Pattern-of-Life Activity Recognition In Seismic Data

References

• Ackerman, M., and S. Dasgupta (2014). Incremental clustering: The case for extra clusters. In Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal Canada, pp. 307–2761.
   Google Scholar
• Adams, R. P., and D. J. MacKay (2007). Bayesian online changepoint detection. arXiv:0710.3742.
   Google Scholar
• Allen, R. 1982. Automatic phase pickers: Their present use and future prospects. Bulletin of the Seismological Society of America 72 (6B):S225–S242.
   Google Scholar
• Chamarczuk, M., Nishitsuji, Y., Malinowski, M., and Draganov, D. 2020. Unsupervised learning used in automatic detection and classification of ambient‐noise recordings from a large‐N array. Seismological Research Letters 91 (1):370–389.
   Web of Science ®Google Scholar
• Craddock, R., D. Watson, and W. Saunders (2016). Generic pattern of life and behaviour analysis. In IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Diego, USA. IEEE Press.
   Google Scholar
• Ghosh, R., A. Akula, S. Kumar, and H. K. Sardana. 2015. Time–frequency analysis based robust vehicle detection using seismic sensor. Journal of Sound and Vibration 346:424–34.
   Web of Science ®Google Scholar
• Ghosh, R., and Sardana, H. K. 2020, June. Multi-feature optimization strategies for target classification using seismic and acoustic signatures. In Automatic Target Recognition (Vol. 11394, p. 113940). International Society for Optics and Photonics.
   Google Scholar
• Huang, J., Q. Zhou, X. Zhang, E. Song, B. Li, and X. Yuan. 2013. Seismic target classification using a wavelet packet manifold in unattended ground sensors systems. Sensors 13 (7):8534–50. doi:10.3390/s130708534.
   PubMed Web of Science ®Google Scholar
• Jin, X., S. Sarkar, A. Ray, S. Gupta, and T. Damarla. 2011. Target detection and classification using seismic and pir sensors. IEEE Sensors Journal 12 (6):1709–18.
   Web of Science ®Google Scholar
• Johnson, S. C. 1967. Hierarchical clustering schemes. Psychometrika 32 (3):241–54. doi:10.1007/BF02289588.
   PubMed Web of Science ®Google Scholar
• Johnson, C. W., Ben‐Zion, Y., Meng, H., and Vernon, F. 2020. Identifying different classes of seismic noise signals using unsupervised learning. Geophysical Research Letters 47(15):e2020GL088353.
   Web of Science ®Google Scholar
• Kalra, M., S. Kumar, and B. Das. 2018. Target detection using smooth pseudo Wigner-Ville distribution. IEEE Recent Advances in Intelligent Computational Systems (RAICS), Thiruvananthapuram, India. IEEE Press, pp. 6-10.
   Google Scholar
• Kalra, M., S. Kumar, and B. Das. 2020. Seismic signal analysis using empirical wavelet transform for moving ground target detection and classification. IEEE Sensors Journal 20 (14):7886-7895.
   Web of Science ®Google Scholar
• Killick, R., P. Fearnhead, and I. Eckley. 2012. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association 107 (500):1590–98.
   Web of Science ®Google Scholar
• Lan, J., S. Nahavandi, T. Lan, and Y. Yin. 2005. Recognition of moving ground targets by measuring and processing seismic signal. Measurement 37 (2):189–99.
   Web of Science ®Google Scholar
• Lloyd, S. P. 1982. Least squares quantization in PCM. IEEE Transactions on Information Theory 28 (2):129–37.
   Web of Science ®Google Scholar
• McNamara, D. E., and R. Boaz. 2006. Seismic noise analysis system using power spectral density probability density functions: A stand-alone software package. Reston, Virginia: US Geological Survey: Citeseer.
   Google Scholar
• Moore, A. W. 1998. Very fast EM-based mixture model clustering using multiresolution kd-trees. Advances in Neural information processing systems, Denver, CO, USA.
   Google Scholar
• Ntalampiras, S. 2018. Moving vehicle classification using wireless acoustic sensor networks. IEEE Transactions on Emerging Topics in Computational Intelligence 2(2): 129–138.
   Google Scholar
• Riahi, N., and Gerstoft, P. 2017. Using graph clustering to locate sources within a dense sensor array. Signal Processing 132:110–120.
   Web of Science ®Google Scholar
• Scott, A. J., and M. Knott. 1974. A cluster analysis method for grouping means in the analysis of variance. Biometrics 30:507–12.
   Web of Science ®Google Scholar
• Snover, D. et al. 2021. Deep clustering to identify sources of urban seismic noise in Long Beach, California. Seismological Society of America 92(2A):1011–1022.
   Google Scholar
• Stephane, M. (1999). A wavelet tour of signal processing.
   Google Scholar
• Tian, Y., H. Qi, and X. Wang. 2002. Target detection and classification using seismic signal processing in unattended ground sensor systems. IEEE International Conference on Acoustics Speech and Signal Processing 4 :4172-4172.
   Google Scholar
• Tibshirani, R., G. Walther, and T. Hastie. 2001. Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society. Series B, Statistical Methodology 63 (2):411–23.
   Web of Science ®Google Scholar
• van der Maaten, L., and G. Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9:2579–605.
   Web of Science ®Google Scholar
• Vasco, D. W., J. E. Peterson Jr, and E. L. Majer. 1996. A simultaneous inversion of seismic traveltimes and amplitudes for velocity and attenuation. Geophysics 61 (6):1738–57.
   Web of Science ®Google Scholar
• Watanabe, S. 2013. A widely applicable Bayesian information criterion. Journal of Machine Learning Research 14(Mar):867–97.
   Web of Science ®Google Scholar
• Welch, P. 1967. The use of fast Fourier transforms for the estimation of power spectra: A method based on time averaging over short modified periodograms. IEEE Transactions on Audio and Electroacoustics 15:70–73.
   Google Scholar
• William, P. E., and M. W. Hoffman. 2011. Classification of military ground vehicles using time domain harmonics’ amplitudes. IEEE Transactions on Instrumentation and Measurement 60 (11):3720–31.
   Web of Science ®Google Scholar
• Wold, S., K. Esbensen, and P. Geladi. 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2 (1–3):37–52.
   Web of Science ®Google Scholar