Integrating advanced frequency-domain signal processing with machine learning for accurate leak detection in subsurface CO2 storage
Bibliographic record
Abstract
Ensuring the integrity of geological CO 2 storage is critical for the long-term success of carbon capture and storage (CCS) technologies. The detection and localisation of potential leakage events rapidly and accurately remains a key challenge, particularly under constraints of limited monitoring data. This study presents a proof-of-concept framework that integrates advanced frequency-domain signal processing with machine learning to address this challenge using only pressure data from a monitoring well in a CO 2 storage site. Here, pressure signals are translated into the frequency domain using Fast Fourier Transform (FFT) in order to extract physically meaningful features that are highly sensitive to leakage phenomena. These features capture subtle variations in signal behaviour that are often missed in time-domain analysis. A two-stage machine learning pipeline is also developed, involving a classification stage to distinguish leak versus no-leak conditions, followed by leak localisation in a regression stage. The results showed that in the leak detection stage, ensemble and probabilistic classifiers, particularly Naive Bayes (test accuracy = 0.9873, F1 = 0.9788) and Random Forest (test accuracy = 0.9823, F1 = 0.9016), outperformed linear models by a substantial margin. In the localisation stage, the K-Nearest Neighbours Regressor (test R 2 = 0.9899, MAE ≈ 6.8 m) and Gradient Boosting Regressor (test R 2 = 0.9790, MAE ≈ 9.5 m) achieved the highest spatial prediction accuracy. Additionally, the findings demonstrate that frequency-domain feature engineering substantially enhances leak-detection sensitivity and spatial inference accuracy compared to time-domain methods. The proposed framework is computationally efficient, requiring only sparse pressure data, and can be integrated into real-time monitoring systems. • Novel framework integrates frequency-domain signal processing (FFT) with machine learning for CO 2 leak detection. • Achieves accurate leak detection and localisation using only sparse pressure data from a single monitoring well. • Fast Fourier Transform (FFT) extracts features sensitive to leakage phenomena. • A two-stage ML pipeline classifies leaks and estimates their location. • Ensemble models show superior detection and localisation performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".