Mass anomaly visualisation and depth estimation from full tensor gradient gravity data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Full Tensor Gradient (FTG) gravity data measures the derivatives of the Earth’s gravitational field. Such variations in the gravitational field may be due to the presence of bodies of higher or lower density relative to the surrounding rock.As the gravity tensor contains 5 independent components, effective visualisation of this high-dimensional dataset is advantageous for efficient processing of the FTG data. We present two aspects of visualising mass anomalies in FTG gravity datasets. First, we create a textured image where the orientations of the resulting texture reflect local lateral orientations encoded in the FTG data. It uses a colour map to highlight geologically significant structures such as linear features and radially symmetric points by identifying different geological features and using colour components to represent different feature types. This visualisation method is shown to be robust to significant levels of modelled noise, and we demonstrate its applicability to a field FTG survey.Second, we present an algorithm for estimating the depths of mass anomalies in FTG data. A voxel representation of the subsurface is created and voxels are voted for according to gravitational curvature properties encoded in the FTG tensor. A visualisation of the volume at successive depths highlights 3D locations of mass anomalies at local maxima of the volume. The algorithm is evaluated on a forward-modelled FTG dataset where the depths of mass anomalies are known. The depths of mass anomalies are shown to be accurately located in the presence of noise.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it