Mapping Mcfaulds Lake ‘Ring of Fire’ crystalline basement architecture in Ontario, Canada from airborne gravity gradiometry (AGG) data using steerable filter and normalized-cut image segmentation
Bibliographic record
Abstract
Two of the most important tasks of interpreting gravity or magnetic data over sedimentary basins are, (1) to extract lineaments which often reflect concealed structural elements such as faults, fractures and lithological contacts, and (2) to partition the crystalline basement into segmented blocks or domains that may reflect possible litho-tectonic terrane boundaries. These two tasks play a major role in oil and gas exploration because they provide pathway for their migration, accumulation and they may also enhance reservoir permeability effectiveness. Occasionally, these two tasks are accomplished by generating a suite of filtered map enhancements that are visually inspected by experienced interpreter to pick lineaments. In some cases an attempt is made to partition the crystalline basement into segmented blocks based on their gravity, magnetic or seismic signatures complemented by other geological information such as well logs. However, the process of picking lineaments and partitioning the basement into different blocks using conventional approach is tedious and time consuming, and to some extent subjective. Therefore, in this study we developed a new approach to automate and accelerate the process. This new approach is much faster, cost effective and is less subjective than the conventional approach. Furthermore, it is more suitable to process a large volume of data such as those acquired by marine and airborne geophysical surveys. Two powerful image processing techniques are used in this new approach; steerable filter and normalized-cut segmentation filter. Steerable filter is a rotated filter that is able to pick geological features at any orientation or angle defined by the geophysical interpreter. Thus, steerable filter is able to efficiently extract linear, curvilinear and circular geological edges and ridges, a feature that is difficult to accomplish by using the conventional approach. The normalized-cut segmentation filter which is based on graph theory is used to partition or classify the crystalline basement into different segmented blocks. However, in this study the segmentation filter is used as a test to evaluate its effectiveness in delineating various density blocks using the Bouguer gravity anomaly (gD) for this purpose. The segmentation filter is using the gravity intensity, color and distance between pixels to partition the basement. We applied these two techniques to the Bouguer gravity anomaly (gD) derived from a publicly available Falcon airborne gravity gradiometry (AGG) survey that was flown over part of the McFaulds Lake ‘Ring of Fire’ area, Ontario, Canada. The results reveal mapping various lineaments related to edges and ridges over the study area and considerable number of them are coinciding with already mapped structural elements. In addition, we were also able to partition the crystalline basement into segmented basement blocks. The preliminary results are interesting and it may be used as an initial approach to mark litho-tectonic terranes of the study area. Presentation Date: Tuesday, October 18, 2016 Start Time: 10:20:00 AM Location: 161 Presentation Type: ORAL
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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) | 0.004 | 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 teacher head, 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".