Investigation of Hazardous Waste Sites and their Environment Using the BGR Helicopter-Borne Geophysical System
Bibliographic record
Abstract
The Federal Institute for Geosciences and Natural Resources of Germany (BGR) completed a research and development project aiming at optimizing its helicopter-borne geophysical system for high resolution site characterization. The overall objective was to adapt the existing helicopter-borne geophysical system used for groundwater and mineral exploration to survey conditions where the anomalies to be recorded are much smaller. The BGR helicopter-borne system permits simultaneous electromagnetic (AEM), magnetic (AMAG), and gamma-ray surveying. At the suggestion of the BGR, the AEM system manufactured by Geoterrex-Dighem, Toronto, Canada, was improved compared with the Dighem III system formerly used. The new system operates at five frequencies and the transmitter and receiver dipole moments are increased up to 25%. In addition, the system is now calibrated during flight. The sensitivity to waste objects was augmented by reducing the sensor heights from more than 30m (AEM) and 45m (AMAG), respectively, to less than 20m about ground level by means of installing a magnetic sensor and a laser altimeter in the AEM bird. Enhanced spatial resolution was achieved by decreasing the sampling distance along line from about 10mto3m and by reducing the line separation from about a hundred meters to less than 50m due to better navigational and positioning instruments. The modified system was tested over two former military training areas south of Berlin, Germany. Special surveys to locate steel drums, scrap metal, steel pipes, petrol tanks, ordnance, buried at depths from 0.3to1.5m were carried out with nominal bird heights of 20m and flight-line spacings of 50m. Due to the extremely weak AEM and magnetic anomalies produced by these materials, suitable detection algorithms were developed to recognize and to identify these weak anomalies. They were tested using an airborne data set collected over an area where thousands of anomalies had been found. Many of them were subsequently verified on the ground. More than 90% of the anomalies selected for verification could be confirmed either by visual inspection of the ground surface or ground geophysical surveying or excavation. The modified AEM system not only allows better detection of waste but also better investigation of the environment. The AEM data could be reliably inverted to resistivity vs. depth sections using multi-layer inversion procedures. These resistivity data provide information about the hydrogeology and lithology, e.g., the depth of the groundwater table or the distribution of clay and silt. Thus, AEM can be successfully used for the hydrogeological and geological mapping of the near surface.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".