Modeling Rupture Directivity Effects on Ground Motion Using Neural Networks
Description
We use Artificial Neural Networks (ANNs) to more accurately modeling ground motion amplification effects caused by rupture directivity during earthquakes. These effects are complex and challenging to capture using traditional methods, which can impact the accuracy of seismic hazard assessments.
Detailed example
The outputs here are adjustments to the median and standard deviation of ground-motion model predictions. The AI system predicts the amplified ground motion effects due to rupture directivity and are used to refine seismic hazard assessments in a computationally lightweight manner.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
computationally efficient method to improve seismic hazard modeling by incorporating rupture directivity effects
Controls / human review
ATO: Yes; PIA: Not published
Data needed
The training data was derived from a publicly available rupture directivity model.