Earthquake Forecasting Ensemble Methods for Merging Models - PowerPoint PPT Presentation

Jun 03, 2023 •197 likes •389 views

Earthquake Forecasting Ensemble Methods for Merging Models Alexander K. Christensen Dr. Maximilian J. Werner Motivation What is forecasting? What can we actually forecast? What can we do with this? We developed a new

Earthquake Forecasting Ensemble Methods for Merging Models Alexander K. Christensen Dr. Maximilian J. Werner
Motivation What is ‘forecasting’? • What can we actually forecast? • What can we do with this? • We developed a new strategy for combining models. •
Canterbury Earthquake Sequence, NZ • CSEP • Sept 2010 – Dec 2011 • Why this Sequence? - Complex but well-documented series - Very destructive - Significant aftershocks • Our dataset begins 1s after Darfield M7.1 event.
Experiment Design
Base Models 3 types • 1. Physical – e.g. stress modelling 2. Statistical – e.g. smoothing/clustering 3. Hybrids Our portfolio: • - 5 physical - 6 statistical - 4 hybrid - 15 total
Model Ensembling “The information gains of the best multiplicative ensembles are greater than those of additive ensembles constructed from the same models.”
Optimised Log-Linear Pooling
Existing Ensembles
Results
Weights from Existing Ensembles
Comparison to Existing Ensembles
Performance Ranking
Performance Ranking
Performance Ranking
Performance Ranking
Discussion & Implications • Multiplicative approach – verdict? - Competitive - First effort – could improve further! - Slower - Mustn’t overinterpret! • Future directions - Other earthquake sequences - Deeper analysis
Machine Learning • Insufficient data - Weights only 300 data points. - Possibly with more models + time windows (e.g. daily)
Conclusion • Optimised Log-Linear Pooling is effective on this dataset - Merits further study/improvement - Other multiplicative approaches? • Machine learning not yet appropriate - Need more data

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