A Single Array Approach for Infrasound Signal Discrimination from Quarry Blasts via Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Pre-Processing
2.3. Feature Extraction
- Number of detections. This feature helps to distinguish between coherent noise sources (e.g., the power plant) and sources when multiple detections belong to the same “event” (e.g., storms) and between, for example, explosions which are usually solitary (or at least have only a few neighbors) on the time–azimuth diagram.
- The difference between the mean azimuth in the box and the detection’s azimuth. For the MPP, almost zero is expected for this value since it is not a moving source. For storms, this feature varies, depending on the movement of the storm relative to the station (visually, the angle on the time–azimuth domain). For quarry blasts, the value also varies. When no neighboring detections are present, basically this feature takes a value of zero, whereas a big difference from the mean is expected when there are adjacent detections.
- The standard deviation of the azimuths in the box. As with the previous feature, we expect larger values for storms and lower ones for MPP. For quarry blasts the same is true as with the foregoing feature.
2.4. Model Selection
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Transformation | Parameter Range | Probability |
---|---|---|
Gaussian noise | 0.01–0.05 | 0.5 |
Time shift | 0.0–0.3 | 1 |
Time domain mask | 0.0–0.03 | 1 |
Frequency domain mask | 0.0–0.03 | 1 |
Storm | Quarry Blast | MPP | |
---|---|---|---|
Training | 3962 (75%) | 568 (58%) | 5334 (75%) |
Validation | 834 (15%) | 203 (21%) | 1161 (15%) |
Test | 869 (15%) | 208 (21%) | 1120 (15%) |
Parameter | Values |
---|---|
Number of trees | 20, 30, 40, 50, 60, 80 |
Minimum samples per leaf | 2, 4, 8, 12, 16, 20, 24 |
Minimum samples per split | 2, 4, 8, 12, 16, 20, 24 |
Maximum depth | 10, 20, 30, 40, 50 |
Maximum features | all, log2, square root |
Parameter | Values |
---|---|
C | 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, 1000 |
kernel | Radial Basis Function |
scale, auto |
Training CV Mean f1 Score | Training CV f1 Score Standard Deviation | Validation f1 Score | Test f1 Score | |
---|---|---|---|---|
Random forest with 11 features | 0.84 | 0.009 | 0.89 | 0.88 |
Random forest with 14 features | 0.86 | 0.005 | 0.92 | 0.92 |
SVM with 11 features | 0.83 | 0.016 | 0.88 | 0.88 |
SVM with 14 features | 0.88 | 0.012 | 0.93 | 0.93 |
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Pásztor, M.; Czanik, C.; Bondár, I. A Single Array Approach for Infrasound Signal Discrimination from Quarry Blasts via Machine Learning. Remote Sens. 2023, 15, 1657. https://doi.org/10.3390/rs15061657
Pásztor M, Czanik C, Bondár I. A Single Array Approach for Infrasound Signal Discrimination from Quarry Blasts via Machine Learning. Remote Sensing. 2023; 15(6):1657. https://doi.org/10.3390/rs15061657
Chicago/Turabian StylePásztor, Marcell, Csenge Czanik, and István Bondár. 2023. "A Single Array Approach for Infrasound Signal Discrimination from Quarry Blasts via Machine Learning" Remote Sensing 15, no. 6: 1657. https://doi.org/10.3390/rs15061657