Bibliographic record
Abstract
SummaryGeophysical data processing is a highly quantitative field that involves modelling, inversion and visualization. In most cases a geophysical experiment is conducted to collect data that are sensitive to a particular physical property of the earth. The data is processed and inverted to generate an earth model of the physical property in question. To better understand the structure of the earth, different experiments are conducted using a variety of imaging modalities. For example, from seismic, gravity and electromagnetic experiments we may obtain information about the earth's elastic, density and conductivity characteristics. Usually the data of each experiment are inverted separately to generate an ensemble of earth models. However, since the inversion process of each geophysical modality is typically carried out independently, most inversion algorithms do not utilize the information obtained through other modalities.In this research we propose to jointly invert the data obtained by two physical experiments since the information contained in each model can be used to correct the other model. In many of the cases the two models share the important structures, therefore, edges occur in the same locations. In order to exploit this information, we propose using a level set formulation of the problems. Assuming that both models take two known discrete values, we can then use a single level set function for both models together. This can be later extended to multi-level set functions and with unknown values. By using this formulation we are able to improve inversion results of both problems.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".