Seismic Data Classification Using Machine Learning

Li, Wenrui; Narvekar, Nishita; Nakshatra, Nakshatra; Raut, Nitisha; Sirkeci, Birsen; Gao, Jerry

doi:10.1109/bigdataservice.2018.00017

Cited by 23 publications

(14 citation statements)

References 7 publications

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“…Reference [56] examined the impact of deep learning algorithms for classification of earthquake precursors for extraction of seismic patterns and unique features from big data. Reference [57] distinguished between seismic signals and non seismic signals using logistic regression method on the data collected from National Seismological Network of Colombia. Reference [58] applied Support Vector regression and Hybrid neural Network for earthquake prediction in Hindukush, Chile and Southern California regions with prediction accuracy rate of 82.7%, 84.9%, 90.6% respectively.…”

Section: Discussionmentioning

confidence: 99%

“…For probablistic forcasts, multiple artificial intelligence methods have been exercised for making earthquake predictions. Comparitive studies have been conducted [13], systematic methods of predicate logic have also been applied to analyze the precursor that may have vital importance for earthquake prediction [9,27,32,57,65]. To estimate the mutual relationship of precursors, regression line has been calculated [26].…”

Section: Discussionmentioning

confidence: 99%

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Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES). We aim to identify and compare methods, models, frameworks, and tools used to forecast earthquakes using different parameters. We have conducted a systematic mapping study based upon 70 systematically selected high quality peer reviewed research articles involving ES for earthquake prediction, published between January 2010 and January 2020.To the best of our knowledge, there is no recent study that provides a comprehensive survey of this research area. The analysis shows that most of the proposed models have attempted long term predictions about time, intensity, and location of future earthquakes. The article discusses different variants of rule-based, fuzzy, and machine learning based expert systems for earthquake prediction. Moreover, the discussion covers regional and global seismic data sets used, tools employed, to predict earth quake for different geographical regions. Bibliometric and meta-information based analysis has been performed by classifying the articles according to research type, empirical type, approach, target area, and system specific parameters. Lastly, it also presents a taxonomy of earthquake prediction approaches, and research evolution during the last decade.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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“…The LDA was tested for classifying seismic signals with the goal of differentiating earthquakes from man-made explosions [38]. The RF was used in the classification of earthquake and non-earthquake signals [39] and the SVM was used to perform classification of volcanic events [18].…”

Section: Classificationmentioning

confidence: 99%

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Lara

Fernandes

Inza

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral and cepstral domains, extracted from the EMD of the signals, as well as a set of pre-processing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis (PCA) method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine (SVM) providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.

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“…[38] RBFNN Energy, mg., b-value, etc. [105] Time series data [106] dpt., ssd., mg., la., lo., Mc [107] Month, day and the F-E seismic regions [3] PNN te,Āe, Ee, dAe dt (dB), ∆e,te, δAe, ρ [108] [109] KNN, NB, SVM, DT, ANN Seismicity indicators, increments of b-value [110] DT, SVM, RF, LR Maximum amplitude, sd., start and end time, etc. [111] RF, ANN, SVM Sixty different seismic features.…”

Section: Cheraghi and Ghanbarimentioning

confidence: 99%

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

et al. 2020

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Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field.

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Seismic Data Classification Using Machine Learning

Cited by 23 publications

References 7 publications

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

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