3D forward modelling and inversion of inductive source resistivity data
Bibliographic record
Abstract
Time domain electromagnetic (TEM) surveys using inductive sources generally measure and interpret dB/dt data. However the electric fields (E-fields) also carry information about the conductivity and this has prompted renewed interest in collecting and interpreting them. Inductive source resistivity (ISR) surveys have recently been carried out at several exploration sites. Because much of the ISR signal arises from galvanic currents the ISR technique is applicable to geologic regimes exhibiting conductivity contrasts but not necessarily high conductivity. Thus even poorly conductive bodies at depth can sometimes be detectable. We investigate the ISR method through use of 3D forward modelling and inversion. This allows us, with synthetic models, to simulate the basic patterns of response, and evaluate the sensitivity of ISR data to different geologies and different transmitter and receiver configurations. We also invert ISR field data from Shea Creek, a uranium deposit in north Saskatchewan, Canada. Our study shows E-field TEM is a promising technique to map the 3D distribution of ground conductivity and can be an effective alternative to the dB/dt TEM survey in some resistive geologic settings.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".