Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques

Yildirim, Eray; Gülbağ, Ali; Horasan, Gündüz; Doğan, Emrah

doi:10.1016/j.cageo.2010.09.005

Cited by 51 publications

(21 citation statements)

References 33 publications

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“…Seismometers at seismic stations record all types of earth vibrations in the region without the ability to clarify their origin. Considering that misidentified artificial seismic events, such as quarry blasts and underground nuclear tests, can lead to erroneous analyses, the classification of the signals' source should be performed as a preliminary work prior to seismic signal processing and analysis [1]. General seismic discrimination is usually performed by visually inspecting the records of earthquakes and explosions or by calculating the characteristics of each record.…”

Section: Introductionmentioning

confidence: 99%

Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Kim

Lee

You

2020

Sensors

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The discrimination between earthquakes and explosions is a serious issue in seismic signal analysis. This paper proposes a seismic discrimination method using support vector machine (SVM), wherein the amplitudes of the P-wave and the S-wave of the seismic signals are selected as feature vectors. Furthermore, to improve the seismic discrimination performance using a heterodyne laser interferometer for seismic wave detection, the Hough transform is applied as a compensation method for the periodic nonlinearity error caused by the frequency-mixing in the laser interferometric seismometer. In the testing procedure, different kernel functions of SVM are used to discriminate between earthquakes and explosions. The outstanding performance of a laser interferometer and Hough transform method for precision seismic measurement and nonlinearity error compensation is confirmed through some experiments using a linear vibration stage. In addition, the effectiveness of the proposed discrimination method using a heterodyne laser interferometer is verified through a receiver operating characteristic curve and other performance indices obtained from practical experiments.

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Section: Introductionmentioning

confidence: 99%

Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Kim

Lee

You

2020

Sensors

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“…As interest in earthquakes increases, more research continues to be conducted on seismic signal analysis around the world. In the field of seismic signal analysis, it is important to distinguish artificial seismic data such as quarry explosions and nuclear tests from natural earthquake data [1]. Many statistical machine learning-based methods have been proposed to make seismic signal classification more efficient and automated.…”

Section: Introductionmentioning

confidence: 99%

Seismic Discrimination between Earthquakes and Explosions Using CART Algorithm

Lee¹,

Lee²,

You³

2019

IJMMM

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The distinction between earthquake and explosion signals is an essential issue in seismic signal analysis. We propose a machine learning model of decision tree (DT) applied to discriminate between earthquakes and explosions. The amplitudes of the P-wave and the S-wave are selected as feature vectors and built into the database. Classification and regression trees (CART) algorithm is used in our method, which is built through a greedy approach by the Gini impurity. The performance of the DT model using the CART algorithm is evaluated with the ROC curve. The results show the advantages of DT according to various evaluation indexes based on confusion matrix, and demonstrate that DT is efficient in seismic signal discrimination due to the nonparametric model characteristics of DT model.

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“…The latter can be extracted from time-domain representation of the signal, time frequency representation or spectral representation. Spectral ratios P/S and P/L are commonly presented as good discriminants between earthquakes and quarry blasts [3] [4] [5] [6]. Nevertheless, due to the low magnitude and overlapped P and S waves of quarry blast events, we cannot use the previous discriminant methods.…”

Section: Introductionmentioning

confidence: 99%

Seismic Signal Discrimination between Earthquakes and Quarry Blasts Using Fuzzy Logic Approach

Laasri

Akhouayri

Agliz

et al. 2012

Lecture Notes in Computer Science

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Seismic analysts identify earthquakes signals from those of explosions by visual inspection and calculating some characteristics of seismogram. Such work supposes a great deal of workload for seismic analysts. Therefore, an automatic classification tool reduces dramatically this arduous task, turns classification reliable, removes errors associated to tedious evaluations and changing of personnel. In this present paper we are interested in transforming the analysts' knowledge of classifying seismograms into an automated soft classification system based on fuzzy logic. This is primarily due to its capability of modelling human reasoning and decision-making, managing complexity and controlling computational cost. These capabilities are essential for manipulating high dimensionality and complexity of seismic signal. To conduct effective discrimination, relevant seismogram characteristics are extracted based on human experience. Using these characteristics, a fuzzy classifier is built and tested with real seismic data. The results are found to be encouraging.

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Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques

Cited by 51 publications

References 33 publications

Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Seismic Discrimination between Earthquakes and Explosions Using CART Algorithm

Seismic Signal Discrimination between Earthquakes and Quarry Blasts Using Fuzzy Logic Approach

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