Abstract
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.
Highlights
Seismic signal analysis is one of the significant problems in geology
Several studies have been conducted to discriminate between earthquakes and artificial explosions, and the results have exhibited the importance of seismic discrimination on the grounds of preparation and damage minimization caused by an earthquake
The main contribution of this research was the outstanding performance of seismic discrimination using machine learning in specific circumstances
Summary
Seismic signal analysis is one of the significant problems in geology. In particular, several studies have been conducted to discriminate between earthquakes and artificial explosions, and the results have exhibited the importance of seismic discrimination on the grounds of preparation and damage minimization caused by an earthquake. To overcome the nonlinear data distribution caused by the similarity between earthquake and artificial explosion signals, the regularized logistic regression (RLR) and kernel methods for SVM are applied to construct seismic discrimination system. For supervised machine-learning methods to improve the seismic discrimination accuracy, the feature vector is obtained from the time- and frequency-domain-based amplitude ratio. Differences in the incidence rate lead to the imbalances in the dataset that can bring unintended error in designing a machine-learning-based seismic classifier. The supervised training techniques derived in this paper are compared and evaluated to identify an optimal machine-learning-based seismic classifier between earthquakes and artificial explosions.
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