Joint denoising and classification network: Application to microseismic event detection in hydraulic fracturing distributed acoustic sensing monitoring
Bibliographic record
Abstract
ABSTRACT Deep learning has been applied to microseismic event detection over the past few years. However, it is still challenging to detect microseismic events from records with low signal-to-noise ratios (S/Ns). To achieve high accuracy of event detection in a low-S/N scenario, we have developed an end-to-end network that jointly performs denoising and classification tasks (JointNet) and applied it to fiber-optic distributed acoustic sensing (DAS) microseismic data. JointNet consists of 2D convolution layers that are suitable for extracting features (such as moveout and amplitude) of the dense DAS data. Moreover, JointNet uses a joint loss, rather than any intermediate loss, to simultaneously update the coupled denoising and classification modules. With the preceding advantages, JointNet is capable of simultaneously attenuating noise and preserving fine details of events and therefore improving the accuracy of event detection. We generate synthetic events and collect real background noise from a real hydraulic fracturing project and then expand them using data augmentation methods to yield sufficient training data sets. We train and validate the JointNet using training data sets of different S/Ns and compare it with the conventional classification networks visual geometry group (VGG) and deep VGG (DVGG). The results demonstrate the effectiveness of JointNet: it consistently outperforms the VGG and DVGG in all S/N scenarios and it has a superior capability to detect events, especially in a low-S/N scenario. Finally, we apply JointNet to detect microseismic events from the real DAS data acquired during hydraulic fracturing. JointNet successfully detects all manually detected events and has a better performance than VGG and DVGG.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".