Advanced Search

Indexed by SCI、CA、РЖ、PA、CSA、ZR、etc .

Volume 31 Issue 5
Oct.  2020
Turn off MathJax
Article Contents

Lihui Wu, Dun Wang, Ziguang Lei, Jing Fu, Shuai Min, Xianbing Xu, Sarina Bao. Campus Vibration in Nanwangshan Campus, China University of Geosciences at Wuhan Monitored by Short-Period Seismometers. Journal of Earth Science, 2020, 31(5): 950-956. doi: 10.1007/s12583-020-1332-8
Citation: Lihui Wu, Dun Wang, Ziguang Lei, Jing Fu, Shuai Min, Xianbing Xu, Sarina Bao. Campus Vibration in Nanwangshan Campus, China University of Geosciences at Wuhan Monitored by Short-Period Seismometers. Journal of Earth Science, 2020, 31(5): 950-956. doi: 10.1007/s12583-020-1332-8

Campus Vibration in Nanwangshan Campus, China University of Geosciences at Wuhan Monitored by Short-Period Seismometers

doi: 10.1007/s12583-020-1332-8
More Information
  • Continuous seismic observations can record seismic waveforms, and ambient noise, for the purposes of earthquake researches and other applications. Here we deploy three digital seismometers (EPS-2) in and around the Nanwangshan Campus of the China University of Geosciences (Wuhan). This network was running from April 9 to May 9 of 2018. During this period, the seismometers recorded the May 4, 2018 M6.9 Hawaii earthquake. From the recorded waveforms, we could observe clearly the P and S arrivals, and the corresponding particle motions. Analysis of continuous observations of ambient noise shows obvious fluctuation of vibration intensity inside of the campus. The campus is quietest from 0 to 5 am. From 5 am on, the vibration intensity increases, and reaches the peak of entire day at 12 am. The amplitude then decreases to a very low level at 19:30 to 20:00 pm, and reaches another strong noisy time at 21:00 to 21:30 pm. After 21:30 pm, the intensity goes down slowly. We also observed seismic signals that were generated by the interaction of speed-control hump cars and ground. By taking the envelope and smooth operations, we observe different characteristics for different car speeds, which suggests that seismic monitoring approaches can be used for speed measurement of cars. This kind of small seismic network running in a real time fashion, would greatly help understanding of the sources of ambient noise at high frequency bands in interested areas. Analysis of a long-term observed dataset, and real time illustration will help to strengthen campus security and high-precision laboratory deployments, and also contribute to research atmosphere in earthquake science.
  • 加载中
  • Behm, M., Leahy, G. M., Snieder, R., 2014. Retrieval of Local Surface Wave Velocities from Traffic Noise—An Example from the La Barge Basin (Wyoming). Geophysical Prospecting, 62(2):223-243. https://doi.org/10.1111/1365-2478.12080 doi:  10.1111/1365-2478.12080
    Cai, S., Guan, Z., 1979. Study on Lake Geology (Quaternary Period) of Lake Dong Hu, Wuhan, Hubei Province, China-with Comments on Its Formation and on Ancient Yun Meng Swamp. Oceanologia et Limnologia Sinica, 10(4):383-384 (in Chinese with English Abstract)
    Chen, L., Shao, C., Wang, C., 2014. Research on Wangwushan Fault and Paleoseismic Wedges in Wuhan. Acta Geologica Sinica, 88(8):1453-1460 (in Chinese with English Abstract) http://search.cnki.net/down/default.aspx?filename=DZXE201408007&dbcode=CJFD&year=2014&dflag=pdfdown
    Díaz, J., Ruiz, M., Sánchez-Pastor, P. S., et al., 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:  10.1038/s41598-017-15499-y
    Gabriel, I. V., Anghelescu, P., 2015. Vibration Monitoring System for Human Activity Detection. International Conference on Electronics Computers and Artificial Intelligence, June 25-27, 2015, Bucharest, Romania
    Geng, W., Liu, D., Cai, Y., et al., 2014. Prediction of the Influence of the Proposed Beijing Metro Line 16 on a Precise Instrument of Peking University. Earthquake Engineering & Engineering Dynamics, 34(6):19-25 (in Chinese with English Abstract) http://en.cnki.com.cn/Article_en/CJFDTOTAL-DGGC201406003.htm
    Groos, J. C., Ritter, J. R. R., 2009. Time Domain Classification and Quantification of Seismic Noise in an Urban Environment. Geophysical Journal International, 179(2):1213-1231. https://doi.org/10.1111/j.1365-246x.2009.04343.x doi:  10.1111/j.1365-246x.2009.04343.x
