Seismic Vulnerability in Yogyakarta Basin Based on HVSR Frequency Domain Window Rejection Algorithm
Bibliographic record
Abstract
Assessing seismic vulnerability is crucial for disaster preparedness and urban planning, especially in regions with complex geological conditions like the Yogyakarta Basin.The devastating 2006 earthquake caused severe structural damage, numerous casualties, and significant economic losses, highlighting the need for continuous seismic vulnerability assessment.This study assessed seismic vulnerability by determining the site's resonant frequency, the horizontal-to-vertical spectral ratio (HVSR) peak amplitude, and the ground vulnerability index across the Yogyakarta Basin.Seismic microtremors were recorded at 707 locations within the Yogyakarta Basin.The analysis employed the lognormal distribution and automated frequency domain window rejection algorithms to process the microtremor data.These methods effectively eliminated noisy windows in high-variance datasets while minimizing the unnecessary exclusion of valid windows in low-variance datasets.Areas with Kg values exceeding 10 and low resonant frequencies were considered highly vulnerable because of the potential for ground motion amplification.Such regions include Sanden, Kretek, Pundong, Bambanglipuro, Jetis, Pleret, Sewon, Berbah, Prambanan, Banguntapan, Kalasan, and parts of Yogyakarta City, Depok, Gamping, and Kasihan.The results offer valuable insights for guiding land-use planning, prioritizing mitigation efforts, and informing seismic risk management.Moreover, the findings emphasize the need for integrated seismic hazard modeling to improve resilience and preparedness across the Yogyakarta Basin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".