the characteristics of seismic data, and the classification model is obtained and used for event type discrimination. (Bian 2002;Huang et al 2010;Bi et al 2011;Zhao et al 2017;Fan et al 2019;Liu et al 2020;Cai et al 2020). The main difficulty with traditional machine learning is that waveform features must be manually selected and then extracted using a specific technique, which may result in the loss of other valuable features in the data .…”

Section: Introductionmentioning

confidence: 99%

Seismic event classification based on a two-step convolutional neural network

Long

Zhou

et al. 2023

Preprint

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The identification of unnatural earthquake events is one of the tasks of earthquake rapid report. The identification accuracy is of great significance for improving the quality of earthquake catalogue and seismological research. In this work, a seven-layer convolution neural network model was constructed to identify unnatural earthquakes. First, the three-component seismic waveform was used as input to obtain the waveform image classifier, and then the time-frequency spectrum of explosion and collapse was used as input to obtain the time-frequency spectrum classifier. The two classifiers were used to identify earthquake, explosion and collapse. The model was trained and tested using 3386 seismic events of Shandong seismic network from 2017 to 2022. The events identified as explosion events by the waveform image classifier were reidentified by the time-frequency spectrum classifier. Finally, the identification accuracy of natural earthquake, explosion and collapse is 97.50%, 95.87% and 86.84% respectively, with an average identification accuracy of 96.13%. The experimental results show that the two-step convolution neural network can extract the characteristics of seismic signals from different angles, and get a good result in seismic event classification.

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Section: Introductionmentioning

confidence: 99%

Seismic event classification based on a two-step convolutional neural network

Long

Zhou

et al. 2023

Preprint

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“…Shallow fully connected neural networks (NN) is one of the earliest machine learning methods for the seismic signal recognition classification [1] [2]. The neural network accepts the extracted feature vector as input and transforms it through a series of neurons in the hidden layer to predict the expectation in the output layer.…”

Section: Introductionmentioning

confidence: 99%

Seismic Waveform Classification Based on Multi-classification Model Weighted Voting Method

Gao¹,

Qi²

2020

dtetr

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The earthquake event is caused by the activity of the interior earth, which has the potential and can create great damage. Thus, in big data era, it is necessary to fast, efficient and universal automatic recognition and classification of seismic waveform. A well-performed recognition and classification algorithm should be sensitive to small and weak events with various waveform shapes, robust to background noise and non-seismic signals, and effective for processing a large number of data in different areas. In this paper, we introduce the weighted voting method in ensemble learning, taking 50000 seismic signals monitored by the capital circle and its nearby stations as training and testing data, we learn the main time-frequency characteristics of seismic positions in the three-component seismic signal data through the sub-model, and then identify and classify them. Finally, the classification score of the sub-model is weighted to confirm the final event type. Compared with single machine learning models, our result shows improved recognition and classification performance and reduced uncertainty of ensembles.

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“…[13][14][15][16] and theoretically. [17] NiPc and CoPc have different work functions, 4.0 eV and 4.4 eV, respectively. The optical studies of NiPc and CoPc, based on the combination of the integrated capacitance and impedance, are conducted in this work.…”

Section: Introductionmentioning

confidence: 99%

Optical sensors based on the NiPc–CoPc composite films deposited by drop casting and under the action of centrifugal force

et al. 2017

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In this study, solution processed composite films of nickel phthalocyanine (NiPc) and cobalt phthalocyanine (CoPc) are deposited by drop casting and under centrifugal force. The films are deposited on surface-type inter-digitated silver electrodes on ceramic alumina substrates. The effects of illumination on the impedance and capacitance of the NiPc-CoPc composite samples are investigated. The samples deposited under centrifugal force show better conductivity than the samples deposited by drop casting technique. In terms of impedance and capacitance sensitivities the samples fabricated under centrifugal force are more sensitive than the drop casting samples. The values of impedance sensitivity (S z ) are equal to (−1.83) MΩ•cm 2 /mW and (−5.365) MΩ•cm 2 /mW for the samples fabricated using drop casting and under centrifugal force, respectively. Similarly, the values of capacitance sensitivity (S c ) are equal to 0.083 pF•cm 2 /mW and 0.185 pF•cm 2 /mW for the samples fabricated by drop casting and under centrifugal force. The films deposited using the different procedures could potentially be viable for different operational modes (i.e., conductive or capacitive) of the optical sensors. Both experimental and simulated results are discussed.

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Application of genetic BP network to discriminating earthquakes and explosions

Cited by 8 publications

References 2 publications

Seismic event classification based on a two-step convolutional neural network

Seismic event classification based on a two-step convolutional neural network

Seismic Waveform Classification Based on Multi-classification Model Weighted Voting Method

Optical sensors based on the NiPc–CoPc composite films deposited by drop casting and under the action of centrifugal force

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