Comparative Evaluation of 1D, 2.5D and 3D Inversions for Resolving Tectonic Elements in Floodplains and Near-Surface Inverted Sedimentary Basins
Bibliographic record
Abstract
This study reports the results of a comparative evaluation of 1D, 2.5D and 3D AEM inversions for resolving hydrostratigraphy and structural elements in two contrasting settings: unconsolidated Quaternary floodplain sediments affected by Neogene deformation; and a tectonically inverted Palaeozoic sedimentary basin.Previous studies have demonstrated the importance of airborne electromagnetic (AEM) data optimization to ensure that key elements of the hydrogeological system, including geological faults, are appropriately represented in inversion models. In the inverted sedimentary basin study, 1D inversions of AEM data indicated greater structural complexity than previously known. Initially, a suite of equivalent 1D inversion models produced very similar inversion model results. However, 2.5D inversions produced a disparity in solutions in key locations. To resolve these differences, 3D AEM inversion methods have been trialled. In the second study (floodplain setting), 3D inversions have helped resolve the geometry of hydrostratigraphic units and tectonic elements (folds and faults). In both study areas, independent validation of inversion results has involved an inter-disciplinary approach incorporating a range of borehole and ground geophysics techniques (e.g. passive seismic and Ground Magnetic Resonance (GMR)), tectonic mapping and analysis, hydrochemistry and drilling.In summary, comparative evaluation of 1D, 2.5D, and 3D AEM inversions in two contrasting settings demonstrates the importance of optimizing inversion procedures, taking into consideration all available geological, hydrogeological and tectonic data. The benefits of using 2.5D and/or 3D inversion procedures are particularly evident in areas of structural complexity. Confidence in 3D inversions is maximised when all elements of the system response are modelled appropriately.
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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 | 0.000 |
| 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) | 0.000 | 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".