Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi
Bibliographic record
Abstract
SUMMARY Historical and instrumental earthquake catalogs in low strain rate regions are not necessarily indicative of the long-term spatio-temporal distribution of seismicity. This implies that probabilistic seismic hazard analysis (PSHA) should also consider geologic and geodetic data through fault-based seismogenic sources. However, it is not always clear how on-fault magnitude-frequency distributions (MFDs) should be described and, if the seismogenic layer is especially thick, how fault sources should be extrapolated down-dip. We explore these issues in the context of a new PSHA for Malawi, where regional extensional rates are 0.5–2 mm yr−1, the seismogenic layer is 30–40-km thick, the instrumental catalog is ∼60 yr long and fault-based sources were recently collated in the Malawi Seismogenic Source Model. Furthermore, Malawi is one of several countries along the East African Rift where exposure to seismic hazard is growing, but PSHA does not typically consider fault sources. We use stochastic event catalogs to explore different fault source down-dip extents and MFDs. Our PSHA indicates that hazard levels are highest for a Gutenberg–Richter on-fault MFD, even at low probabilities of exceedance (2 per cent in 50 yr), whilst seismic hazard levels are also sensitive to how relatively short (<50 km) fault sources are extrapolated down-dip. For sites close to fault sources (<40 km), seismic hazard levels are doubled compared to previous instrumental-seismicity based PSHA in Malawi. Cumulatively, these results highlight the need for careful fault source modelling in PSHA of low strain rate regions and the need for new fault-based PSHA elsewhere in the East Africa Rift.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".