Detection of mining-induced microseismicity through a deep convolutional neural network
Notice bibliographique
Résumé
The underground extraction of mineral resources is often closely linked to induced microseismic events. The use of a seismic network to continuously monitor mining-induced seismicity to reduce risks and improve operational safety is common. For this monitoring to be effective, a comprehensive catalog of microseismic events, containing low-to high-magnitude events, is essential to evaluate the response of the rock mass to mining activities. However, detecting low-magnitude events based on manual picking or automated conventional approaches has been challenging in mining environments owing to the inherent noise level. Recent advancements in deep learning and data-driven methods, particularly Convolutional Neural Networks (CNNs) trained on extensive seismic datasets, have shown improved capabilities in automated event detection and arrival phase picking on seismic data recorded by regional seismic networks. In this study, we assessed the performance of PhaseNet, a deep learning arrival-time picking method, in detecting the P- and S-wave arrivals of mining-induced microseismic events at different noise levels. As access to high-quality, labeled microseismic datasets for such mining applications is rare, a realistic three-component synthetic dataset was generated using full-waveform modeling. This simulation accounted for the geological conditions and network geometry specific to a mine in Ontario, Canada. The mine, which integrates copper and nickel operations, experiences considerable mining-induced earthquakes annually, posing risks to miners and infrastructure. The simulation includes a variety of source mechanisms with different magnitudes and offers more than 270,000 labeled seismograms. The results from the PhaseNet-trained model, which utilized the simulated dataset, demonstrated its effectiveness in managing noisy waveforms. This capability allows the detection of low-magnitude events within the mine environment, which may be overlooked by traditional methods. Furthermore, the model shows high accuracy in picking both the P- and S-wave arrival times, achieving precision rates exceeding 0.9. Tests on real data were performed in three different scenarios. The first scenario involves training the model exclusively using real data. The second scenario combines synthetic and real data to retrain the model previously trained with synthetic data only. Finally, the third scenario focuses on retraining the pre-trained model using only synthetic data. All these trained models were used to evaluate the performance on the real test dataset. The results indicate that the model retrained with synthetic and real seismograms yielded the best arrival time predictions for the mine dataset.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».