Joint transmission and reflection traveltime tomography using the fast sweeping method and the adjoint-state technique
Bibliographic record
Abstract
We present a joint transmission and reflection traveltime tomography algorithm based on the Fast Sweeping Method and the adjoint-state technique. In contrast to classical ray based tomography, this algorithm utilizes a grid-based Eikonal equation solver to circumvent the non-linearity of conventional ray shooting and bending approaches in complex media. The adjoint-state technique is used to obtain the gradient of the objective function without the explicit estimation of the Fréchet derivative matrix, which is usually computationally prohibitive for large-scale problems. When combined with Huygens′s Principle, the tomographic inversion can simultaneously use direct and reflected arrivals to optimize a final velocity model, further mitigate the ambiguity of the inverse problem and reveal deeper structures not visible to transmission tomography alone. In this paper, we describe the theoretical basis of our algorithm, evaluate its performance on synthetic models, and then apply it to a 20 km long 2-D seismic survey acquired in the Mackenzie Delta, Northwest Territories of Canada. The subsurface at that location is characterized by a thick permafrost (600 m) comprising high- and low-velocity areas associated with thermokarst lakes. Our results show the potential of the joint tomography in characterizing multi-scale heterogeneous velocity structures within the permafrost.
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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 | 0.000 |
| 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".