Monitoring urban construction and quarry blasts with low-cost seismic sensors and deep learning tools in the city of Oslo, Norway
DOI:
https://doi.org/10.26443/seismica.v3i1.1166Keywords:
machine learning, Earthquake detection, explosion monitoring, smart cityAbstract
The aim of this study is to collect information about events in the city of Oslo, Norway, that produce a seismic signature. In particular, we focus on blasts from the ongoing construction of tunnels and under-ground water storage facilities under populated areas in Oslo. We use seismic data recorded simultaneously on up to 11 Raspberry Shake sensors deployed between 2021 and 2023 to quickly detect, locate, and classify urban seismic events. We present a deep learning approach to first identify rare events and then to build an automatic classifier from those templates. For the first step, we employ an outlier detection method using auto-encoders trained on continuous background noise. We detect events using an STA/LTA trigger and apply the auto-encoder to those. Badly reconstructed signals are identified as outliers and subsequently located using their surface wave (Rg) signatures on the seismic network. In a second step, we train a supervised classifier using a Convolutional Neural Network to detect events similar to the identified blast signals. Our results show that up to 87% of about 1,900 confirmed blasts are detected and locatable in challenging background noise conditions. We demonstrate that a city can be monitored automatically and continuously for explosion events, which allows implementing an alert system for future smart city solutions.
References
Al Nuaimi, E., Al Neyadi, H., Mohamed, N., & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1), 1–15. https://doi.org/10.1186/s13174-015-0041-5 DOI: https://doi.org/10.1186/s13174-015-0041-5
Bergen, K. J., Chen, T., & Li, Z. (2019). Preface to the Focus Section on Machine Learning in Seismology. Seismological Research Letters, 90(2A), 477–480. https://doi.org/10.1785/0220190018 DOI: https://doi.org/10.1785/0220190018
Bergen University, E. S. (2012). Operation of the Norwegian National Seismic Network 2011 [Techreport]. Department of Earth Science University of Bergen. https://nnsn.geo.uib.no/reports/2011/All_reports_2011.pdf
Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., & Wassermann, J. (2010). ObsPy: A Python toolbox for seismology. Seismological Research Letters, 81(3), 530–533. https://doi.org/10.1785/gssrl.81.3.530 DOI: https://doi.org/10.1785/gssrl.81.3.530
Bouchard, S., L’Heureux, J.-S., Johansson, J., Leroueil, S., & LeBoeuf, D. (2018). Blasting induced landslides in sensitive clays. In Landslides and Engineered Slopes. Experience, Theory and Practice (pp. 497–504). CRC Press. DOI: https://doi.org/10.1201/9781315375007-42
Brockerhoff, M. (1999). Urban growth in developing countries: a review of projections and predictions. Population and Development Review, 25(4), 757–778. https://doi.org/10.1111/j.1728-4457.1999.00757.x DOI: https://doi.org/10.1111/j.1728-4457.1999.00757.x
Chamarczuk, M., Nishitsuji, Y., Malinowski, M., & 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. https://doi.org/10.1785/0220190063 DOI: https://doi.org/10.1785/0220190063
Chang, S. E., McDaniels, T., Fox, J., Dhariwal, R., & Longstaff, H. (2014). Toward disaster-resilient cities: Characterizing resilience of infrastructure systems with expert judgments. Risk Analysis, 34(3), 416–434. https://doi.org/10.1111/risa.12133 DOI: https://doi.org/10.1111/risa.12133
Chollet, F., & others. (2015). Keras. GitHub. https://github.com/fchollet/keras
Dando, B. D., Goertz-Allmann, B. P., Brissaud, Q., Köhler, A., Schweitzer, J., Kværna, T., & Liashchuk, A. (2023). Identifying attacks in the Russia–Ukraine conflict using seismic array data. Nature, 1–6. https://doi.org/10.1038/s41586-023-06416-7 DOI: https://doi.org/10.21203/rs.3.rs-2613796/v1
Dı́az, J., Ruiz, M., Sánchez-Pastor, P. S., & Romero, P. (2017). Urban seismology: On the origin of earth vibrations within a city. Scientific Reports, 7(1), 15296. https://doi.org/10.1038/s41598-017-15499-y DOI: https://doi.org/10.1038/s41598-017-15499-y
Dowding, C. H. (2016). Blast Vibration Monitoring for Engineering (J. A. Hudson, Ed.; Vol. 4). Elsevier.
