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A probabilistic neural network for earthquake magnitude prediction
1Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 2070 Neil Avenue, Columbus, OH 43210, USA. adeli.1@osu.edu
Summary
A new probabilistic neural network (PNN) predicts earthquake magnitudes using seismicity indicators. This model shows good accuracy for predicting earthquakes between magnitudes 4.5 and 6.0.
Area of Science:
- Geophysics
- Seismology
- Artificial Intelligence
Background:
- Accurate earthquake prediction remains a significant challenge in seismology.
- Existing models often focus on specific magnitude ranges.
Purpose of the Study:
- To develop and evaluate a probabilistic neural network (PNN) for predicting the largest earthquake magnitude within a future time period.
- To utilize seismicity indicators for improved earthquake forecasting.
Main Methods:
- A PNN model was developed using eight seismicity indicators derived from historical earthquake data.
- Model performance was assessed using probability of detection, false alarm ratio, and true skill score (R score).
- The model was trained and validated using Southern California seismic data.
Main Results:
- The PNN model demonstrated good prediction accuracy for earthquakes in the magnitude 4.5 to 6.0 range.
- The model complements previous recurrent neural network models effective for larger magnitude predictions (>6.0).
Conclusions:
- The developed PNN offers a valuable tool for forecasting moderate-magnitude earthquakes.
- This approach enhances seismic hazard assessment by providing predictions for a specific magnitude range.
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