    Li, F. Y., Clemente, J., Valero, M., et al., 2019. Smart Home Monitoring System via Footstep-Induced Vibrations. IEEE Systems Journal, 1-7. https://doi.org/10.1109/jsyst.2019.2937960 doi:  10.1109/jsyst.2019.2937960
    Li, R., Zhang, H., Liu, W., 2008. Metro-Induced Ground Vibrations and Their Impacts on Precision Instrument. Chinese Journal of Rock Mechanics & Engineering, 27(1):206-214 (in Chinese with English Abstract) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb200801029
    Lin, H. Y., Li, K. J., Chang, C. H., 2008. Vehicle Speed Detection from a Single Motion Blurred Image. Image and Vision Computing, 26(10):1327-1337. https://doi.org/10.1016/j.imavis.2007.04.004 doi:  10.1016/j.imavis.2007.04.004
    Ma, M., Liu, W., Markine, V., et al., 2011. Measurement of Vibrations Induced by Road Traffic and Subway Trains in Laboratory. 8th International Conference on Structural Dynamics (EURODYN2011). July 4-6, 2011, Leuven
    Manea, E. F., Michel, C., Poggi, V., et al., 2016. Improving the Shear Wave Velocity Structure beneath Bucharest (Romania) Using Ambient Vibrations. Geophysical Journal International, 207(2):848-861. https://doi.org/10.1093/gji/ggw306 doi:  10.1093/gji/ggw306
    Mao, X. S., Inoue, D., Kato, S., et al., 2012. Amplitude-Modulated Laser Radar for Range and Speed Measurement in Car Applications. IEEE Transactions on Intelligent Transportation Systems, 13(1):408-413. https://doi.org/10.1109/tits.2011.2162627 doi:  10.1109/tits.2011.2162627
    Meng, M., Song, H., Weining, L., et al., 2012. Measurement of Vibration Influence on Sensitive Equipment Induced by Metro and Road Traffic. Journal of Beijing Jiaotong University, 36(4):50-54 (in Chinese with English Abstract) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bfjtdxxb201204010
    Nakata, N., Snieder, R., 2011. Near-Surface Weakening in Japan after the 2011 Tohoku-Oki Earthquake. Geophysical Research Letters, 38(17):L17302. https://doi.org/10.1029/2011gl048800 doi:  10.1029/2011gl048800
    Nakata, N., Snieder, R., Tsuji, T., et al., 2011. Shear Wave Imaging from Traffic Noise Using Seismic Interferometry by Cross-Coherence. Geophysics, 76(6):SA97-SA106. https://doi.org/10.1190/geo2010-0188.1 doi:  10.1190/geo2010-0188.1
    Pelegri, J., Alberola, J., Llario, V., 2002. Vehicle Detection and Car Speed Monitoring System Using GMR Magnetic Sensors. IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02, 2:1693-1695. https://doi.org10.1109/iecon.2002.1185535 doi:  10.1109/iecon.2002.1185535
    Qin, W. B., Zhang, S. X., Li, M. K., et al., 2018. Distribution of Intra-Crustal Low Velocity Zones beneath Yunnan from Seismic Ambient Noise Tomography. Journal of Earth Science, 29(6):1409-1418. https://doi.org/10.1007/s12583-017-0815-8 doi:  10.1007/s12583-017-0815-8
    Riahi, N., Gerstoft, P., 2015. The Seismic Traffic Footprint:Tracking Trains, Aircraft, and Cars Seismically. Geophysical Research Letters, 42(8):2674-2681. https://doi.org/10.1002/2015gl063558 doi:  10.1002/2015gl063558
    Wang, H., Quan, W., Liu, X., et al., 2013. A Two Seismic Sensor Based Approach for Moving Vehicle Detection. Procedia—Social and Behavioral Sciences, 96:2647-2653. https://doi.org/10.1016/j.sbspro.2013.08.296 doi:  10.1016/j.sbspro.2013.08.296
    Wessel, P., Smith, W. H. F., 1991. Free Software Helps Map and Display Data. Eos, Transactions American Geophysical Union, 72(41):441-441. https://doi.org/10.1029/90eo00319 doi:  10.1029/90eo00319
    Wu, J., Liu, Z., Li, J., et al., 2009. An Algorithm for Automatic Vehicle Speed Detection Using Video Camera. 2009 4th International Conference on Computer Science & Education, July 25-28, Nanjing
    Yamazaki, F., Liu, W., Vu, T. T., 2008. Vehicle Extraction and Speed Detection from Digital Aerial Images. IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2008, July 8-11, 2008, Boston
    Zou, Z. H., Zhou, H. W., Gurrola, H., et al., 2018. Impact and Solutions of Seawater Heterogeneity on Wide-Angle Tomographic Inversion of Crustal Velocities in Deep Marine Environments-Numerical Studies. Journal of Earth Science, 29(6):1380-1389. https://doi.org/10.1007/s12583-017-0816-7 doi:  10.1007/s12583-017-0816-7
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(7)

