Visualizing highly multidimensional time varying Microseismic Events
Bibliographic record
Abstract
Making decisions about improving an oil and gas reservoir model based upon microseismic data is a difficult challenge for reservoir engineers and analysts. These difficulties arise because the available data contains inaccuracies, has high-dimensionality and has a high degree of uncertainty. Currently these difficulties are intensified by the lack of computational tools to support interactive visual interpretation and integration of geophysical data leading to robust structural models of the reservoir and its parameters. To address these difficulties domain experts are demanding better and more detailed visualization tools to help them as they explore their data. In this paper, we present a tool that contains a set of interactive visualizations that combines, merges and extends existing visualization techniques. We describe the iterative design process we undertook to develop the tool, relying on insight from domain specialists. Our tool supports 3D spatial analysis and exploration of the data with a set of manipulations designed to provide domain experts with insights into their highly complex microseismic data. Our microseismic visual-analysis tool also provides an extended parallel coordinates implementation to: (1) support interactive filtering and selection through combined filter and shadow boxes that can remove the uninteresting events from further analysis, (2) correlate between the data attributes by axes reordering and outlier discovery, and (3) visually correlate the data events rendering through additional visual elements such as color maps. Our multiple coordinated views link the insights gained from one view with other views instantaneously. We conclude with a discussion of the feedback provided to us by the domain experts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".