Discriminating Stratigraphic and Acquisition Discontinuities From Natural Fractures Using Multi-Attributes Neural Networks
Bibliographic record
Abstract
Application of Neural Networks analyses has recently attracted more attention among geoscientists trying to apply mathematical knowledge to gain more geological information from seismic data. Neural networks are one of the most efficient ways to recombine multiple input attributes and achieve a high quality extraction of a target feature or rock property from seismic data. This paper presents a new application of neural network method to combine curvature attributes, mainly most positive curvature, with different step-outs for discontinuity analysis using examples of North Sea 3D dataset (F3 block), Western Canada Sedimentary Basin, and Kandkhot (Sui Main Limestone) block, Pakistan. Curvature attribute analysis is often used to separate structure and/or stratigraphic discontinuities such as faults below seismic resolution, shear zones, channel edges and strand-plains. Classification process also allows the suppression of acquisition artifacts of the seismic data. In the curvature analysis, a critical aspect is the spatial extent of each curvature calculation. A smaller spatial extent will reveal features while the larger spatial calculations will reveal a different set of geological details. Usually, the curvature attribute highlights the acquisition footprints while the other lineaments are obscured by these curvatures. The interest of the interpreter is to have all these curvature information classified so they could be separated to investigate each type of lineament. The Most Positive Curvature attribute provides the maximum correlation to the different geological features of available 3D dataset. We generated Most Positive Curvature attributes using step-outs of 5x5, 7x7, 9x9, 11x11 and 15x15 samples in the XY domain. The afore mentioned five volumes were then classified together using un-supervised neural network. The process starts with choosing 5000 random samples between two horizons covering a slice of about 100 ms thick. These 5000 samples represent a set of various curvature values from all five volumes. The neural network approach is given 10 classes. This volume can also be known as a hybrid volume and it generates a classified output. The classified volume or Most Positive Classfied Curvature Cube (MPC3) can differentiate lineaments from the background such as acquisition footprints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".