Article Metrics

Article views(103) PDF downloads(7) Cited by()

Related
Proportional views

Campus Vibration in Nanwangshan Campus, China University of Geosciences at Wuhan Monitored by Short-Period Seismometers

doi: 10.1007/s12583-020-1332-8

Abstract: Continuous seismic observations can record seismic waveforms, and ambient noise, for the purposes of earthquake researches and other applications. Here we deploy three digital seismometers (EPS-2) in and around the Nanwangshan Campus of the China University of Geosciences (Wuhan). This network was running from April 9 to May 9 of 2018. During this period, the seismometers recorded the May 4, 2018 M6.9 Hawaii earthquake. From the recorded waveforms, we could observe clearly the P and S arrivals, and the corresponding particle motions. Analysis of continuous observations of ambient noise shows obvious fluctuation of vibration intensity inside of the campus. The campus is quietest from 0 to 5 am. From 5 am on, the vibration intensity increases, and reaches the peak of entire day at 12 am. The amplitude then decreases to a very low level at 19:30 to 20:00 pm, and reaches another strong noisy time at 21:00 to 21:30 pm. After 21:30 pm, the intensity goes down slowly. We also observed seismic signals that were generated by the interaction of speed-control hump cars and ground. By taking the envelope and smooth operations, we observe different characteristics for different car speeds, which suggests that seismic monitoring approaches can be used for speed measurement of cars. This kind of small seismic network running in a real time fashion, would greatly help understanding of the sources of ambient noise at high frequency bands in interested areas. Analysis of a long-term observed dataset, and real time illustration will help to strengthen campus security and high-precision laboratory deployments, and also contribute to research atmosphere in earthquake science.

Lihui Wu, Dun Wang, Ziguang Lei, Jing Fu, Shuai Min, Xianbing Xu, Sarina Bao. Campus Vibration in Nanwangshan Campus, China University of Geosciences at Wuhan Monitored by Short-Period Seismometers. Journal of Earth Science, 2020, 31(5): 950-956. doi: 10.1007/s12583-020-1332-8
Citation: Lihui Wu, Dun Wang, Ziguang Lei, Jing Fu, Shuai Min, Xianbing Xu, Sarina Bao. Campus Vibration in Nanwangshan Campus, China University of Geosciences at Wuhan Monitored by Short-Period Seismometers. Journal of Earth Science, 2020, 31(5): 950-956. doi: 10.1007/s12583-020-1332-8
  • Ambient noise observation is important in pure and applied geophysics, as it offers better understanding of earth structures, detailed geodynamic environments, and environment changes (e.g., ice quakes) as well.

