Earthquake Signal Detection Using Neural Networks

Harvard EPS55

Schematic representation of a fully connected neural network for seismic event classification

Input Seismic Signal Time (s) Amplitude Feature Extraction Input Layer f₁ f₂ f₃ f₄ f₅ Hidden Layer 1 Hidden Layer 2 Output Layer E N Softmax Detection Probability Normal Earthquake 0.25 0.75 θ=0.5 Features: f₁: Peak amplitude f₂: Dominant frequency f₃: Signal duration f₄: Zero-crossing rate f₅: Energy content E: Earthquake N: Normal ⚠ Earthquake Detected

Advantages

  • Automatic Feature Learning: Neural networks can automatically discover relevant patterns in seismic data without manual feature engineering
  • High Accuracy: Can achieve detection rates >95% with low false positive rates when properly trained
  • Real-time Processing: Once trained, can process incoming seismic data in milliseconds for rapid alerts
  • Noise Robustness: Can distinguish earthquake signals from various types of background noise (traffic, construction, etc.)
  • Multi-station Integration: Can process data from multiple seismic stations simultaneously for improved accuracy
  • Continuous Improvement: Performance improves as more labeled earthquake data becomes available for training
  • P/S Wave Discrimination: Can identify different wave types and estimate earthquake parameters

Disadvantages

  • Training Data Requirements: Needs large datasets of labeled earthquakes which may be limited for rare event types
  • Black Box Nature: Difficult to interpret why specific decisions are made, limiting scientific insight
  • Generalization Issues: May perform poorly on earthquake types or locations not well-represented in training data
  • Computational Resources: Training requires significant computational power and time
  • False Alarms: May trigger on unusual but non-earthquake events (explosions, mine blasts)
  • Hyperparameter Tuning: Requires expertise to optimize network architecture and training parameters
  • Overfitting Risk: May memorize training data rather than learning generalizable patterns

Key Consideration: Neural networks work best as part of a comprehensive earthquake detection system, combining their pattern recognition capabilities with traditional seismological methods and expert validation.