Improving Earthquake Prediction with Principal Component Analysis: Application to Chile

Asencio-Cortés, Gualberto; Martínez‐Álvarez, Francisco; Morales‐Esteban, Antonio; Reyes, J.; Troncoso, Alicia

doi:10.1007/978-3-319-19644-2_33

Cited by 19 publications

(13 citation statements)

References 19 publications

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“…10.1029/2019RS006931 fault zones (Kannan, 2014), and an empirical probabilistic model that had been proposed to predict onset and magnitude of an upcoming EQ (Papazachos & Papaioannou, 1993). Several machine learning models are also implemented on past seismic activity data to predict EQ onset, magnitude, or epicenter such as decision trees, random forest, AdaBoost, information network, multiobjective info-fuzzy network, k-nearest neighbors, support vector machine (SVM), artificial neural networks (Asencio-Cortés et al, 2015, 2016Last et al, 2016;Mahmoudi et al, 2016;Moustra et al, 2011) , support vector regressors and hybrid neural networks trained on an EQ catalog (Asim et al, 2018), deep neural networks (Panakkat & Adeli, 2009;Wang et al, 2017), and convolutional neural networks to predict an upcoming EQ with seismic waveforms of length of 100 s Ibrahim et al (2018).…”

Section: Radio Sciencementioning

confidence: 99%

“…Some of the proposed mathematical models can be listed as fault line strain related force models that are investigated to suggest a periodicity in EQ appearances (Bendick & Bilham, 2017), Fibonacci, Lucas, Dual (FDL) numbers that are embedded in the occurrence times of old EQs to predict the upcoming EQ onsets (Boucouvalas et al, 2015), spatial connection model that fits to the EQ occurrence pattern around the fault zones (Kannan, 2014), and an empirical probabilistic model that had been proposed to predict onset and magnitude of an upcoming EQ (Papazachos & Papaioannou, 1993). Several machine learning models are also implemented on past seismic activity data to predict EQ onset, magnitude, or epicenter such as decision trees, random forest, AdaBoost, information network, multiobjective info‐fuzzy network, k ‐nearest neighbors, support vector machine (SVM), artificial neural networks (Asencio‐Cortés et al, 2015, 2016; Last et al, 2016; Mahmoudi et al, 2016; Moustra et al, 2011) , support vector regressors and hybrid neural networks trained on an EQ catalog (Asim et al, 2018), deep neural networks (Panakkat & Adeli, 2009; Wang et al, 2017), and convolutional neural networks to predict an upcoming EQ with seismic waveforms of length of 100 s Ibrahim et al (2018).…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning‐Based Detection of Earthquake Precursors Using Ionospheric Data

Akyol

Arıkan

2020

Radio Science

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Detection of precursors of strong earthquakes is a challenging research area. Recently, it has been shown that strong earthquakes affect electron distribution in the regional ionosphere with indirectly observable changes in the ionospheric delays of GPS signals. Especially, the total electron content (TEC) estimated from GPS data can be used in the seismic precursor detection for strong earthquakes. Although physical mechanisms are not well understood yet, GPS‐based seismic precursors can be observed days prior to the occurrence of the earthquake. In this study, a novel machine learning‐based technique, EQ‐PD, is proposed for detection of earthquake precursors in near real time based on GPS‐TEC data along with daily geomagnetic indices. The proposed EQ‐PD technique utilizes support vector machine (SVM) classifier to decide whether an observed spatiotemporal anomaly is related to an earthquake precursor or not. The data fed to the classifier are composed of spatiotemporal variability map of a region. Performance of the EQ‐PD technique is demonstrated in a case study over a region covering Italy in between the dates of 1 January 2014 and 30 September 2016. The data are partitioned into three nonoverlapping time periods, that are used for training, validation, and test of detecting precursors of earthquakes with magnitudes above 4 in Richter scale. The EQ‐PD technique is able to detect precursors in 17 out of 21 earthquakes while generating 7 false alarms during the validation period of 266 days and 22 out of 24 earthquakes while generating 13 false alarms during the test period of 282 days.

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Section: Radio Sciencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning‐Based Detection of Earthquake Precursors Using Ionospheric Data

Akyol

Arıkan

2020

Radio Science

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“…The most significant value of this paper is its computing procedure (in processing big data) which can be further evolved for an uncalibrated earthquake prediction. According to the scholar paper written by Asencio-Cortes, Morales-Esteban & Martinez-Alvarez [46], -generalized linear models (GLM) were finally used in combination with a model fitting based on the AIC measure for predicting the probability of near-fault earthquake ground motion pules.‖ During the prediction, there was a set of four machine learning-based regressors:…”

Section: Remarksmentioning

confidence: 99%

Students’ Digital Equality and Scholarly Outcomes

Shun¹

2020

SJPMS

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“…Over the past few decades, earthquake prediction research attracts the attention of the scientific community due to the devastating nature of this phenomenon. However, there is no obvious success so far, due to the involvement of many variables [1][2][3][4]. Therefore, it is imperative to conduct research in this direction to mitigate the associated geohazards and minimize the loss of life and property.…”

Section: Introductionmentioning

confidence: 99%

Implication of Radon Monitoring for Earthquake Surveillance Using Statistical Techniques: A Case Study of Wenchuan Earthquake

et al. 2020

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The seismotectonically induced changes in groundwater radon (Rn) are considered to be strong imputes for the surveillance of imminent major earthquakes. Groundwater facilitates the migration of soil gases as a result of tectonic stresses. In this regard, a radon time series is statistically analysed to identify the radon anomalies possibly induced by Wenchuan earthquake. The statistical analysis mainly involves the deterministic analysis of the Rn data and residual Rn analysis using a criterion x¯±2σ of anomaly selection having a confidence interval of 95%. The deterministic analysis reveals that the Rn time series follows a persistent trend 0.5≤H≤1 which confirms the absence of a chaotic regime. On the other hand, the residual Rn shows a notable upsurge straddling the time of the Wenchuan earthquake in the form of pre- and post earthquake changes at monitoring stations having RE/RD≤0.3. The residual Rn level passes the anomaly selection criterion x¯±2σ and is declared as a tectonically induced Rn anomaly. Contrary to this, the response of distant monitoring stations (RE/RD>0.3) to this particular earthquake further validates the link between Rn and earthquake activity. In a nutshell, the present study highlights the potential implications of earthquake-induced radon anomalies for earthquake prediction research.

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Improving Earthquake Prediction with Principal Component Analysis: Application to Chile

Cited by 19 publications

References 19 publications

A Machine Learning‐Based Detection of Earthquake Precursors Using Ionospheric Data

A Machine Learning‐Based Detection of Earthquake Precursors Using Ionospheric Data

Students’ Digital Equality and Scholarly Outcomes

Implication of Radon Monitoring for Earthquake Surveillance Using Statistical Techniques: A Case Study of Wenchuan Earthquake

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