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Articoli di riviste sul tema "Anthropogenic Seismic Sources"
Lecocq, Thomas, Stephen P. Hicks, Koen Van Noten, Kasper van Wijk, Paula Koelemeijer, Raphael S. M. De Plaen, Frédérick Massin et al. "Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures". Science 369, n. 6509 (23 luglio 2020): 1338–43. http://dx.doi.org/10.1126/science.abd2438.
Testo completoSchippkus, Sven, Mikaël Garden e Götz Bokelmann. "Characteristics of the Ambient Seismic Field on a Large-N Seismic Array in the Vienna Basin". Seismological Research Letters 91, n. 5 (29 luglio 2020): 2803–16. http://dx.doi.org/10.1785/0220200153.
Testo completoZhu, Tieyuan, Junzhu Shen e Eileen R. Martin. "Sensing Earth and environment dynamics by telecommunication fiber-optic sensors: an urban experiment in Pennsylvania, USA". Solid Earth 12, n. 1 (28 gennaio 2021): 219–35. http://dx.doi.org/10.5194/se-12-219-2021.
Testo completoDobrorodny, Vladimir I., e Oksana A. Kopylova. "CHARACTERISTICS OF MICROSEISMS AND ACOUSTIC NOISES IN THE TRANSPORT POLYGON CONDITIONS". Interexpo GEO-Siberia 4, n. 1 (21 maggio 2021): 118–25. http://dx.doi.org/10.33764/2618-981x-2021-4-1-118-125.
Testo completoQin, Lei, Frank L. Vernon, Christopher W. Johnson e Yehuda Ben‐Zion. "Spectral Characteristics of Daily to Seasonal Ground Motion at the Piñon Flats Observatory from Coherence of Seismic Data". Bulletin of the Seismological Society of America 109, n. 5 (27 agosto 2019): 1948–67. http://dx.doi.org/10.1785/0120190070.
Testo completoHarris, David, Julie Albaric, Bettina Goertz-Allmann, Daniela Kuehn, Sebastian Sikora e Volker Oye. "Interference suppression by adaptive cancellation in a high Arctic seismic experiment". GEOPHYSICS 82, n. 4 (1 luglio 2017): V201—V209. http://dx.doi.org/10.1190/geo2016-0452.1.
Testo completoKumar, Santosh, R. Chaitanya Kumar, Ketan Singha Roy e Sumer Chopra. "Seismic Monitoring in Gujarat, India, during 2020 Coronavirus Lockdown and Lessons Learned". Seismological Research Letters 92, n. 2A (3 febbraio 2021): 849–58. http://dx.doi.org/10.1785/0220200260.
Testo completoLehujeur, Maximilien, Jérôme Vergne, Alessia Maggi e Jean Schmittbuhl. "Vertical seismic profiling using double-beamforming processing of nonuniform anthropogenic seismic noise: The case study of Rittershoffen, Upper Rhine Graben, France". GEOPHYSICS 82, n. 6 (1 novembre 2017): B209—B217. http://dx.doi.org/10.1190/geo2017-0136.1.
Testo completoRanguelov, Boyko, Ruben Paul Borg, Edelvays Spassov, Fathimath Shadiya e Antoaneta Frantzova. "Determination of Vs_30 from existing geophysical investigation data". Engineering Geology and Hydrogeology 36, n. 1 (10 gennaio 2023): 35–43. http://dx.doi.org/10.52321/igh.36.1.35.
Testo completoChai, Chengping, Omar Marcillo, Monica Maceira, Junghyun Park, Stephen Arrowsmith, James O. Thomas e Joshua Cunningham. "Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning". Seismic Record 5, n. 1 (1 gennaio 2025): 64–72. https://doi.org/10.1785/0320240046.
Testo completoTesi sul tema "Anthropogenic Seismic Sources"
Huynh, Camille. "Real-time seismic monitoring using DAS fiber-optic instrumentation and machine learning : towards autonomous classification of natural and anthropogenic events". Electronic Thesis or Diss., Strasbourg, 2025. http://www.theses.fr/2025STRAH001.
Testo completoIn recent years, alongside traditional seismometer-based approaches, a new technology based on the use of optical fibers has emerged for monitoring natural or anthropogenic acoustic events: Distributed Acoustic Sensing (DAS). This innovative technology enables the measurement of seismic vibrations at very high spatial resolution over distances ranging from tens of meters to several hundred kilometers. Although these data are larger and more complex to process than those from traditional seismometers, they offer promising perspectives, particularly for analyzing the wavefields generated by earthquakes, detecting landslides, monitoring various anthropogenic events (such as pedestrian movements, vehicle movements, or seismic signals from human activities), low-amplitude or highly localized events, and precisely locating the origin of these seismic events. The goal of this thesis is to develop and test automated data analysis chains using AI-based approaches to detect, classify and analyze near-real-time fiber-optics DAS data. The objective is focused on local and regional monitoring of specific areas to enable the real-time detection and identification of natural events such as earthquakes and landslides