Fiori, R., Vergne, J., Schmittbuhl, J., & Zigone, D. (2023). Monitoring induced microseismicity in an urban context using very small seismic arrays: The case study of the Vendenheim EGS project. Geophysics, 88(5), WB71–WB87. https://doi.org/10.1190/geo2022-0620.1 DOI: https://doi.org/10.1190/geo2022-0620.1
Fischer, J., Redlich, J.-P., Scheuermann, B., Schiller, J., Günes, M., Nagel, K., Wagner, P., Scheidgen, M., Zubow, A., Eveslage, I., & others. (2013). From earthquake detection to traffic surveillance–about information and communication infrastructures for smart cities. System Analysis and Modeling: Theory and Practice: 7th International Workshop, SAM 2012, Innsbruck, Austria, October 1-2, 2012. Revised Selected Papers 7, 121–141. https://doi.org/10.1007/978-3-642-36757-1_8 DOI: https://doi.org/10.1007/978-3-642-36757-1_8
Gharti, H. N., Oye, V., Roth, M., & Kühn, D. (2010). Automated microearthquake location using envelope stacking and robust global optimization. Geophysics, 75(4), MA27–MA46. https://doi.org/10.1190/1.3432784 DOI: https://doi.org/10.1190/1.3432784
Gibbons, S. J., & Ringdal, F. (2006). The detection of low magnitude seismic events using array-based waveform correlation. Geophysical Journal International, 165(1), 149–166. https://doi.org/10.1111/j.1365-246X.2006.02865.x DOI: https://doi.org/10.1111/j.1365-246X.2006.02865.x
Hillers, G., T. Vuorinen, T. A., Uski, M. R., Kortström, J. T., Mäntyniemi, P. B., Tiira, T., Malin, P. E., & Saarno, T. (2020). The 2018 geothermal reservoir stimulation in Espoo/Helsinki, southern Finland: Seismic network anatomy and data features. Seismological Research Letters, 91(2A), 770–786. https://doi.org/10.1785/0220190253 DOI: https://doi.org/10.1785/0220190253
Jenkins, W. F., Gerstoft, P., Bianco, M. J., & Bromirski, P. D. (2021). Unsupervised deep clustering of seismic data: Monitoring the Ross Ice Shelf, Antarctica. Journal of Geophysical Research: Solid Earth, 126(9), e2021JB021716. https://doi.org/10.1029/2021JB021716 DOI: https://doi.org/10.1029/2021JB021716
Johnson, C. W., Ben-Zion, Y., Meng, H., & Vernon, F. (2020). Identifying different classes of seismic noise signals using unsupervised learning. Geophysical Research Letters, 47(15), e2020GL088353. https://doi.org/10.1029/2020GL088353 DOI: https://doi.org/10.1029/2020GL088353
Kalinowski, M. B., & Mialle, P. (2021). Introduction to the topical issue on nuclear explosion monitoring and verification: scientific and technological advances. Pure and Applied Geophysics, 178(7), 2397–2401. https://doi.org/10.1007/s00024-021-02783-2 DOI: https://doi.org/10.1007/s00024-021-02783-2
Köhler, A, Myklebust, E., & Mæland, S. (2022). Enhancing seismic calving event identification in Svalbard through empirical matched field processing and machine learning. Geophysical Journal International, 230(2), 1305–1317. https://doi.org/10.1093/gji/ggac117 DOI: https://doi.org/10.1093/gji/ggac117
Köhler, Andreas. (2021). GEObyIT seismic network in Oslo, Norway. https://doi.org/10.7914/3dms-sj84
Köhler, Andreas, & Myklebust, E. B. (2024). Code for monitoring urban construction and quarry blasts with low-cost seismic sensors and machine learning tools in the city of Oslo, Norway. https://doi.org/10.5281/zenodo.10777734
Köhler, Andreas, Ohrnberger, M., & Scherbaum, F. (2010). Unsupervised pattern recognition in continuous seismic wavefield records using self-organizing maps. Geophysical Journal International, 182(3), 1619–1630. https://doi.org/10.1111/j.1365-246X.2010.04709.x DOI: https://doi.org/10.1111/j.1365-246X.2010.04709.x
Kong, Q., Allen, R. M., Schreier, L., & Kwon, Y.-W. (2016). MyShake: A smartphone seismic network for earthquake early warning and beyond. Science Advances, 2(2), e1501055. https://doi.org/10.1126/sciadv.1501055 DOI: https://doi.org/10.1126/sciadv.1501055
Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J., & Gerstoft, P. (2019). Machine learning in seismology: Turning data into insights. Seismological Research Letters, 90(1), 3–14. https://doi.org/10.1785/0220180259 DOI: https://doi.org/10.1785/0220180259
Kraft, T., Mai, P. M., Wiemer, S., Deichmann, N., Ripperger, J., Kästli, P., Bachmann, C., Fäh, D., Wössner, J., & Giardini, D. (2009). Enhanced geothermal systems: Mitigating risk in urban areas. Eos, Transactions American Geophysical Union, 90(32), 273–274. https://doi.org/10.1029/2009EO320001 DOI: https://doi.org/10.1029/2009EO320001
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 25). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
Leonard, M., & Kennett, B. (1999). Multi-component autoregressive techniques for the analysis of seismograms. Physics of the Earth and Planetary Interiors, 113(1–4), 247–263. https://doi.org/https://doi.org/10.1016/S0031-9201(99)00054-0 DOI: https://doi.org/10.1016/S0031-9201(99)00054-0
McKinsey. (2018). Smart cities: Digital solution for a more liveable future. NY: McKinsey Global Institute.
