Three-dimensional inversion of SQUID TEM data at Lalor Lake VMS deposit
Bibliographic record
Abstract
The combination of high fidelity and late time channels make SQUID magnetometers an attractive sensor for deep and highly conducting targets. Here we focus upon a data set acquired over the Lalor Lake VMS deposit. The deposit consists of a zinc zone between 700m and 1000m depth and a deeper gold/copper zone. The SQUID data set had previously been interpreted using the plate modeling to yield two conductors respectively corresponding to the zinc zone and the gold-copper zone. In this paper we invert these data with our 3D TEM inversion algorithm to produce a 3D voxel conductivity volume. Our 3D model recovers a shallow dipping conductor, which coincides well with the plate model of the zinc zone, as well as a large conductor below 1000m depth. Our inversion is done without incorporating prior knowledge and there is always the potential that large features at depth can be an artifact of the inversion algorithm. To investigate this, and to have confidence, or not, in the existence of the deep body, we carry out a hypothesis analysis where we attempt to find a model that does not have a high conductivity at depth. Forward modeling and subsequent inversion confirms that there must be another conductor below the zinc zone. Much of the concentration of conductivity lies near the region indicated by the initial blind inversion, but the amplitudes and distribution are different. Nevertheless, despite lack of knowledge about geometric details, there is some highly conductive material at depth and it would warrant a drill hole. In a final analysis we look at the relative merits of using B or dB/dt for the particular geometry of this survey. We generate synthetic B and dB/dt data based on our inversion model of Lalor Lake deposit. While the B-field data inversion recovers the correct locations and geometries of the two compact conductors, the dB/dt inversion shows the shallow conductor in a distorted geometry and the deep conductor as a blurred conductive region with conductivity much smaller than the true model. This demonstrates that B-field data can be are superior to dB/dt data for this survey in which we have surface transmitters and receivers.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".