    Continuous ambient noise studies in seismology have greatly advanced our understanding of detailed structure in tectonically active areas. Studies concerning velocity changes before and after large earthquakes (e.g., the 2011 M9 Tohoku, Japan earthquake) using ambient noise show clear evidence for the significant decrease of the velocity reduction in the uppermost shallow crust (Nakata and Snieder, 2011). Vibrations generated ambient noise were used to estimate travel times of surface wave velocities, so as to image shallow crust structures (Qin et al., 2018; Zou et al., 2018; Diaz et al., 2017; Manea et al., 2016; Behm et al., 2014; Nakata et al., 2011).

    There are other studies concerning non-tectonic vibration sources that are caused by human activities such as footfall-induced and traffic vibrations, which can be generated by human activities, traffic and subway activities (Diaz et al., 2017). In recent years, there has been an increasing trend towards vibration produced by human activities due to many human induced seismic signals and public concerns.

    For example, two seismic sensors were displayed on a road shoulder to monitor the seismic signals that were generated by moving vehicles (Wang et al., 2013). They applied a generalized cross-correlation approach to estimate the time delay of arrived seismic signals, and then estimated the vehicle speeds with errors of around 20%. A much denser seismic network was deployed at Long Beach of California for probing the subsurface structure, however, also catching many traffic signals (Riahi and Gerstoft, 2015). By calculating seismic noise power in time series, they can image the traffic sources such as Metro trains, aircrafts, and vehicles. Since very dense seismic sensors (100 m spacing) were used, a simple plot of the recorded seismic signals can show the vehicles speed in a very good resolution.

    Further, vibration observation has been extended to human activity detection and monitoring purposes. A vibration monitoring system based on sensors was proposed for detecting walking and running persons in semi-real time (Gabriel and Anghelescu, 2015). Similarly, a vibration monitoring system was applied to sense footsteps of individuals in rooms. Therefore, the resident activities and their social interactions can be inferred. Even with multi-people vibration sources, their activities can be detected in a reasonable resolution (Li et al., 2019).

    In this work, we monitor the campus vibration using short-period seismometers in and around the China University of Geosciences at Wuhan (CUG), China. The main purpose is to monitor the ambient noise, to investigate the vibration intensities, and understand the noise characteristics for better understanding of campus vibration environment and high-precision instrument deployments.

  • The Nanwang Mountain area, where the CUG West Campus sets down, is located at the northern margin of the Yangtze Block, to the south of the Triassic E-W-striking Dabieshan organic belt. The Nanwang Mountain extends eastwardly to the Yujia Mountain. They contact with each other by a top-to-the-SW reverse fault (Chen et al., 2014; Cai and Guan, 1979).

    Both the Nawang and Yujia mountains are composed mainly of the Middle Silurian Fentou Formation and the Upper Devonian Wutong Formation (Fig. 1). The Fentou Formation mainly consists of pebbly quartz sandstone, mudstone and silty mudstone. The Wutong Formation is composed mainly of quartz conglomerate and quartz sandstone, which contacts with the underlying Fentou Formation along a disconformity. Both the Fentou and Wutong formations dip to north at 45–58 degree.

    Figure 1.  Geological map in and around Nanwangshan Campus, China University of Geosciences. Black triangles indicate the locations of three seismic stations deployed in this study P1g, Lower Permian Gufeng Formation; D3w. Upper Devonian Wutong Formation; S2-3f. Middle–Upper Silurian Fentou Formation.

    An E-W-striking fault occurs along the northern margin of the Nanwang and Yujia moutains, which led to siliceous rocks of the Early Permian Gufeng Formation lies on the Wutong Formation. This fault is almost covered by the Quaternary alluvial sediments and residual sediments. Steep bedding and asymmetric folds indicate that the E-W-striking fault dips to north and had suffered top-to-the-south thrusting. The thrusting possibly occurred during Middle Triassic, which is related to the continent-continent collision between the South China Block and the North China Block.

    Three EPS-2 type short-period seismometers with a nature frequency of 5 Hz are used in this study. The EPS-2 seismometer is 3 components, portable, light, and with battery, and now has been widely used in geophysical prospecting, micro-seismicity monitoring, and ambient noise studies (Fig. 2). Since it is equipped with 3 components, it can record the intensity and direction of ground motion in horizontal and vertical dimensions.

