New improvement in interpretation of gravity gradient tensor data using eigenvalues and invariants: An application to Blatchford Lake, Northern Canada
Bibliographic record
Abstract
Recently, interpretation of causative sources using components of the gravity gradient tensor (GGT) has had a rapid progress. Assuming N as the structural index, components of the gravity vector and gravity gradient tensor have a homogeneity degree of -N and - (N+1), respectively. In this paper, it is shown that the eigenvalues, the first and the second rotational invariants of the GGT (I1 and I2) are homogeneous with the homogeneity degree of - (N+1), -2(N+1) and -3(N+1), respectively. Furthermore, the product of M homogeneous functions with a homogeneity degree of - (N+1) itself is homogeneous with the degree of –M(N+1), and their summation do not change the homogeneity degree. Therefore, the Euler deconvolution of these functions can be used to estimate the location and type of the source, simultaneously. The advantage of using Euler deconvolution of invariants compared to other methods that use invariants is that the only parameters involved in location approximation are invariants and their derivatives. Therefore, it is completely independent of the orientation of the coordinate system as well as having little sensitivity to random noise. In this study, the model is tested on synthetic models with and without noise. Finally, application of the method has been demonstrated on measured gravity gradient tensor data set from the Blatchford Lake area, Southeast of Yellowknife, Northern Canada.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".