Seismic detection using support vector machines

Ruano, A. E.; Madureira, Guilherme; Barros, Ozias; Khosravani, H.; Ruano, M.G.; Ferreira, Pedro

doi:10.1016/j.neucom.2013.12.020

Cited by 60 publications

(41 citation statements)

References 23 publications

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“…Automatic classification methods have been developed for detecting the sources in volcanic areas [Langer et al, 2006;Curilem et al, 2009] to differentiate earthquakes and blasts [Fäh and Koch, 2002;Laasri et al, 2015] or for characterizing large rockslides [Dammeier et al, 2016]. For multiclass problems, many classifiers were used such as hidden Markov models (HMMs), artificial neural networks, and support vector machines (SVMs), mainly on a reduced number of seismic attributes [Curilem et al, 2009;Hibert et al, 2014;Ruano et al, 2014]. Recently, some studies [Beyreuther and Wassermann, 2008;Ruano et al, 2014;Quang et al, 2015] focused on the classification of continuous seismic records discriminating the background noise from the signal of interest.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier

Provost

Hibert

Malet

2017

Geophysical Research Letters

119

132

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The deformation of slow‐moving landslides developed in clays induces endogenous seismicity of mostly low‐magnitude events (ML<1). Long seismic records and complete catalogs are needed to identify the type of seismic sources and understand their mechanisms. Manual classification of long records is time‐consuming and may be highly subjective. We propose an automatic classification method based on the computation of 71 seismic attributes and the use of a supervised classifier. No attribute was selected a priori in order to create a generic multi‐class classification method applicable to many landslide contexts. The method can be applied directly on the results of a simple detector. We developed the approach on the seismic network of eight sensors of the Super‐Sauze clay‐rich landslide (South French Alps) for the detection of four types of seismic sources. The automatic algorithm retrieves 93% of sensitivity in comparison to a manually interpreted catalog considered as reference.

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Section: Introductionmentioning

confidence: 99%

Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier

Provost

Hibert

Malet

2017

Geophysical Research Letters

119

132

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“…Regarding recent credit scoring techniques, artificial neural network [4,5] has been criticized for its poor performance when incorporating irrelevant attributes or small data sets, while support vector machine, motivated by statistical learning theory [6,7], is particularly well suited for coping with a large number of explanatory attributes or sparse data sets [8][9][10][11]. Baesens et al studied the performance of various state-of-the-art classification algorithms on eight real-life credit scoring data sets [12].…”

Section: Introductionmentioning

confidence: 99%

Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

Shi

2016

JRFM

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Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM) credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD) is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method.

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“…We used a preprocessing stage, which removes noise and nonvolcanic originated signals such as thunder, by using bandpass filters [11]. Then, a stage capable of detecting volcanic events is proposed [12], and it is followed by a feature extraction [13] and feature selection stage [14]. Following the state-of-the-art of preceding studies in geophysics, the use of filter and embedded methods allowed us to determine the main features in time, frequency, and scale domains.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Lara-Cueva

Benítez

Ruiz

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Geophysics experts are interested in understanding the behavior of volcanoes and forecasting possible eruptions by monitoring and detecting the increment on volcano-seismic activity, with the aim of safeguarding human lives and material losses. This paper presents an automatic volcanic event detection and classification system, which considers feature extraction and feature selection stages, to reduce the processing time toward a reliable real-time volcano early warning system (RT-VEWS). We built the proposed approach in terms of the seismicity presented in 2009 and 2010 at the Cotopaxi Volcano located in Ecuador. In the detection stage, the recordings were time segmented by using a nonoverlapping 15-s window, and in the classification stage, the detected seismic signals were 1-min long. For each detected signal conveying seismic events, a comprehensive set of statistical, temporal, spectral, and scale-domain features were compiled and extracted, aiming to separate long-period (LP) events from volcano-tectonic (VT) earthquakes. We benchmarked two commonly used types of feature selection techniques, namely, wrapper (recursive feature extraction) and embedded (cross-validation and pruning). Each technique was used within a suitable and appropriate classification algorithm, either the support vector machine (SVM) or the decision trees. The best result was obtained by using the SVM classifier, yielding up to 99% accuracy in the detection stage and 97% accuracy and sensitivity in the event classification stage. Selected features and their interpretation were consistent among different input spaces in simple terms of the spectral content of the frequency bands at 3.1 and 6.8 Hz. A comparative analysis showed that the most relevant features for automatic discrimination between LP and VT events were one in the time domain, five in the frequency domain, and nine in the scale domain. Our study provides the framework for an event classification system with high accuracy and reduced computational requirements, according to the orientation toward a future RT-VEWS.

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Seismic detection using support vector machines

Cited by 60 publications

References 23 publications

Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier

Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier

Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

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