Design and Implementation of a 25-Year Pseudo-Prospective Earthquake Forecasting Experiment in China (AoyuX)
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| Format: | Preprint |
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2025
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| _version_ | 1866914340797415424 |
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| author | Li, Jiawei Zhang, Qingyuan Sornette, Didier |
| author_facet | Li, Jiawei Zhang, Qingyuan Sornette, Didier |
| contents | Forecast models in statistical seismology are commonly evaluated with log-likelihood scores of the full distribution P(n) of earthquake numbers, yet heavy tails and out-of-range observations can bias model ranking. We develop a tail-aware evaluation framework that estimates cell-wise P(n) using adaptive Gaussian kernel density estimation and tests three strategies for handling out-of-range counts. Using the AoyuX platform, we perform a ~25-year month-by-month pseudo-prospective forecast experiment in the China Seismic Experimental Site (CSES), comparing Epidemic-Type Aftershock Sequence (ETAS) model with a homogeneous background (ETASμ) to a spatially heterogeneous variant (ETASμ(x,y)) across six spatial resolutions and five magnitude thresholds. Empirical probability density functions (PDFs) of counts per cell are well described by power laws with exponents a = 1.40 +- 0.21 across all settings. Using previous theoretical results, this provides a robust estimate of the productivity exponent, α = 0.57 +- 0.08 using a b-value equal to 0.8, providing a valuable quantification of this key parameter in aftershock modeling. Model ranking is sensitive to how the tail of the full distribution P(n) of earthquake counts is treated: power law extrapolation is both theoretically justified and empirically the most robust. Cumulative information gain (CIG) shows that ETASμ(x,y) outperforms ETASμ in data-rich configurations, whereas in data-poor settings stochastic fluctuations dominate. A coefficient-of-variation analysis of per-window log-likelihood differences distinguishes genuine upward trends in CIG from noise-dominated fluctuations. By aligning a fat-tail-aware scoring methodology with an open testing platform, our work advances fair and statistically grounded assessment of earthquake forecasting models for the CSES and beyond. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_14407 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design and Implementation of a 25-Year Pseudo-Prospective Earthquake Forecasting Experiment in China (AoyuX) Li, Jiawei Zhang, Qingyuan Sornette, Didier Geophysics Forecast models in statistical seismology are commonly evaluated with log-likelihood scores of the full distribution P(n) of earthquake numbers, yet heavy tails and out-of-range observations can bias model ranking. We develop a tail-aware evaluation framework that estimates cell-wise P(n) using adaptive Gaussian kernel density estimation and tests three strategies for handling out-of-range counts. Using the AoyuX platform, we perform a ~25-year month-by-month pseudo-prospective forecast experiment in the China Seismic Experimental Site (CSES), comparing Epidemic-Type Aftershock Sequence (ETAS) model with a homogeneous background (ETASμ) to a spatially heterogeneous variant (ETASμ(x,y)) across six spatial resolutions and five magnitude thresholds. Empirical probability density functions (PDFs) of counts per cell are well described by power laws with exponents a = 1.40 +- 0.21 across all settings. Using previous theoretical results, this provides a robust estimate of the productivity exponent, α = 0.57 +- 0.08 using a b-value equal to 0.8, providing a valuable quantification of this key parameter in aftershock modeling. Model ranking is sensitive to how the tail of the full distribution P(n) of earthquake counts is treated: power law extrapolation is both theoretically justified and empirically the most robust. Cumulative information gain (CIG) shows that ETASμ(x,y) outperforms ETASμ in data-rich configurations, whereas in data-poor settings stochastic fluctuations dominate. A coefficient-of-variation analysis of per-window log-likelihood differences distinguishes genuine upward trends in CIG from noise-dominated fluctuations. By aligning a fat-tail-aware scoring methodology with an open testing platform, our work advances fair and statistically grounded assessment of earthquake forecasting models for the CSES and beyond. |
| title | Design and Implementation of a 25-Year Pseudo-Prospective Earthquake Forecasting Experiment in China (AoyuX) |
| topic | Geophysics |
| url | https://arxiv.org/abs/2510.14407 |