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Academic literature on the topic 'Anthropogenic Seismic Sources'
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Journal articles on the topic "Anthropogenic Seismic Sources"
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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, no. 6509 (July 23, 2020): 1338–43. http://dx.doi.org/10.1126/science.abd2438.
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Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. Although the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This quiet period provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of human activities.
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Schippkus, Sven, Mikaël Garden, and Götz Bokelmann. "Characteristics of the Ambient Seismic Field on a Large-N Seismic Array in the Vienna Basin." Seismological Research Letters 91, no. 5 (July 29, 2020): 2803–16. http://dx.doi.org/10.1785/0220200153.
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Abstract The ambient seismic field is now routinely used for imaging and monitoring purposes. Most commonly, applications aim at resolving crustal-scale features and utilize ocean-generated surface waves. At smaller scales and at frequencies above the microseismic peaks, local sources of seismic energy, often anthropogenic, are dominant, and understanding of their contributions to the ambient seismic field becomes important to apply ambient noise techniques. This study uses data of an industrial-scale seismic deployment covering ∼500 km2 with 10,532 stations, each equipped with several collocated 10 Hz geophones, to provide unique insight into anthropogenic sources of seismic energy in a suburban-to-rural area. We compute amplitude levels, their distance dependency, power spectral densities, and spectrograms to describe the source characteristics. The sources we observe in great detail include windmills, a railway track and trains, cars, oil pumpjacks, power lines, gas pipelines, and airplanes. These sources exhibit time-dependent behavior that is illustrated strikingly by videos of amplitude levels in certain frequency bands that we provide as supplemental material. The data described in this study are a potential resource for future studies, such as automatic signal classification, as well as underground imaging using microseismic noise or the sources presented here.
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Zhu, Tieyuan, Junzhu Shen, and Eileen R. Martin. "Sensing Earth and environment dynamics by telecommunication fiber-optic sensors: an urban experiment in Pennsylvania, USA." Solid Earth 12, no. 1 (January 28, 2021): 219–35. http://dx.doi.org/10.5194/se-12-219-2021.
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Abstract. Continuous seismic monitoring of the Earth's near surface (top 100 m), especially with improved resolution and extent of data both in space and time, would yield more accurate insights about the effect of extreme-weather events (e.g., flooding or drought) and climate change on the Earth's surface and subsurface systems. However, continuous long-term seismic monitoring, especially in urban areas, remains challenging. We describe the Fiber Optic foR Environmental SEnsEing (FORESEE) project in Pennsylvania, USA, the first continuous-monitoring distributed acoustic sensing (DAS) fiber array in the eastern USA. This array is made up of nearly 5 km of pre-existing dark telecommunication fiber underneath the Pennsylvania State University campus. A major thrust of this experiment is the study of urban geohazard and hydrological systems through near-surface seismic monitoring. Here we detail the FORESEE experiment deployment and instrument calibration, and describe multiple observations of seismic sources in the first year. We calibrate the array by comparison to earthquake data from a nearby seismometer and to active-source geophone data. We observed a wide variety of seismic signatures in our DAS recordings: natural events (earthquakes and thunderstorms) and anthropogenic events (mining blasts, vehicles, music concerts and walking steps). Preliminary analysis of these signals suggests DAS has the capability to sense broadband vibrations and discriminate between seismic signatures of different quakes and anthropogenic sources. With the success of collecting 1 year of continuous DAS recordings, we conclude that DAS along with telecommunication fiber will potentially serve the purpose of continuous near-surface seismic monitoring in populated areas.
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Dobrorodny, Vladimir I., and Oksana A. Kopylova. "CHARACTERISTICS OF MICROSEISMS AND ACOUSTIC NOISES IN THE TRANSPORT POLYGON CONDITIONS." Interexpo GEO-Siberia 4, no. 1 (May 21, 2021): 118–25. http://dx.doi.org/10.33764/2618-981x-2021-4-1-118-125.
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The work is related to numerical estimation and comparative analysis of microseismic and acoustic noise levels in transport polygon conditions. The aim of the work is to study and further define the difference between the signal and noise to improve the ability to detect poorly distinguishable events, as well as to study the propagation features of the interrelated seismic and acoustic wave fields. It is related to the fact that wave processes generated by many natural and anthropogenic sources are conjugate nature. In particular, it is related to simultaneous propagation of seismic waves in the ground and acoustic waves in the atmosphere. It determines expediency of taking into account simultaneously parameters of both types of waves in the tasks of geophysical monitoring of the environment, as well as in solving some applied problems of seismics and acoustics.
