Numerical Simulation by MRS Vertical Coil Response for Tunnel Water Detection
Bibliographic record
Abstract
Surface Magnetic Resonance Sounding( MRS) has become increasingly popular as a new geophysical technique to detect the groundwater directly. It will be actual significance to use this method in the detection of groundwater ahead of mining or tunneling face. Since the loop size is severely restricted in underground strait condition, it was brought forward that the multi-circle vertical coil can be used to detect the presence of water in whole space. The equations of excited field numerical and the MRS sounding formulation of vertical coil were carried out for whole space. According to changing loop size,circle number,the coil's placing angle and the geomagnetic inclination,the result of the MRS sounding for one model were simulated,and correctness of the results of numerical simulation was verified by field experiment. We gained the conclusions: the larger of the loop size or circle number is,the larger of the maximum of the initial amplitude of MRS sounding is,coil for horizontal or vertical rotation has little effect on the initial amplitude of MRS sounding,but the excitation pulse moments need be adjusted to the rotating angle for the max amplitude detection; the larger of resistivity is in stratum,the superior of detecting effect is; as the detecting depth increases,the maximum value of the initial signal amplitude decreases,and at the same time,the excitation pulse moments increase with the detecting depth increasing.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".