A novel imaging method for crosshole radio imaging (RIM) data: Complex permittivity inversion
Bibliographic record
Abstract
The radio imaging method (RIM) is a cross-hole imaging method that uses radio-frequency electromagnetic waves to delineate the electric properties in the borehole plane. In this paper, we describe the two-dimensional (2-D) complex permittivity inversion method of interpreting RIM data and use synthetic data to test its efficacy. The method is based on forward modeling using the moment method. The total electric field is divided to the incident field and the secondary fields, where the latter is assumed to be the integral of radiated fields from the coupling currents in the model. A set of equations for the total fields are built in the model domain and solved to calculate the electric fields on the model, and then the fields at the receiver. The solution process involves iteratively updating the model using a linear relation with the data misfit. This algorithm was tested with 3-D synthetic data generated using the finite-element modeling tool Comsol Multiphysics and compared with the straight-ray method commonly used in mining exploration. Two sets of experiments were carried out: 1) a rectangular prism model, and 2) models of different lengths extending from one borehole. The results show that this method provides much better images than those obtained using the straight-ray method, with more coherent results for different frequencies and the shapes of the anomalies are more accurately shown. Presentation Date: Tuesday, October 18, 2016 Start Time: 10:20:00 AM Location: 168 Presentation Type: ORAL
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".