# Time-Frequency-Based Separation of Earthquake and Noise Signals on Real Seismic Data: EMD, DWT and Ensemble Classifier Approaches

## TL;DR

This study shows that combining time-frequency methods with a Random Forest classifier can perfectly distinguish earthquake signals from noise in real seismic data.

## Contribution

A novel approach using EMD+DWT features with Lasso selection and Random Forest achieves perfect classification of earthquake and noise signals.

## Key findings

- Random Forest with Lasso-selected EMD+DWT features achieved 100% accuracy, specificity, and sensitivity.
- DWT and EMD+DWT features outperformed EMD alone in separating earthquake and noise signals.
- Tree-based classifiers outperformed k-NN and SVM in this task.

## Abstract

What are the main findings?
Time-frequency features extracted using EMD, DWT, and combined EMD+DWT effectively separate earthquake and noise signals.Random Forest classifier with Lasso-selected EMD+DWT features achieved 100% accuracy, specificity, and sensitivity.

Time-frequency features extracted using EMD, DWT, and combined EMD+DWT effectively separate earthquake and noise signals.

Random Forest classifier with Lasso-selected EMD+DWT features achieved 100% accuracy, specificity, and sensitivity.

What are the implication of the main findings?
Time-frequency-based feature extraction and selection improve real-time earth-quake detection.The approach provides a robust foundation for operational monitoring and ear-ly-warning systems.

Time-frequency-based feature extraction and selection improve real-time earth-quake detection.

The approach provides a robust foundation for operational monitoring and ear-ly-warning systems.

Earthquakes are sudden and destructive natural events caused by tectonic movements in the Earth’s crust. Although they cannot be predicted with certainty, rapid and reliable detection is essential to reduce loss of life and property. This study aims to automatically distinguish earthquake and noise signals from real seismic data by analyzing time-frequency features. Signals were scaled using z-score normalization, and extracted with Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT), and combined EMD+DWT methods. Feature selection methods such as Lasso, ReliefF, and Student’s t-test were applied to identify the most discriminative features. Classification was performed with Ensemble Bagged Trees, Decision Trees, Random Forest, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). The highest performance was achieved using the RF classifier with the Lasso-based EMD+DWT feature set, reaching 100% accuracy, specificity, and sensitivity. Overall, DWT and EMD+DWT features yielded higher performance than EMD alone. While k-NN and SVM were less effective, tree-based methods achieved superior results. Moreover, Lasso and ReliefF outperformed Student’s t-test. These findings show that time-frequency-based features are crucial for separating earthquake signals from noise and provide a basis for improving real-time detection. The study contributes to the academic literature and holds significant potential for integration into early warning and earthquake monitoring systems.