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Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

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Authors

Basri Kerem Alhan , Kenessary Khabat

Abstract

The Sea of Marmara’s locked central fault lies beneath ~18 million people; the 2025 Mw 6.2 Kumburgaz earthquake renewed forecasting interest. We present a leakage-audited benchmark on a homogenized, strictly causal KOERI catalogue: causality is machine-checkable (a truncated-catalogue self-test), and every comparison is adjudicated by a pre-specified block bootstrap requiring both paired intervals to exclude zero. Four physics forecasters (first-generation, cascade Monte-Carlo, spatially-variable-background, and an independent ETAS inversion) form a stable cluster whose verdicts never move: the first three are mutually inseparable, and first-generation beats the inversion. The ETAS×ML hybrid has no robust standing against them: re-running across a narrow range of one calibration constant swings its verdict versus physics between inseparable and beaten, so the machine-learning stage adds no reliable value over a well-fit ETAS. Two engineered channels (GNSS, dense sub-Mc3 catalogue) gave interval-significant gains a placebo battery voided. The Mw 6.2 cell ranked top-1% all year, but temporal information arrived only with its ML 4.0 foreshock 36 minutes before rupture. CSEP tests show the top-ranked forecasters over-predict counts at the honest calibration; the live 30-day regional M≥6 probability is ~1%.

DOI

https://doi.org/10.31223/X5W78X

Subjects

Geophysics and Seismology, Physical Sciences and Mathematics

Keywords

earthquake forecasting, ETAS, machine learning, data leakage, Sea of Marmara, operational earthquake forecasting, foreshocks, information gain, seismology, causal validation, aftershock forecasting, earthquake prediction, Marmara Fault, KOERI catalogue

Dates

Published: 2026-07-06 17:00

Last Updated: 2026-07-12 14:15

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
The authors declare no competing interests.

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