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Qin, Lei, Frank L. Vernon, Christopher W. Johnson, and 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, no. 5 (August 27, 2019): 1948–67. http://dx.doi.org/10.1785/0120190070.
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Abstract We investigate coherences of seismic data recorded during three years (2015–2017) at the Piñon Flats Observatory (PY) array and a collocated 148 m deep borehole station B084, along with oceanic data from a buoy southwest of the PY array. Seismic and barometric recordings at PY stations are analyzed with a multitaper spectral technique. The coherence of signals from seismic sources is >0.6 at 0.05–8 Hz between closely spaced (<65 m) surface stations and decreases to ∼0.2 in frequency bands in which the wavelengths are smaller than interstation distances. There are several local coherence increases at 1–8 Hz between nearby (<65 m) surface stations, whereas large coherence values between a surface and 148 m deep borehole stations are only present at the secondary microseism (∼0.14 Hz). These points to significant modification of seismic recordings in the top crust, and those continual near‐surface failures might produce shallow rapidly attenuating signals at surface stations. Incoherent local atmospheric effects induce incoherent seismic signals in low‐ and high‐frequency ranges through different coupling mechanisms. Between 0.003 and 0.05 Hz, atmospheric loadings generate ground tilts that contaminate the two horizontal seismic recordings and decrease their coherence, whereas the vertical component is less affected. At 1–8 Hz, coupling of atmospheric pressure with surface structures transmits incoherent signals into the ground, degrading the seismic coherence in all three components. The two horizontal coherences show seasonal variations with extended coherent frequency bands in winter and spring, likely to be produced by seasonal variations in microseisms and local ground tilts. The coherences also contain high anomalies between 2 and 4 Hz resulting from anthropogenic activities. The results provide useful information on instrument characteristics and variations in the shallow crustal response to earthquakes, seasonal and ambient sources of seismic energy, along with atmospheric pressure–temperature changes and anthropogenic activities.
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Harris, David, Julie Albaric, Bettina Goertz-Allmann, Daniela Kuehn, Sebastian Sikora, and Volker Oye. "Interference suppression by adaptive cancellation in a high Arctic seismic experiment." GEOPHYSICS 82, no. 4 (July 1, 2017): V201—V209. http://dx.doi.org/10.1190/geo2016-0452.1.
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Mechanical and electromagnetic interference (process noise) is common in seismic data recorded to monitor and characterize induced microseismicity during industrial injection and production operations. We have developed a case study of adaptive cancellation to reduce observed process noise in passive seismic data recorded during the 2014 injection test at the [Formula: see text] Lab research site in Spitsbergen. Our results suggest that adaptive cancellation is effective when major sources of interference are readily identifiable. Adaptive cancellation requires these sources to be instrumented separately but conceivably with low-cost sensors. We suggest that adaptive cancellation should be considered routinely when planning microseismic monitoring operations when strong industrial or anthropogenic noise is anticipated. Interference suppression algorithms are sufficiently simple that they could be implemented in acquisition systems to avoid archival of noise reference data.
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Kumar, Santosh, R. Chaitanya Kumar, Ketan Singha Roy, and Sumer Chopra. "Seismic Monitoring in Gujarat, India, during 2020 Coronavirus Lockdown and Lessons Learned." Seismological Research Letters 92, no. 2A (February 3, 2021): 849–58. http://dx.doi.org/10.1785/0220200260.
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Abstract The Gujarat region, situated in the westernmost part of India, experienced a deadly intraplate 2001 Mw 7.6 Bhuj earthquake. In the aftermath of the disaster, the Institute of Seismological Research established the Gujarat (India) seismic network in 2006. The network is being operated in online and offline modes, whereas, seismicity monitoring is being done in near-real-time, using data received from the online seismic stations. The Coronavirus disease-19 lockdown provided an opportunity to assess the network reliability in a difficult and challenging scenario. The positive aspect of the lockdown is reflected in signal-to-noise ratio, which improved significantly at all the sites during the lockdown, with more prominent being at sites located on top of the Quaternary sediments due to the absence of high-frequency anthropogenic noise. A sharp fall in the seismic background noise is noticed at most of the stations during the lockdown period, with respect to the prelockdown period. We used the lockdown data to identify other natural sources of noise, besides anthropogenic. The lockdown helped in solving the enigma of seismicity in certain pockets, which turned out to be related to quarry blasts.
