Deep learning applied to induced-seismicity location in the Groningen gas field in the Netherlands
Bibliographic record
Abstract
Induced seismicity occurs in many oil/gas fields all over the world, including the USA, Canada, China, Europe and the Middle East. Many countries have deployed dense seismic networks to monitor induced seismicity and to obtain information about the seismic source physics, reservoir structure and ground motions. For example, in one gas field in Europe, more than 350 3-component seismic sensors have been installed to monitor induced seismicity continuously. With this tremendous increase of seismic data, the traditional analysis method is impractical and costly. At the same time, the problem is an ideal candidate for the application of AI based analysis. The goal of this work is to use deep learning to solve the problems of earthquake location using a complex 3-D velocity model in the Groningen field. The algorithm is being tested using synthetic data. Presentation Date: Wednesday, October 17, 2018 Start Time: 9:20:00 AM Location: Poster Station 15 Presentation Type: Poster
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".