In the Shadow of Large Gravity Anomalies - Recovering Useful Gravity Data for Geologic Exploration
Bibliographic record
Abstract
The Nash Creek area in Northern New Brunswick, Canada, is home to a blind Zinc Lead Silver sulphide deposit hosted within a complex Ordovician-Devonian rift system. Extensive exploration of the area over several decades has resulted in a varied set of data at multiple scales, including – but not limited to – magnetic, radiometric, and high resolution LIDAR DEM data at the regional scale and TEM, resistivity, and high resolution terrestrial gravity data at the local scale. Additionally, approximately 400 boreholes have been drilled and logged over and around the known extents of the mineral deposit. This has also resulted in a comprehensive petrophysical database, including the densities of different rock units. Despite this wealth of data, many questions about the geology of the area remain unresolved, and the deposit is poorly constrained. This is in large part due to a thick layer of overburden blanketing the region, rendering existing geological maps ambiguous. Moreover, the gravity data has been difficult to incorporate as it shows a steep gradient over the region. In order to best characterize this gradient, large scale regional gravity data must be analysed. Unfortunately, geodetic control stations (GCSs) are sparse within the region. Thus, we turn to a satellite derived global gravity model (GGM) to get a picture of the regional gravitational field. Using the GGM, we see that a large positive gravity anomaly of approximately 20 mGal lies directly to the west of the study area, and a similarly sized negative anomaly lies just to the east. Over the study area itself, the regional field changes at a rate of roughly 1.62 mGal/km. The primary goal of this study is to investigate the optimal method to remove this gravity gradient from our high resolution dataset. We characterize the regional field by combining the GCS data with the GGM, and then use this regional field approximation to isolate the residual field in our high-resolution gravity dataset. We then compare the results to those attained using other methods, such as the upward continuation method of regional field approximation. Another goal of this study is to study the source(s) of this distinctive gravitational feature. We do this by using mass excess/deficiency calculations, and by incorporating regional magnetic, radiometric, and geologic data into our interpretation. We also compare this feature to other similar large scale gravity anomalies. Studying the cause of the gravity anomaly at Nash Creek can help to achieve a better understanding of the regional geologic history, and potentially help to identify the most promising approaches for geophysical exploration in this complex geological setting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".