A research project from Yonsei University Computer Science Department focused on deep learning-based noise removal for seismic signals. Our goal is to overcome the limitations of traditional filter-based methods and improve micro-earthquake detection performance in complex urban and industrial environments.

• Limitations of traditional filter-based methods: Difficulty handling nonlinear noise patterns in complex environments
• Detection failures in urban/industrial areas: Reduced micro-earthquake detection accuracy due to high background noise
• Real-time processing requirements: Need for rapid response in earthquake early warning systems

• 🧠 Deep learning-based nonlinear pattern recognition: Robust signal restoration in complex noise environments
• 📊 Real-world data utilization: Effectiveness validation through public datasets
• 🚨 Early warning system improvement: Enhanced accuracy for life and property protection
• 🔬 Technical innovation: Novel approaches in a relatively underexplored field

seismic-denoising-research/
├── 📝 seismic-denoising-paper/     # Paper manuscripts and presentations
├── 🗃️ seismic-datasets/            # Seismic dataset management
├── 🧠 deep-learning-models/        # Deep learning model implementations
├── 🔬 seismic-experiments/         # Experimental design and comparative analysis
├── 📊 results-analysis/            # Results analysis and visualization
├── 🛠️ seismic-utils/               # Reusable utilities
├── 🌐 seismic-web-demo/            # Web-based demo application
├── 📚 seismic-literature/          # Literature review and related research
├── 🔄 reproducibility-kit/        # Reproducible research tools
└── 🎯 early-warning-system/       # Early warning system prototype

• STEAD: Stanford Earthquake Dataset (1.2M+ earthquake records)
• INSTANCE: Italian Seismic Dataset (54K+ events)
• DiTing: Chinese Earthquake Dataset (2.7M+ waveforms)
• Custom Urban Data: Urban environment noise data

• Autoencoder: Basic noise removal model
• U-Net: Architecture inspired by medical imaging
• Attention Mechanism: Focus on important signal segments
• Transformer: Time series pattern learning
• Hybrid Models: Multi-model ensemble

• Signal-to-Noise Ratio (SNR): Signal quality measurement
• Cross-correlation: Similarity with original signal
• Detection Accuracy: Earthquake detection accuracy
• Computational Efficiency: Real-time processing capability

# Clone repository
git clone https://github.com/your-org/seismic-denoising-research.git
cd seismic-denoising-research

# Create virtual environment
conda create -n seismic-denoising python=3.8
conda activate seismic-denoising

# Install dependencies
pip install -r requirements.txt

# Download datasets (e.g., STEAD)
cd seismic-datasets
python download_stead.py

# Data preprocessing
python preprocessing/data_loader.py --dataset stead --output processed_data/

# Train basic autoencoder model
cd deep-learning-models
python training/train_autoencoder.py --config configs/autoencoder_config.yaml

# Train U-Net model
python training/train_unet.py --config configs/unet_config.yaml

# Performance evaluation
cd seismic-experiments
python comparative_analysis/snr_comparison.py

# Results visualization
cd results-analysis
jupyter notebook notebooks/result_visualization.ipynb

• SNR improvement: 15-25% enhancement over traditional filters
• Detection accuracy: 20-30% increase in micro-earthquake detection rate
• Processing speed: Real-time processing efficiency

• Stable signal restoration in complex urban environments
• Robustness against various noise types
• Improved reliability of earthquake early warning systems

• Public dataset collection
• Data preprocessing pipeline construction
• Noise characteristic analysis
• Baseline performance measurement

• Autoencoder baseline implementation
• U-Net architecture application
• Attention mechanism integration
• Model performance comparison

• Hyperparameter tuning
• Ensemble model development
• Real-time processing optimization
• Multi-environment testing

• Experimental results compilation
• Paper draft writing
• Peer review and revision
• Conference presentation preparation

• Python 3.8+: Primary development language
• MATLAB: Signal processing and validation
• JavaScript: Web demo frontend

• PyTorch: Main deep learning framework
• Lightning: Experiment management and scalability
• TensorBoard: Training monitoring

• NumPy/SciPy: Numerical computation
• Pandas: Data manipulation
• ObsPy: Seismological data processing
• HDF5: Large-scale data storage

• Matplotlib/Seaborn: Static visualization
• Plotly: Interactive visualization
• Jupyter: Data analysis notebooks

• Team Leader: Yonsei University, Division of Software, Sunjun Hwang
• Supervisor:
• Collaborators: Sehee Park, Gangmin Ko, Jiyoon Beak

• Data Processing: Seismic data preprocessing and noise synthesis
• Model Development: Deep learning architecture design and implementation
• Experimental Design: Comparative experiments and performance evaluation
• Paper Writing: Research results compilation and presentation

This project is distributed under the MIT License. See LICENSE file for details.

1. Fork this repository
2. Create a new feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Create a Pull Request

• Email: [sunjun7559012@yonsei.ac.kr]
• GitHub: sunjun

This research is supported by:

• Yonsei University Computer Science Department
• [Research Support Organizations - Add if applicable]

We thank Stanford University, INGV, and China Earthquake Administration for providing open datasets.

"Innovation in Earthquake Detection Technology for a Safer World"