    Figure 2.  Apparatus structure of the EPS-2 seismometer (left) and its instrument responses (right).

    The deployment period is from April 9 to May 9, 2018. To keep the balance between the instrument performance and easy maintenance, we set the sampling rate at 250 Hz, which is adequate for monitoring campus ambient noise at frequency range of 0.1 to 100 Hz.

    Since we aim to investigate the ambient noise level and its characteristics, we deploy one seismometer in the most crowded place, a garden behind the Wutan building in the West Campus of CUG (Fig. 1). This site is located at the center among student dormitories and teaching buildings, therefore is representative of human induced vibrations caused by student activities. The other two seismometers are deployed at the Nanwanshan Mountain (station NWS) and Yujiashan Mountain (station DQ) for comparison (Fig. 1).

  • To verify the quality of the recorded data and correct orientation of the seismometers, we select a tele-seismic event to check the recorded waveforms and the corresponding particle motions. On May 4, 2018, a Mw 6.9 earthquake occurred in Hawaii (Lon.: -154.71, Lat.: 19.12, Depth: 12.0 km). The seismometers at CUG Campus are ~81.27 degrees from the epicenter, and with an azimuth of 299 degrees.

    The recorded waveforms show clear P onset (Fig. 3), although the S phases are not well recorded in all of the three recordings (Fig. 4). We attribute it to the different site effects and/or conformity of the sensors.

    Figure 3.  The locations of the May 4, 2018 Mw 6.9 Hawaii earthquake and the seismic stations at Enshi, Wuhan City, and Nanwangshan Campus (station WT) of CUG. The onsets of the P waves recorded at the three seismic stations are marked by red lines. Focal mechanism data is from the Global CMT (www.globalcmt.org).

    Figure 4.  Seismic waveforms recorded at three stations inside of the Nanwangshan Campus of CUG, station Wuhan (WHA), and station Enshi (ENH). The waveforms are aligned at P arrivals with red line. The blue line indicates the S arrival at station DQ inside of the campus.

    We calculate the particle motions in three windows that cover the pre-P-, P-, and S- waveforms. The window length is 3 s that cover the onsets of the P-/S- phases. The particle motion of the noise before the P wave show random movement, whereas the motions for the P- and S- phases are predominantly NE and NW directions that are consistent with the particle motions predicted by wave propagation theories. This observation validates the correct orientations of the seismometers.

  • As the seismometers record the continuous ground velocities of the sites, we take the following procedures before further analysis: (1) Convert the raw recording into SAC files using the software offered by the Chongqing Geological instrument factory; (2) remove the instrument responses using software SAC and instrument response shown in Fig. 2; (3) convert the timing from GMT time to Beijing time for further analysis and interpretation; (4) cut and sort the continuous data by day (Beijing Time).

    Ambient noise consists mainly surface waves, and comes from many resources including human activities, winds, ocean waves, and other atmospheric phenomena (Groos and Ritter, 2009). There are mainly two types of noises. One is in the frequency band of below 1 Hz that is closely related to water waves, and the other is at high frequency band (over 1 Hz) that is more related to human activities, wind, and other atmospheric phenomena. The instrument response of the seismometer EPS-2 is flat over 1 Hz. Therefore, it is capable of recording the interested ambient noise at frequency over 1 Hz.

    We calculate the spectrums of ambient noise recorded at the time periods with less human activity (~03:00) and with strong human activity (~10:00) on April 9, 2018 and find that the spectrums at 8–15 Hz show an obvious peak at human active time period, whereas they are not clear in the quiet time period (Fig. 5). It is consistent with the analysis of human induced seismic signals recorded by broadband seismometers, which suggested that vibrations caused by human activity was in the frequency band of over 1 Hz (Groos and Ritter, 2009). Therefore, we choose the frequency band of 8–20 Hz as the characteristic frequency bands for analyzing campus ambient noise.

    Figure 5.  Frequency spectrums of the observed seismic waveforms recorded at station WT at quite (left) and noisy (right) time periods.

