Application of Convolutional Neural Network to GIS and Physics

Area of Science:

• Geosciences
• Artificial Intelligence
• Spatial Information Systems

Background:

• Natural disasters like earthquakes pose significant threats to human life and property.
• Traditional seismic data detection methods struggle with increasing data volume and complex nonlinear relationships.
• Geographic Information Systems (GIS) offer capabilities for collecting, storing, and managing spatial data.

Purpose of the Study:

• To evaluate the effectiveness of integrating GIS with artificial intelligence for seismic data analysis.
• To improve the accuracy and efficiency of earthquake aftershock detection and prediction.
• To explore the application of convolutional neural networks (CNNs) in processing complex seismic waveforms.

Main Methods:

• Utilized a dataset of 14,000 Wenchuan earthquake events and 8,800 aftershock events.
• Employed Geographic Information Systems (GIS) for the collection and management of seismic physical signals.
• Applied convolutional neural networks (CNNs) for pattern matching and prediction on seismic waveform data.

Main Results:

• The integration of GIS and CNNs achieved a training and detection accuracy exceeding 90%.
• CNNs demonstrated superior detection accuracy and recall rates compared to traditional training methods.
• The combined approach successfully identified a large number of microseismic events often missed by manual selection.

Conclusions:

• GIS is effective in intercepting and collecting seismic physical signals for analysis.
• CNNs combined with GIS data provide a highly accurate and efficient method for seismic event detection.
• This advanced methodology enhances the understanding and prediction of earthquake aftershocks, improving disaster preparedness.