Three-Dimensional Inversion of Long-Offset Transient Electromagnetic Method over Topography
Bibliographic record
Abstract
The long-offset transient electromagnetic method (LOTEM) is widely employed in geophysical exploration, including environmental investigation, mineral exploration, and geothermal resource exploration. However, most interpretations of LOTEM data assume a flat Earth, and the commonly used one-dimensional (1D) interpretation encounters significant challenges in achieving reliable geological interpretations when topography is ignored. To address these challenges, this study presents an effective three-dimensional (3D) LOTEM inversion method. In this study, we discretize the simulation domain using unstructured tetrahedra to accurately simulate complex geological structures. The finite-element time-domain (FETD) method is utilized to calculate the LOTEM forward responses, and the limited-memory BFGS (L-BFGS) optimization method is employed for 3D LOTEM inversion. To avoid explicit calculation of sensitivity, we obtain the product of the transposed sensitivity matrix and the vector through adjoint forward modeling. Several synthetic models are used to verify the developed program, and the influence of topography on LOTEM inversion is examined. The numerical results demonstrate that topography can significantly impact the inversion result, potentially leading to incorrect geological interpretations. Finally, the developed inversion algorithm is applied to a realistic ore model from Voisey’s Bay, Labrador, Canada. The 3D inversion successfully reconstructs the spatial distribution of the ore body, further confirming the effectiveness of the developed algorithm.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".