    In order to better characterize the intensity of ambient noise and its fluctuation with time, we filter the daily continuous data at above mentioned frequency band, and stack the absolute amplitudes in the time period of April 9 to April 17, 2018 (Fig. 6).

    Figure 6.  Daily stacked absolute amplitudes of the ground vibrations recorded at the three seismic stations from April 9 to May 9 of 2018. The high amplitudes spikes are caused by ground shakings that are very close to the sensors.

    Apparently, one could observe that the noise levels are low from 1:00 to 05:00 at three sites. After 06:00, the amplitudes of the noises increase, with peak amplitude 2–3 times larger than those at noon.

    There are several peaks clearly observed at the vertical component for the station WT. The first peak ranges from 05:00–6:00, at a level of 1×10-6 m/s, which may indicate the morning exercises of the students. The ambient noise gradually becomes larger in the morning, and reaches its maximum level at around 12:00–13:00 with average velocity of 2×10-6–3×10-6 m/s. There is another clear peak that appears at 19:00, which might be attributed to the students moving to teaching building at night. This interpretation has been further supported by a significant drop of the noise amplitude at 20:00 which is usually the time period for undergraduate students attending evening self-study in the class, and a sharp increase from 20:30 to 21:30 during which most students leave classrooms to take exercises, go to dormitories or do other activities (Fig. 6). We compare the intensity of the ambient noise recorded at station WT with the daily schedule of undergraduate students at Nanwangshan Campus, Wuhan, find a close correlation between intensities of student activity and ambient noise. The amplitude fluctuation of the ambient noise at station WT is likely modulated by student activity at the west part of the Nanwangshan Campus.

    Similar phenomena are observed at station DQ (Fig. 6). The quietest time at site DQ is 02:00–05:00, and there is a clear peak amplitude at 05:30–06:00 in the morning. The amplitude then gowns up, and reaches maximum at 12:00, and keep noisy until 20:00. After 20:00, the amplitude goes down gradually. Since the station DQ is far from the student activity areas such as teaching buildings and student dormitories, we don't observe a clear correlation between intensities of student activities and the ambient noise. The station DQ is ~50 m from the Lumo Road, which is a main street at local. We attribute the varied intensity of the ambient noise to the traffic activities at local area. The noise fluctuation curve reflects the traffic situations at Lumo Road, which might be statistically important for understanding vehicle flow with time.

    Ambient noise at station NWS shows very low value of the ground vibration, 6–10 times smaller than those at station WT on average (Fig. 6). The average value of the ground velocity is ~1×10-7–2×10-7 m/s for the vertical component. They didn't show strong periodicity compared to those at station WT. Although the noise amplitudes don't show close correlation to the schedule of student activity, the peak ground vibration is at 11:00–13:00, consistent with those at stations WT and DQ, suggesting that the vibration sources are probably modeled human activities (factory machines and/or vehicles) in the around area. The noise level at station NWS is similar to those of obtained from a broadband seismic observation conducted at a seismological observatory inside of the campus of Wuhan University, which show a vertical velocity of ~1×10-7 m/s on average (Wulin Liao, Personal communications).

    Although we observe that the ambient vibrations are most likely caused by student activities, especially in the area around the station WT, some other resources may also strengthen the vibration intensity, such as car activities. We knowledge that separating those signals remains challenging. Through machine or deep learning, may there can come up to new approaches that can resolve this issue in the future. For now, recognizing the seismic signals that are generated by car movements and understanding the generation process are more practical.

    Here we conduct a seismic experiment that observes seismic signals generated by controlled moving cars. At the flat ground, we don't observe characteristic seismic signals that are generated by the movements of cars. The corresponding seismograms show slightly increased intensity of ambient noise depending on the distances of the cars to our observation site. But when cars go through a speed-control hump, they can generate strong seismic signals that are observed by seismometers (Fig. 7). This indicates that the traffic noises are positively related to the roughness of the nearby roads. In some shaking sensitive environments, we may need to flatten the roads around and removing speed-control humps for the purpose of reducing traffic noise.

    Figure 7.  Seismic waveforms that are generated when moving vehicles hit speed-control humps with different speeds. By measuring the delayed pulsed that are generated by the interactions among front-/rear-wheels, speed-control hump, and the ground, we can estimate the car speeds without visible inspections.