Mousavi, S. M., & Beroza, G. C. (2023). Machine Learning in Earthquake Seismology. Annual Review of Earth and Planetary Sciences, 51. https://doi.org/10.1146/annurev-earth-071822-100323 DOI: https://doi.org/10.1146/annurev-earth-071822-100323
Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications, 11(1), 1–12. https://doi.org/10.1038/s41467-020-17591-w DOI: https://doi.org/10.1038/s41467-020-17591-w
Mousavi, S. M., Zhu, W., Ellsworth, W., & Beroza, G. (2019). Unsupervised clustering of seismic signals using deep convolutional autoencoders. IEEE Geoscience and Remote Sensing Letters, 16(11), 1693–1697. https://doi.org/10.1109/LGRS.2019.2909218 DOI: https://doi.org/10.1109/LGRS.2019.2909218
Navarro, J., Schiavon, A., Vieira, M., & Silva, P. (2019). Deep seismic compression. 81st EAGE Conference and Exhibition 2019, 1–5. https://doi.org/10.3997/2214-4609.201901620 DOI: https://doi.org/10.3997/2214-4609.201901620
Naveen, G., Sastry, V., & Chandar, K. R. (2021). Assessment of Structural Damage Due to Blasting in Hydro Power Tunnel. International Conference on Geotechnical Challenges in Mining, Tunneling and Underground Infrastructures, 229–240. DOI: https://doi.org/10.1007/978-981-16-9770-8_13
Nugent, J. (2018). Raspberry Shake: Watch the Earth move under your feet. Science Scope, 42(4), 22–25. https://www.jstor.org/stable/26611884 DOI: https://doi.org/10.2505/4/ss18_042_04_22
Nuha, H. H., Balghonaim, A., Liu, B., Mohandes, M., Deriche, M., & Fekri, F. (2020). Deep neural networks with extreme learning machine for seismic data compression. Arabian Journal for Science and Engineering, 45(3), 1367–1377. https://doi.org/10.1007/s13369-019-03942-3 DOI: https://doi.org/10.1007/s13369-019-03942-3
OpenStreetMap contributors. (2017). Planet dump retrieved from https://planet.osm.org . %7Bhttps://www.openstreetmap.org%7D
Ottemöller, L., Michalek, J., Christensen, J.-M., Baadshaug, U., Halpaap, F., Natvik, Ø., Kværna, T., & Oye, V. (2021). UiB-NORSAR EIDA node: Integration of seismological data in Norway. Seismological Society of America, 92(3), 1491–1500. https://doi.org/10.1785/0220200369 DOI: https://doi.org/10.1785/0220200369
Ottemöller, L., Strømme, M. L., & Storheim, B. M. (2018). Seismic monitoring and data processing at the Norwegian National Seismic Network. Summary of the Bulletin of the International Seismological Centre, 52(I), 27–40. DOI: https://doi.org/10.31905/1M97CSYL
Provost, F., Hibert, C., & Malet, J.-P. (2017). Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier. Geophysical Research Letters, 44(1), 113–120. https://doi.org/10.1002/2016GL070709 DOI: https://doi.org/10.1002/2016GL070709
Ritter, J. R., Balan, S. F., Bonjer, K.-P., Diehl, T., Forbriger, T., Mărmureanu, G., Wenzel, F., & Wirth, W. (2005). Broadband urban seismology in the Bucharest metropolitan area. Seismological Research Letters, 76(5), 574–580. https://doi.org/10.1785/gssrl.76.5.574 DOI: https://doi.org/10.1785/gssrl.76.5.574
Seydoux, L., Balestriero, R., Poli, P., Hoop, M. de, Campillo, M., & Baraniuk, R. (2020). Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nature Communications, 11(1), 3972. https://doi.org/10.1038/s41467-020-17841-x DOI: https://doi.org/10.1038/s41467-020-17841-x
