Performance comparison of compact phased arrays and traditional seismic networks for microseismic monitoring at a CO2 sequestration test site
Bibliographic record
Abstract
Abstract As carbon capture, utilization, and sequestration scales toward the gigatonnes level, the need for underground reservoir surveillance is driving efforts in advancing technologies for cost-effective passive seismic monitoring. Quantum Technology Sciences, in cooperation with Carbon Management Canada's Containment and Monitoring Institute (CaMI), installed a network of four permanent compact volumetric phased arrays (seismic and acoustic detection and ranging [SADAR] system) at CaMI's Field Research Station (FRS) to demonstrate the results that can be achieved through passive monitoring of microseismicity using this technology. Configured as a sparse network, the SADAR arrays provide passive, persistent, and permanent data acquisition and analysis for monitoring microseismicity in the earth volume of interest. Data from the phased arrays are processed to take advantage of the spatial coherence of the incident seismic signals to increase signal resolution while suppressing noise and clutter signals and providing signal attributes such as angle of incidence and phase velocity. The CaMI FRS has a network of 28 permanent surface stations that are deployed in an x-shaped geometry centered on the injection well. It has a downhole array of 24 geophones that are permanently deployed in an observation well. This provides a ready and unique opportunity to evaluate the detection and location performance of the different systems for passive seismic monitoring. We analyze observations of five example events selected from the microseismicity detected by the SADAR arrays with moment magnitudes (Mw) down to approximately −2. Signal-to-noise ratio (S/N) and location uncertainties are compared for the events acquired using SADAR arrays versus the surface sensors. The results demonstrate improved performance of networked SADAR arrays compared to traditional surface sensor deployment for detecting and locating microseismicity. Specifically, the results show that coherent processing of SADAR arrays achieves S/N gains up to about 20 dB and location errors down to 10 m.
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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) | 0.000 | 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".