Detection and identification of seismic P-Waves using Artificial Neural Networks

Kaur, Komalpreet; Wadhwa, Manish; Park, E. K.

doi:10.1109/ijcnn.2013.6707117

Cited by 20 publications

(9 citation statements)

References 6 publications

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“…CNN, DNN and RNN network can achieve to pick P and S phases with three-component seismograms [14]- [17]. Some previous work focused on inputting derived attributes from seismic data into a hybrid artificial neural network for phase picking [18], [19]. These works prove that neural networks have advantages in phase picking.…”

Section: Introductionmentioning

confidence: 91%

Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network

Jiang

Zheng

2020

IEEE Access

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High-accuracy microseismic phase picking is fundamental to microseismic signal processing. Phase picking methods based on deep learning show great potential dealing with low signal to noise ratio (SNR) data but need enormous training data. However, it's not easy to obtain a big size of field datasets and label them manually to train the neural network. In this paper, a novel method is proposed by applying feature extraction (Gammatone Feature) and neural networks to pick phases automatically. The feature extraction scheme aims to train the neural network using a relatively small size of training datasets. To test the performances of the proposed method, synthetic datasets were obtained by numerical simulation and used to train the neural network. Both synthetic datasets and field datasets were used to test the neural network for picking P-and S-phases of microseismic events. The results of phase picking illustrate that: (1) feature extraction scheme in the training stage can help reduce the size of training datasets; (2) The neural network can be trained well just by synthetic data and phase picking results are accurate and satisfying when the method was tested by both synthetic and field datasets. INDEX TERMS Feature extraction, Gammatone feature, neural networks, phase picking.

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Section: Introductionmentioning

confidence: 91%

Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network

Jiang

Zheng

2020

IEEE Access

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“…The window length in samples is subject to the P-wave picking time and the window duration. A 2-s window is a recommended length [55]. It is necessary to obtain non-P-wave windows as well, since the classification algorithm learns to distinguish between the attributes of a P-wave and the attributes of a non-P-wave signal.…”

Section: Classification Processmentioning

confidence: 99%

Proposal for a System Model for Offline Seismic Event Detection in Colombia

et al. 2020

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This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendations for its implementation. The resulting model allows understanding the integration of the phases required for the design and development of an offline seismic event detection system.

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“…Thus, ANNs are ideal tools for tasks such as pattern recognition. ANNs have been successfully used in a variety of pattern recognition tasks such as gravitational wave detection [17]- [19], exoplanet discovery by surveying light curves obtained by space telescopes [20], brain wave feature extraction and classification [21], seismic wave detection and identification [22], character recognition [23], and voice recognition [24]. ANNs are also used in more familiar tools that we use every day, for example, in the Google search engine and in the iPhone personal assistant Siri [25].…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

Identification of Plasma Waves at Saturn Using Convolutional Neural Networks

Ruhunusiri

2018

IEEE Trans. Plasma Sci.

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Cassini has recently completed its 13-year mission at Saturn leaving a vast data set. A large interest among the scientific community is to investigate plasma waves and instabilities at Saturn. It is no longer feasible to manually search through Cassini's vast data set to identify all such waves of interest. Thus, the feasibility of using artificial neural networks (ANNs) to identify plasma waves at Saturn is demonstrated using Cassini data. A convolutional neural network (CNN) was trained to identify low-frequency plasma waves that occur in the upstream region of Saturn using images constructed from the Cassini magnetometer time series data. By systematically varying the network architecture during training and validation, a CNN was obtained that can identify upstream waves with an accuracy of 94% ± 2%. The CNN's high accuracy for wave identification demonstrates that it is, in fact, feasible to use ANNs to identify plasma waves at Saturn and by extension in other planetary and lunar plasma environments using spacecraft data.

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Detection and identification of seismic P-Waves using Artificial Neural Networks

Cited by 20 publications

References 6 publications

Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network

Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network

Proposal for a System Model for Offline Seismic Event Detection in Colombia

Identification of Plasma Waves at Saturn Using Convolutional Neural Networks

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