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Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Imbalanced Seismic Event Discrimination Using Supervised Machine Learning.

Hyeongki Ahn¹, Sangkyeum Kim¹, Kyunghyun Lee¹

¹Department of Electrical Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea.

Sensors (Basel, Switzerland)

|March 26, 2022

Summary

This summary is machine-generated.

Distinguishing earthquakes from artificial explosions is crucial for seismic safety. Machine learning methods, including support vector machines and logistic regression, effectively improve seismic event discrimination, even with imbalanced data.

Keywords:

artificial explosion oversampling method seismic discrimination supervised machine learning

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Area of Science:

Geophysics
Seismology
Machine Learning

Background:

Discriminating between earthquakes and artificial explosions is vital for seismic event management.
Low incidence rates and similar local magnitudes create challenges in seismic event discrimination due to nonlinear data distributions.

Purpose of the Study:

To propose and evaluate machine learning-based methods for accurate seismic event discrimination.
To address the nonlinear data distribution and data imbalance issues in seismic analysis.

Main Methods:

Employed machine learning algorithms: support vector machine, naive Bayes, and logistic regression.
Utilized kernel functions and regularized logistic regression to handle nonlinear data separation.
Extracted feature vectors from P- and S-wave amplitude ratios (time domain) and spectral ratios (frequency domain) using Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT).
Applied an adaptive synthetic sampling algorithm to mitigate the impact of imbalanced seismic data.

Main Results:

Machine learning classifiers demonstrated improved accuracy in discriminating seismic events.
Kernel functions and regularized logistic regression effectively addressed nonlinear data separation.
The adaptive synthetic sampling algorithm enhanced classifier performance on imbalanced datasets.

Conclusions:

Machine learning approaches offer a robust solution for seismic event discrimination.
The proposed feature extraction and data balancing techniques significantly improve classifier performance.
Accurate seismic event discrimination is achievable through advanced computational methods.