Advances in aspects of the application of magnetotellurics for mineral exploration
Bibliographic record
Abstract
Summary world in the Earth-ionosphere waveguide. The physical T he high-frequency magnetotelluric method, audio-MT dramatically both diurnally and seasonally, and with solar (AMT), is currently being widely used for mining exploration, activity. The ionospheric layers are formed of particles that especially in Canada. However, there are st ill some aspects are electrically charged and that attenuate EM waves. The regarding its implementation that need to be considered. diurnal variation in attenuation is due the lower conductivity These range from signal detection and processing to response of the atmosphere at night because of the smaller aerosol function analysis to appreciation of three-dimensional effects. c ontent (no sun activity). This results in higher penetration of T he main natural electromagnetic source at the range of Sudbury (northern Ontario) and in northern Germany, we frequencies covered by mining scale MT, audio- frequencies have studied this diurnal variation of the magnetic field of 10 Hz - 20 kHz, is the global system of lightning. Due to amplitude at audio frequencies (Figure 1). The daytime and the physical characteristics of the Earth's ionosphere and nig httime amplitudes can vary by 2 to 3 orders of magnitude, atmosphere, there is a minimum in the electromagnetic which explains reports from several studies of an increase in spectrum around 1,000-3,000 Hz, which is exactly the signal-to-noise ratio during nighttime AMT acquisition. frequency range that is first sensitive to the presence of a typical conducting body. Some ore deposit exploration is being carried out in areas where there is existing mining activity, thus the data can be seriously affected by noise. The classical processing schemes are based on either the Fourier or the windowed Fourier transforms, and these methods do not readily separate noise from signal. The application of the wavelet transform offers an analysis of the time series at the frequency and time domains simultaneously. One of the main problems during the interpretation stage of MT data is the detection and removal of galvanic distortion effects caused by near-surface inhomogeneities. In mining exploration there is the additional problem that the targets are complicated 3D structures, and thus the classical 3D/2D decomposition schemes fail. For this reason a new 3D/3D algorithm has been designed. T arget bodies are usually complex in geometry and are strongly conductive, requiring full 3D interpretation of the data. Different structures can be inductively coupled adding a new difficulty to this geophysical method. We have undertaken analyses to check the validity of 2D interpretations over 3D regional structures. In this paper we describe our efforts in these four aspects of MT exploration. Secondly, we have verified a seasonal variation in amplitude. Source field structure
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".