Linear Array Double Difference Adjoint Ambient Noise Tomography of the Central Tanlu Fault Zone, Eastern China
Bibliographic record
Abstract
Abstract In response to the need for high‐resolution imaging of shallow crustal structure, we present a linear array double‐difference (DD) adjoint tomography method, using DD Rayleigh wave traveltime measurements for enhanced spatial resolution. This method, validated through synthetic experiments, improves velocity anomaly detection with fewer iterations compared to absolute traveltime measurements. Applied to data from four linear seismic arrays in the central Tanlu fault zone (TLFZ) in the eastern China, our approach integrated both DD and absolute difference (AD) of adjoint traveltime measurements. We performed cluster analysis for data quality control, reducing data outliers and increasing reliability, particularly in suppressing cycle skipping for short‐period measurements. The resulting high‐resolution S‐wave velocity profiles in the shallow crust well delineate geological structures, revealing a continuous low‐velocity anomaly beneath the eastern branch of TLFZ. Our comparative analysis with the southern segment of TLFZ further highlights the segmented nature of the fault zone structure. These variations might suggest a dominant influence of deep magmatic processes due to destruction of the North China Craton. Our study links shallow structural features to deeper geodynamic activities, emphasizing the role of TLFZ as a critical tectonic boundary.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".