Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Kim, Sangkyeum; Lee, Kyunghyun; You, Kwanho

doi:10.3390/s20071879

Cited by 13 publications

(9 citation statements)

References 29 publications

(33 reference statements)

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“…where α i is the Lagrange multiplier, that is nonzero for support vectors [17]. According to the complementary slackness and stationarity in the KKT condition and dual solution, the decision function is obtained as…”

Section: Support Vector Machinementioning

confidence: 99%

“…Lagrange multipliers in the Karush–Kuhn–Tucker (KKT) condition are applied to maximize the objective function. Equation ( 5 ) can be transformed into

, which represents the converted form of the minimization, and the Lagrange function is

where

is the Lagrange multiplier, that is nonzero for support vectors [ 17 ]. According to the complementary slackness and stationarity in the KKT condition and dual solution, the decision function is obtained as

…”

Section: Implementation Of Adasyn-based Classification Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Imbalanced Seismic Event Discrimination Using Supervised Machine Learning

Ahn

Kim

Lee

et al. 2022

Sensors

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The discrimination between earthquakes and artificial explosions is a significant issue in seismic analysis to efficiently prevent and respond to seismic events. However, the discrimination of seismic events is challenging due to the low incidence rate. Moreover, the similarity between earthquakes and artificial explosions with a local magnitude derives a nonlinear data distribution. To improve the discrimination accuracy, this paper proposes machine-learning-based seismic discrimination methods—support vector machine, naive Bayes, and logistic regression. Furthermore, to overcome the nonlinear separation problem, the kernel functions and regularized logistic regression are applied to design seismic classifiers. To efficiently design the classifier, P- and S-wave amplitude ratios on the time domain and spectral ratios on the frequency domain, which is converted by fast Fourier transform and short-time Fourier transform are selected as feature vectors. Furthermore, an adaptive synthetic sampling algorithm is adopted to enhance the classifier performance against the seismic data imbalance issue caused by the non-equivalent number of occurrences. The comparisons among classifiers are evaluated by the binary classification performance analysis methods.

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Section: Support Vector Machinementioning

confidence: 99%

“…Lagrange multipliers in the Karush–Kuhn–Tucker (KKT) condition are applied to maximize the objective function. Equation ( 5 ) can be transformed into

, which represents the converted form of the minimization, and the Lagrange function is

where

…”

Section: Implementation Of Adasyn-based Classification Algorithmsmentioning

confidence: 99%

Imbalanced Seismic Event Discrimination Using Supervised Machine Learning

Ahn

Kim

Lee

et al. 2022

Sensors

Self Cite

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“…There have also been several studies using recently developed deep learning based approaches to distinguish explosions from natural earthquakes (Kim et al., 2020; Kong et al., 2021; Linville et al., 2019; Magana‐Zook & Ruppert, 2017; Tibi et al., 2019). Linville et al.…”

Section: Introductionmentioning

confidence: 99%

“…There have also been several studies using recently developed deep learning based approaches to distinguish explosions from natural earthquakes (Kim et al, 2020;Kong et al, 2021;Linville et al, 2019;Magana-Zook & Ruppert, 2017;Tibi et al, 2019). Linville et al (2019) used convolutional and recurrent neural networks with spectrograms from seismic sensors as the input to classify explosions and tectonic sources at local distances, achieving 99% accuracy in terms of the source type discrimination.…”

mentioning

confidence: 99%

Combining Deep Learning With Physics Based Features in Explosion‐Earthquake Discrimination

Kong

Wang

Walter

et al. 2022

Geophysical Research Letters

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This paper combines the power of deep‐learning with the generalizability of physics‐based features, to present an advanced method for seismic discrimination between earthquakes and explosions. The proposed method contains two branches: a deep learning branch operating directly on seismic waveforms or spectrograms, and a second branch operating on physics‐based parametric features. These features are high‐frequency P/S amplitude ratios and the difference between local magnitude (ML) and coda duration magnitude (MC). The combination achieves better generalization performance when applied to new regions than models that are developed solely with deep learning. We also examined which parts of the waveform data dominate deep learning decisions (i.e., via Grad‐CAM). Such visualization provides a window into the black‐box nature of the machine‐learning models and offers new insight into how the deep learning derived models use data to make decisions.

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“…Apart from seismological procedures, ML can provide a lot of support in discriminating tectonic seismological events from non-tectonic. Supervised ML algorithms need annotated data for classifying the type of seismic events into tectonic or non-tectonic categories [17]. In the last decade, massive progress has been done in resolving crucial issues in several projects.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Assessments of Machine-Learning Methods for Artificial Seismic Sources Discrimination

et al. 2021

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Mankind is vulnerable to artificial seismic sources and accompanying explosions' consequences. Recently, seismicity catalog contamination is among the main problems faced by seismologists. Since identifying artificial seismic sources is the first and always challenging stage, it is imperative to develop an automated control system that will discriminate tectonic from non-tectonic events. Detection and removal of the artificial seismic sources have become urgent. Early treatments and cleaning of contaminated seismicity catalogs are crucial to assist in accurate seismic hazard identification and enhance the planning of future urban developments. With the advancement of machine learning (ML) techniques, artificial seismic source detection accuracy has been improved. Today, there are different kinds of methods, ML techniques, and diverse processes like knowledge discovery are developed for discriminating artificial seismic sources and earthquakes. ML techniques offer various probabilistic and statistical methods that allow intelligent systems to learn from reoccurring experiences to detect and identify patterns from a dataset. This study aims to build an automated system that is able to detect the existence of artificial seismic sources in seismicity catalogs. More concretely, we classify seismic activity reports into two classes using classical and ensemble ML algorithms. Classical seismicity parameters or features are supplied to linear and nonlinear ML classifiers. The proposed scheme based on the four features (Latitude, Longitude, depth, and Magnitude) can enhance the performance. To assure the enhanced performance, we have examined the proposed scheme by both the accuracy of each model, ROC curves, Precision-Recall, and Calibration. The obtained results prove that the ensemble learning algorithms exhibit better results compared to other classical ML algorithms by having 98.14% testing accuracy.

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Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Cited by 13 publications

References 29 publications

Imbalanced Seismic Event Discrimination Using Supervised Machine Learning

Imbalanced Seismic Event Discrimination Using Supervised Machine Learning

Combining Deep Learning With Physics Based Features in Explosion‐Earthquake Discrimination

Comparative Performance Assessments of Machine-Learning Methods for Artificial Seismic Sources Discrimination

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