Shallan, O., Eraky, A., Sakr, T., & Emad, S. (2014). Response of building structures to blast effects. International Journal of Engineering and Innovative Technology, 4(2), 167–175. https://www.humanitarianlibrary.org/sites/default/files/2018/10/IJEIT1412201408_30.pdf
Sick, B., Guggenmos, M., & Joswig, M. (2015). Chances and limits of single-station seismic event clustering by unsupervised pattern recognition. Geophysical Journal International, 201(3), 1801–1813. https://doi.org/10.1093/gji/ggv126 DOI: https://doi.org/10.1093/gji/ggv126
Spica, Z. J., Perton, M., Martin, E. R., Beroza, G. C., & Biondi, B. (2020). Urban seismic site characterization by fiber-optic seismology. Journal of Geophysical Research: Solid Earth, 125(3), e2019JB018656. https://doi.org/10.1029/2019JB018656 DOI: https://doi.org/10.1029/2019JB018656
Steinmann, Rene, Seydoux, L., Beaucé, E., & Campillo, M. (2022). Hierarchical exploration of continuous seismograms with unsupervised learning. Journal of Geophysical Research: Solid Earth, 127(1), e2021JB022455. https://doi.org/10.1029/2021JB022455 DOI: https://doi.org/10.1029/2021JB022455
Steinmann, René, Seydoux, L., & Campillo, M. (2022). AI-based unmixing of medium and source signatures from seismograms: ground freezing patterns. Geophysical Research Letters, 49(15), e2022GL098854. https://doi.org/10.1029/2022GL098854 DOI: https://doi.org/10.1029/2022GL098854
Thill, M., Konen, W., Wang, H., & Bäck, T. (2021). Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing, 112, 107751. https://doi.org/10.1016/j.asoc.2021.107751 DOI: https://doi.org/10.1016/j.asoc.2021.107751
Valentine, A. P., & Trampert, J. (2012). Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data. Geophysical Journal International, 189(2), 1183–1202. https://doi.org/10.1111/j.1365-246X.2012.05429.x DOI: https://doi.org/10.1111/j.1365-246X.2012.05429.x
Wang, Y., Yao, H., & Zhao, S. (2016). Auto-encoder based dimensionality reduction. Neurocomputing, 184, 232–242. https://doi.org/10.1016/j.neucom.2015.08.104 DOI: https://doi.org/10.1016/j.neucom.2015.08.104
Yin, C., Zhang, S., Wang, J., & Xiong, N. N. (2020). Anomaly detection based on convolutional recurrent autoencoder for IoT time series. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1), 112–122. https://doi.org/10.6038/pg2020DD0013 DOI: https://doi.org/10.1109/TSMC.2020.2968516
Yoon, C. E., O’Reilly, O., Bergen, K. J., & Beroza, G. C. (2015). Earthquake detection through computationally efficient similarity search. Science Advances, 1(11), e1501057. https://doi.org/10.1126/sciadv.1501057 DOI: https://doi.org/10.1126/sciadv.1501057
Zhang, K., Ni, J., Yang, K., Liang, X., Ren, J., & Shen, X. S. (2017). Security and privacy in smart city applications: Challenges and solutions. IEEE Communications Magazine, 55(1), 122–129. https://doi.org/10.1109/MCOM.2017.1600267CM DOI: https://doi.org/10.1109/MCOM.2017.1600267CM
Zheng, H., & Zhang, B. (2020). Intelligent seismic data interpolation via convolutional neural network. Progress in Geophysics, 35(2), 721–727. https://doi.org/https://doi.org/10.6038/pg2020DD0013
Zhu, W., & Beroza, G. C. (2018). PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261–273. https://doi.org/10.1093/gji/ggy423 DOI: https://doi.org/10.1093/gji/ggy423
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