Numerical Simulation and Anomalies Qualification Based on Ground-well Transient Electromagnetics Method
Bibliographic record
Abstract
The ground-well transient electromagnetics (G-W TEM) method is an important way to enhance the resolution and depth of detection, for its signals, emitted on the ground and received in the well or tunnel, are close to anomalies.This paper attempts to detect deep, small anomalies accurately through the G-W TEM.Drawing on the 2D finite-difference timedomain (FDTD) method and the ground TEM method, several field potential models were created for plate-shape conductors and square conductors at different depths and locations.The model construction was conducted with a large fixed loop as the emitter, assuming that the medium obeys uniform distribution.The signals were observed under the ground-borehole or ground-tunnel modes, and subjected to forward modelling, with the aim to disclose the response characteristics of small anomalies to the vertical magnetic field intensity (Hz) curve and multi-track electromagnetic absorption (EA) curves.On this basis, the author put forward an interpretation method that accurately locate small anomalies based on the extreme point of the Hz curve and the intersections of the EA curves.The research results show that the FDTD method can solve the established field potential models of linear conductors in an effective and accurate manner, and output the curve response characteristics of small anomalies; under the ground-borehole mode, the longitudinal position of each anomaly can be identified, and different anomalies can be distinguished based on the extreme point of the Hz curve, and the amplitude of the TEM response curve decreases with the elapse of time; under the groundtunnel mode, the lateral position of each anomaly can be identified, and different anomalies can be distinguished based on the intersections of EA curves, and the EA value is negatively correlated with the distance to the anomaly.To sum up, the proposed method pinpoints small anomalies based on the extreme point of the Hz curve and the intersections of the EA curves, and improves the resolution in both vertical and horizontal directions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".