Seismic Data Augmentation Based on Conditional Generative Adversarial Networks.

Area of Science:

• Geophysics
• Machine Learning
• Seismology

Background:

• Deep learning models in seismology require large datasets for optimal performance.
• Data augmentation is crucial for enhancing deep learning model training.
• Existing methods for synthetic seismic data generation have limitations.

Discussion:

• This study introduces a novel GAN-based model for generating high-quality synthetic seismic waveforms.
• The model leverages conditional knowledge and statistical characteristics of real seismic data in embedding space.
• A content loss function is incorporated to refine synthetic data quality by relating high-level features.

Key Insights:

• The proposed GAN model effectively generates realistic synthetic seismic waveforms.
• Integrating synthetic data improved classification accuracy from 96.84% to 97.92%.
• Experimental results validate the model's efficacy for seismic waveform data augmentation.

Outlook:

• The developed model offers a promising approach for realistic seismic data augmentation.
• This technique can significantly advance deep learning applications in seismology.
• Further research can explore GANs for other geophysical data generation tasks.