2D cross-hole electromagnetic inversion algorithms based on regularization algorithms
Bibliographic record
Abstract
Abstract The cross-hole electromagnetic (EM) method, which is currently at the forefront of electric logging technology, fundamentally solves the problems of the lateral imaging ability of single-well logging and the lack of detection of inter-well physical properties. However, due to the complexity of underground reservoir distribution and the non-uniqueness problem of geophysical inversion, there remains a lack of practical and effective cross-hole electromagnetic inversion methods. Our goal is to develop an efficient method to reduce the non-uniqueness of the physical property model recovered in the inversion. It is worth noting that the regularization algorithm, as a means to approximately solve inversion problems, can obtain different solutions by changing the form of the regularization function, so as to ensure the stability of inversion results and conform to the smooth or non-smooth characteristics in known geology or geophysics. We adjust the features of the final inversion model in a defined framework by changing the values of the $\alpha $ coefficient in the regularization and using the Lawson norm as a ${l}_p$-norm approximation form for $p \in [ {0,2} ]$. At the same time, the iteratively reweighted least-squares method is used to solve the optimization problem, and the gradient in the Gauss–Newton solution is adjusted successively to ensure that every term in the regularization contributes to the final solution. Compared with the traditional ${l}_2$-norm inversion method, the sparse inversion method can make more effective use of information regarding known physical properties and obtain better inversion results. Then, the effectiveness of our inversion method is verified by model tests and inversion of measured data in a mining area.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".