Gravity Data Inversion by a Faulted 2-D Horizontal Block of Arbitrary Thickness With Application to Crustal Imaging
Bibliographic record
Abstract
An efficient inversion scheme has been developed for the interpretation of a residual gravity profile measured over a 2-D truncated fault. The scheme determines the characteristic parameters (depth to the top surface of the fault, depth to the bottom surface of the fault, amount and direction of dip of the fault plane, and density contrast) and solves for the inverse characteristic parameters of a model in the space of their logarithms instead of the space of the model parameters themselves. The accuracy and convergence of the scheme have been successfully verified and assessed on various noise-free numerical examples. It was then carefully assessed on noisy numerical data, and it was found stable but nonunique. The sensitivity analysis and the numerical inversions have shown that the parameter that can be determined with the greatest accuracy is the amount of dip of the fault plane; the parameter is crucial for determining the fault type. The validity of the technique for practical applications has been successfully illustrated in two field examples for crustal imaging. The inversion of the Garber structure, Garber County, OK, USA, has revealed more accurate and realistic results than previously published interpretations. The data inversion of the Saganash Lake fault, Canada, indicates that the fault plane dips to the Southeast and the fault is of a reverse nature; this finding agrees well with the conclusion established by Nitescu and Halls (2002). The scheme is shown to be applicable for shallow and deep Earth imaging, and has potential applications in integrated and reconnaissance studies.
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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 | 0.001 |
| 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".