Trial-and-error modeling of ground-airborne electromagnetic data in the Yishu Faulting Basin, China
Bibliographic record
Abstract
ABSTRACT Although trial-and-error modeling may give some level of interpretation about the subsurface while sacrificing certainty, it is a viable alternative for precise 3D interpretation of real ground-airborne frequency-domain electromagnetic (GAFEM) data. In this sense, a semiautomatic trial-and-error modeling approach is developed. Specifically, we first develop the 3D GAFEM forward-modeling code. Its accuracy is demonstrated using a 3D synthetic model with topography and a tilted anomalous body. Second, an initial model is established based on known geologic constraints. Then, the code is conducted repeatedly, and the parameters of the model are renewed semiautomatically based on a predefined geometry-resistivity combination list. Finally, the model that can achieve the minimum error between the computed response and the collected GAFEM data is selected as the final model. Furthermore, we apply the presented semiautomatic trial-and-error modeling approach to the geothermal resources survey at the Yishu Faulting Basin, China. The purpose of the survey is to interpret the resistivity structure of the subsurface and evaluate the potential development of the geothermal resources in the survey area. As a result, the final model obtained by the trial-and-error modeling, which is constrained by the known geologic information and subsurface geoelectric structures inferred from 2D models inverted by the magnetotelluric and controlled-source audio-frequency magnetotelluric data measured at the same location, indicates the existence of the geothermal resources. This indication is proven by the drilling result of a well site located on the survey line. To further verify the reliability, a comparative analysis is conducted between the model obtained by the trial-and-error modeling and the models obtained by 3D inversion of a GAFEM data set and apparent resistivity calculation using the same data. The results indicate that different approaches can achieve similar subsurface geometry and resistivity distribution of the faulting basin structure.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".