2-D reconstruction of boundaries with level set inversion of traveltimes
Bibliographic record
Abstract
There are many features in the Earth's crust that involve a jump in physical properties across a sharp boundary. One example is the boundary of an ore body embedded in host rocks. Such well-defined boundaries are often of interest to geophysicists, however traditional minimum-structure inversion methods tend to produce blurred images of the subsurface, where sharp boundaries are not well defined. In this paper, we explore the application of a level set inversion method to recovering a sharp boundary between two slowness values, one characterizing an inclusion, for example, an ore body, the other characterizing a background, for example, host rocks, from first arrival traveltime data. The slowness values are assumed to be known, for example, from sonic logs. We consider the scenario of cross-borehole tomography in two dimensions, however the method is extendible to the 3-D tomography. We test the method on a series of synthetic examples including both fast and slow inclusions. We also investigate numerically the use of straight ray and bent ray forward modelling in the inversion for media with different velocity contrasts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".