Improving resolution model of probable asperity distribution along southern Sumatra Subduction Zone using denser GPS stations
Bibliographic record
Abstract
The southern Sumatra Subduction Zone (SSZ) is point outed as an area with seismic potential due to plate tectonic movement. To observe seismic activity in this region, Global Positioning System (GPS) networks can be used to monitor tectonic plate movement. We aim to determine the best resolution model of probable asperity zone in the southern SSZ by testing various slip patch models and using the GPS networks (SuGAr, InaCORS, SuMO, and UNIB networks) installed as synthetic data. This study provides a necessary resolution analysis and a validated methodology, providing a reliable foundation for future studies of interplate coupling models, interseismic deformation, and seismic potential. The testing of resolution model was conducted using a Checkerboard Resolution Test (CRT) through Akaike Bayesian Information Criterion (ABIC) inversion and a bicubic b-spline basis function to parametrize the slip velocity. The results show that utilizing a denser GPS station network by adding the SuMo and UNIB networks (47 stations) can improve resolution model from 40-70% to 80-100% in southern Sumatra near land areas. This enhancement confirms that the denser station distribution is crucial for resolving fine-scale slip heterogeneity. Consequently, the Bengkulu-Enggano segment can now be modeled with high fidelity, providing a reliable foundation for future studies of interplate coupling models, interseismic deformation, and seismic potential. • Denser GPS networks boost resolution from 40-70% to 80-100%. • Network density is key to resolving fine-scale fault asperities. • Land-based data has a critical blind spot offshore near the trench. • Seafloor geodetic stations crucial for resolving offshore seismic gaps. • Optimal 16-km spline interval ensures stable asperity modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".