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Lehujeur, Maximilien, Jérôme Vergne, Alessia Maggi, and 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, no. 6 (November 1, 2017): B209—B217. http://dx.doi.org/10.1190/geo2017-0136.1.
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Correlating ambient seismic noise allows us to image the subsoil in various contexts and at different scales. Applying this technique to anthropogenic seismic noise can be challenging when the spatial distribution of the sources is not uniform. We have addressed the feasibility of exploiting this kind of noise in addition to microseismic noise to extend the reconstruction of Rayleigh-wave dispersion at periods between 0.2 and 1 s. We used data acquired with two small aperture arrays ([Formula: see text] stations with a 200 m helical distribution) deployed near the deep geothermal site of Rittershoffen (Alsace, France). In this region, the sparse human activity causes strong seismic noise, whose nonuniform spatial distribution limits our ability to determine the surface wave velocity between stations using the classical noise correlation technique at periods of less than 1 s. We have used double beamforming to isolate the noise sources that contribute constructively to the empirical Green’s function between the two arrays and recovered the Rayleigh-wave dispersion curve at periods less than 1 s. Using a probabilistic inversion, we found that such data, combined with surface wave measurements at periods greater than 1 s, are helpful to improve the reliability of [Formula: see text] and [Formula: see text] profiles at depths down to the deep-geothermal reservoir (2.5 km). Such profiles are helpful in a geothermal context because they improve the location of induced seismic events, necessary for reservoir monitoring and risk assessment.
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Ranguelov, Boyko, Ruben Paul Borg, Edelvays Spassov, Fathimath Shadiya, and Antoaneta Frantzova. "Determination of Vs_30 from existing geophysical investigation data." Engineering Geology and Hydrogeology 36, no. 1 (January 10, 2023): 35–43. http://dx.doi.org/10.52321/igh.36.1.35.
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The Vs_30 refers to the velocity of transversal seismic waves in the upper earth crust (surface shallow depths) in the 0-30 m interval. This parameter reflects the integral ground properties and is used in almost all seismic hazard assessment software. The determination of Vs_30 is important, especially in the case of urban territories, where in-situ measurements are very difficult and sometimes impossible due to dense urban areas, large anthropogenic noise and the need of expensive boreholes. The paper presents a methodology for extracting necessary data and information from archival sources, mainly geophysical measurements frequently executed for different prospecting purposes. The methodology for assessment of the integral values of Vs_30 is proposed for the definition of seismic hazard maps in Bulgaria. The methodology proposed, based primarily on archive data, represents an effective approach with significant results, especially for the intensive urbanized town territories located in high seismic areas.
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Chai, Chengping, Omar Marcillo, Monica Maceira, Junghyun Park, Stephen Arrowsmith, James O. Thomas, and Joshua Cunningham. "Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning." Seismic Record 5, no. 1 (January 1, 2025): 64–72. https://doi.org/10.1785/0320240046.
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Abstract Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.
Dissertations / Theses on the topic "Anthropogenic Seismic Sources"
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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.
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Ces dernières années, une nouvelle technologie basée sur l'utilisation de fibres optiques est apparue pour surveiller les événements acoustiques naturels ou anthropogéniques : la détection acoustique distribuée (Distributed Acoustic Sensing - DAS). Cette technologie innovante permet de mesurer les vibrations sismiques à très haute résolution spatiale sur des distances allant de quelques dizaines de mètres à plusieurs centaines de kilomètres. Bien que ces données soient plus volumineuses et plus complexes à traiter que celles des sismomètres traditionnels, elles offrent des perspectives prometteuses, notamment pour l'analyse des champs d'ondes générés par les tremblements de terre, la détection des glissements de terrain, la surveillance de divers événements anthropogéniques (tels que les déplacements de piétons, les mouvements de véhicules, ou les signaux sismiques provenant des activités humaines), les événements de faible amplitude ou très localisés, et la localisation précise de l'origine de ces événements sismiques. L'objectif de cette thèse est de développer et de tester des chaînes d'analyse de données automatisées en utilisant des approches basées sur l'IA pour détecter, classer et analyser les données DAS à fibre optique en temps quasi réel. L'objectif est axé sur la surveillance locale et régionale de zones spécifiques afin de permettre la détection et l'identification en temps réel d'événements naturels tels que les tremblements de terre et les glissements de terrainIn 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