    By comparing the seismic signals generated by cars passing speed-control humps with different speeds, we observe characteristic features of the signals that have potential to evaluate car speeds. The pulses shown in the observed waveforms are generated by the interactions among front-/rear-wheels, speed-control hump, and the ground. By measuring the time interval between pulses, we calculate the car speeds as 9.2, 16, 26, and 35 km/h, when the corresponding car speeds are 10, 20, 30, and 40 km/h (showing in the instrument cluster). Considering the uncertainty in the instrument cluster and measurements of the time intervals of the pulses, the average deviation of the estimated car speeds is no larger than 10%, our estimated car speeds are comparable to regular methods for measuring car speeds (Mao et al., 2012; Wu et al., 2009; Lin et al., 2008; Yamazaki et al., 2008; Pelegri et al., 2002). Easy deployment and maintaining make it have potential to monitor car speeds in an economical way and without infringing on the privacy of others.

  • In this study, we deploy three portable digital seismometers in and around the Nanwangshan Campus of CUG and continuously record and observe seismic waveforms and ambient noise from April 9 to May 9, 2018. We find clear peak ground vibrations at several time periods (05:00–6:00; 12:00–13:00; 19:00 and 20:30–21:30) and low level of ground shaking concentrated on the time from 01:00 to 05:00, by analyzing the daily continuous data at the frequency band of 8–100 Hz. The peak ground vibrations of ambient noise are consistent with the student flowrate of daily regular movement patterns guided by the timetables of CUG. Meanwhile, the results provide the evidence that the intensity of the ambient noise is positively related to those of the student activity and/or traffic noise on campus. Therefore, portable digital seismometers can be used to monitor intensity of student activities. From this point of view, dense observations of those signals inside of university campus would generate a detailed spatio-temporal distribution of student activities. Such concise pictures will definitely benefit campus transportation managements and better emergency response.

    We demonstrate that seismometer-based campus vibration monitoring can offer better and detailed understanding of student activities and traffic noise in time and space, therefore benefit campus management and security system.

    Also, long term observation of such small seismic stations could offer detailed shaking map, and amount of vibration intensity in time. Understanding of ground vibrations caused by human activities and traffic noise will help better locations of high-resolution scientific apparatuses such as high-resolution electron microscope (Geng et al., 2014; Meng et al., 2012; Li et al., 2008). An obvious example is the Metro Line 4 at Beijing, which is 100 m away from a research center of Peking University. When the Metro train arrived, high-resolution electron microscopes lose their accuracy to image the atomic structures as they are supposed to do (Ma et al., 2011). As scientific instruments are going to push bottom limit at the CUG, the environment vibration pays a more and more important role in refining the resolution beside the physical Architecture. Detailed vibration map will help research institutions choosing sites of buildings for high-resolution instrumentations.

    Future research will extend research scope to urban areas and focus on analyzing the power spectral density of the ambient noise that are recorded by seismic stations in and around cities. The systematic study of the ambient noise in and around urban area will help seismic observations in site selection, data interpretation, and de-noising operations.

  • Seismic data used in this work are downloaded from the Chinese seismic data center (http://www.ceic.ac.cn/). Focal mechanisms are downloaded from the USGS (https://earthquake.usgs.gov/, last accessed January 18, 2020) and GCMT (www.globalcmt.org, last accessed January 18, 2020). All other data used in the paper came from published sources in the references or were collected by the authors. All the figures were created using the Generic Mapping Tools (GMT) of Wessel and Smith (Wessel and Smith, 1991).

  • This work was supported by the National Key R & D Program of China (No. 2018YFC0603500), Programme on Global Change and Air-Sea Interaction (No. GASI-GEOGE-02), and NSFC (Nos. 41474050, 41874062). This paper is also one of the outcomes of the research projects (No. Q20203004), analysis of campus ambient noise monitored by short-seismometers funded by Scientific Research Foundation of the Education Department of Hubei Province, China. The final publication is available at Springer via https://doi.org/10.1007/s12583-020-1332-8.

Reference (24)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return