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Machine learning versus ETAS for earthquake forecasting in the Sea of Marmara: a leakage-audited negative result and a closed-form scoring artifact
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Abstract
We ask whether machine learning adds 30-day earthquake-forecasting value beyond modern ETAS in the Sea of Marmara, a locked seismic gap beneath Istanbul, once leakage and scoring artifacts are controlled. On a homogenized KOERI catalog, feature causality is machine-checkable, and comparisons follow a pre-specified rule: paired block-bootstrap intervals on likelihood and ranking must agree. The ETAS×ML hybrid adds no ranking skill over a well-fit ETAS cascade. Its likelihood edge is inseparable from a scoring artifact with a closed form: count-scoring a clustered forecast against binary occurrence makes a(h) = h−1−ln h nats per positive recoverable by pure rescaling. Under a proper binary score, a small occurrence-recalibration edge survives, resolved in active cells and absent at M≥3.5; an ablation shows catalog features cannot reconstruct ETAS, and ranking information lives on the ETAS axis alone. The 2025 Mw 6.2 epicentral cell ranked in the top ~1–2% all year; the only escalation was a foreshock 36 minutes before rupture, and ~220 comparable alarms were followed by no M≥6.
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 12:00
Last Updated: 2026-07-24 12:39
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License
CC BY Attribution 4.0 International
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Conflict of interest statement:
The authors declare no competing interests.
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