Multi-observable Thermochemical Tomography: new advances and applications to the Superior and North Australian cratons
Bibliographic record
Abstract
Imaging the Earth’s thermochemical structure is crucial for understanding its dynamics and evolution. Moreover, the increased demand for critical minerals and geothermal energy driven by the energy transition has intensified the need for reliable subsurface models. Multi-Observable Thermochemical Tomography (MTT) is a simulation-based, probabilistic inversion platform designed to harness the combined sensitivities of multiple geophysical datasets and thermodynamic modelling. It produces internally-consistent estimates of the Earth’s interior as probability distributions, offering a powerful means for uncertainty quantification. Here, we present an updated MTT formalism and assess its benefits and limitations to image the thermochemical structure of the lithosphere-asthenosphere system. Individual and combined sensitivities of different observables to parameters of interest (e.g. temperature, composition, crustal architecture) are explored using challenging synthetic models. Our findings demonstrate that a judicious combination of observables can retrieve complex thermochemical structures relevant to greenfields exploration. We then apply MTT to study two cratonic regions of geological and economic significance. In the Superior Craton, we jointly invert receiver functions, gravity anomalies, gravity gradients, geoid anomalies, Rayleigh-wave dispersion curves, absolute elevation and surface heat flow. In the North Australian Craton, we incorporate new data from the Ausarray and add teleseismic P- and S-phase travel times to the datasets. The imaged lithospheric architectures provide new insights into the tectonic evolution of these two regions and the physical meaning of geophysical signatures. Additionally, these models offer unique proxies to guide exploration efforts for clean energy and critical minerals and serve as reference models for future high-resolution 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".