Enterprise AI Analysis
Improving seismic signal classification of different ground activities with advanced AI and signal processing techniques
This study proposes a robust framework for classifying seismic signals from ground activities (pedestrian, bicycle, vehicle) using advanced signal decomposition (VMD, EMD, MPD) and Hilbert Transform-based feature extraction, followed by ensemble machine learning. The VMD-HT framework achieved superior performance with 91.4% accuracy and 0.89 macro-F1 score using a Random Forest classifier, demonstrating improved mode separation and feature stability compared to EMD-HT and MPD-HT.
Executive Impact: Transformative Benefits for Your Enterprise
Leverage advanced AI and signal processing to enhance security, optimize monitoring, and drive operational efficiency across your organization.
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Detection Accuracy
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Feature Interpretability
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Deep Analysis & Enterprise Applications
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91.4%
Peak Classification Accuracy with VMD-HT + Random Forest
The Variational Mode Decomposition (VMD) combined with Hilbert Transform (HT) features, classified by a Random Forest (RF) model, delivered the highest accuracy, outperforming other decomposition methods and classifiers.
| Pipeline | Test Accuracy | Macro-F1 (Test) |
|---|---|---|
| VMD-HT (proposed) | 91.4% | 0.890 |
| EMD-HT | 87.5% | 0.837 |
| MPD-HT | 84.2% | 0.808 |
Proposed Seismic Signal Classification Workflow
Data Acquisition
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Preprocessing
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Decomposition (VMD/EMD/MPD)
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Hilbert-based Feature Extraction
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Machine Learning Classification
Real-time Ground Intrusion Detection
Client: Perimeter Security Solutions Inc.
Challenge: Detecting human and vehicular intrusions in real-time with high accuracy under varying environmental conditions using seismic sensors.
Solution: Implemented the VMD-HT + Random Forest framework with geophone sensors. The system was trained on a diverse dataset including pedestrian, bicycle, and vehicle vibrations, demonstrating high robustness to noise.
Results: Achieved over 91% detection accuracy for different intrusion types with an inference time of 0.15s per segment, enabling reliable real-time alerts and reducing false positives. The system's interpretability allowed for easy fine-tuning based on specific site characteristics.
Calculate Your Potential ROI in Predictive Monitoring
Estimate the cost savings and efficiency gains your organization could achieve by implementing advanced seismic signal classification for asset monitoring and security.
Annual Savings
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Hours Reclaimed
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Phased Implementation Roadmap
A typical deployment involves these key stages, tailored to your specific operational context and data infrastructure.
Phase 1: Data Acquisition & Preprocessing Setup (2-4 Weeks)
Establish sensor network, data logging, and implement initial DC removal and band-pass filtering for clean signal acquisition.
Phase 2: Decomposition & Feature Engineering (3-6 Weeks)
Integrate VMD-HT for optimal mode separation and extract marginal spectral features. Fine-tune hyperparameters for your specific vibration profiles.
Phase 3: Model Training & Validation (4-8 Weeks)
Train ensemble classifiers (e.g., Random Forest) with group-stratified cross-validation. Validate performance on diverse real-world scenarios.
Phase 4: Deployment & Continuous Optimization (Ongoing)
Deploy the trained model on edge devices for real-time inference. Monitor performance and periodically retrain with new data for adaptive learning.
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