Conformal Prediction for Earthquake Early Warning: Finite-Sample Coverage Guarantees for Prediction Intervals of Peak Ground Acceleration

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ID: 331492
2026
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Abstract
Summary Earthquake early warning requires reliable uncertainty estimates from short initial waveforms. We evaluate Split conformal prediction (CP) and conformalized quantile regression (CQR) for single-station peak ground acceleration using event-partitioned K-NET records. Each 3 s waveform is normalized by component while retaining amplitude scales measured before normalization. The convolutional neural network (CNN)–Transformer that retains amplitude information is more accurate and yields narrower intervals than the waveform-only ablation. At the nominal 90 per cent level, CQR attains 90.03 per cent coverage; raw quantile regression and Split CP are also near nominal, whereas heteroscedastic Deep Ensemble and MC-Dropout baselines are more conservative and wider. Event-cluster bootstrap and repeated event-level resplits support marginal-coverage stability, although performance degrades for rare large earthquakes. Upper-bound screening trades increased recall for more false alarms, so the intervals are an uncertainty-quantification and screening layer rather than a standalone alert system. Under exact exchangeability, conformal prediction supplies finite-sample marginal coverage; record-level exchangeability is not established for this event-clustered sample, and event-conditional reliability is not guaranteed.
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Authors Yeong-Hyeon Shin, Woodon Jeong
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Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag415
URL
https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggag415/71606343/ggag415.pdf
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