Autonomous Bathymetric and Magnetic Surveying for Canadian Lakes
Bibliographic record
Abstract
Magnetic surveys are an important step in the process of mineral and archaeological exploration. Current magnetic surveying methods include mounting magnetometers to various survey platforms including inhabited airplanes, helicopters, all-terrain vehicles, boats, and backpacks for walking surveys. Recent developments in autonomous controls and remotely piloted systems have led to conducting magnetic surveys from remotely piloted aerial systems (RPAS). The use of these recently developed platforms has allowed the mineral exploration industry to reduce risk to pilots and operators of inhabited survey platforms and achieve reduced sensor to magnetic source separations while improving data resolution, or speed of data collection compared to previous survey vessels. In survey regions occupied by freshwater lakes (10% of Canada’s surface area), these aerial survey platforms are restricted by the water surface, limiting the achievable proximity to the ground/magnetic source body. The work completed in this thesis has led to the configuration of an autonomous surface vessel (ASV) equipped with a single frequency echo sounder and an Overhauser magnetometer for applications in marine magnetic and bathymetric surveying. This platform and developed operating procedure are capable of deploying a magnetometer only metres above the lakebed thus decreasing the sensor to magnetic source separation and improving magnetic body resolvability compared to pre-existing public magnetic data acquired across Canadian lakes. The survey vessel configuration was tested in three Canadian lakes (Lake Ontario, Surprise Lake – Thunder Bay, Ontario, and Opinicon Lake – South Frontenac, Ontario) by completing magnetic and bathymetric surveys for shallow sections of each lake. The surface area of each survey was less than 1 square kilometre, with a maximum lake depth of 6.5 metres. Through interpretation of acquired data, localized magnetic anomalies on the order of 200nT have been identified and are suspected to indicate the presence of iron-rich geological units, and/or archaeological waste. The data acquired throughout these case studies, combined with a regional investigation of over 877,000 Canadian lakes completed throughout this research, has indicated that the configured ASV and operation procedure could serve as an effective and reliable survey platform for purposes of mineral and archaeological exploration in over 68% of Canadian lakes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".