Cross-fault rupture within a depth-segmented flower structure revealed by the 2024 Hualien earthquakes, eastern Taiwan
Bibliographic record
Abstract
Successive large seismic sequences near Hualien City, eastern Taiwan, offer rich insights into earthquake clustering behaviour. Here, we study the latest of these, the Mw 7.4 and 6.5 earthquakes in April 2024. A bespoke, relocated seismicity catalog, built with deep learning-enhanced phase picking and association, reveals several discrete aftershock trends. By fixing model fault geometries to these trends and inverting space geodetic data, we resolve slip along two steep, E-dipping planes, one on top of the other, which we interpret as depth-segmented strands of the northern Longitudinal Valley fault. These E-dipping planes are crossed and in places truncated by slip along the W-dipping Central Range fault, providing a rare example of cross-fault rupture in a single earthquake. Together with the Meilun and Lingding faults involved in earlier earthquakes in 1951 and 2018, the unusual array of criss-crossing fault planes delineates a complicated flower structure. A simpler, linear trend of aftershocks south of the 2024 mainshock may reflect that the southward-propagating fault zone is less structurally complex here than in the north. The 2024 earthquakes broke neighbouring fault strands to those involved in 1951 and 2018, highlighting how closely parallel structures can host large, complex earthquakes in relatively quick succession and challenging the simplest interpretation of the seismic gap hypothesis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".