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## The Magnitude Threshold for Detecting Recorded Earthquakes in Tehran's Accelerometer Networks | ||

فیزیک زمین و فضا | ||

مقاله 4، دوره 48، شماره 4، اسفند 1401، صفحه 47-54 اصل مقاله (1.66 M)
| ||

نوع مقاله: مقاله پژوهشی | ||

شناسه دیجیتال (DOI): 10.22059/jesphys.2023.332846.1007378 | ||

نویسندگان | ||

Mehrasa Masih^{1}؛ Hossein Kianimehr^{2}؛ Zaher Hossein Shomali^{*} ^{3}؛ Esmaeil Bayramnejad^{4}
| ||

^{1}Department of Seismology, Institute of Geophysics, University of Tehran, Tehran, Iran. E-mail: mehrasa.masih@ut.ac.ir | ||

^{2}Iranian Seismological Center, Kerman, Iran. E-mail: kianimehrhossein@gmail.com | ||

^{3}Corresponding Author, Department of Seismology, Institute of Geophysics, University of Tehran, Tehran, Iran. E-mail: shomali@ut.ac.ir | ||

^{4}Department of Seismology, Institute of Geophysics, University of Tehran, Tehran, Iran. E-mail: ebayram@ut.ac.ir | ||

چکیده | ||

Due to the population growth in metropolitan regions such as Tehran and the existence of the underground constructions, the importance of seismic investigation is evident to reduce damages caused by probable earthquakes. Accordingly, the precise detection of micro to medium earthquakes is effective tool for tracking fault dynamics in seismic cycles, as well as for earthquake prediction and seismic hazard assessment. In this study, the recorded ambient noise at Tehran Disaster Mitigation and Management Organization (TDMMO) as well as Road, Housing and Urban Development Research Center (BHRC) networks as an accelerometer network installed in Tehran city, have been used on the point of characterizing the noise spectrum for each station as a function of time for obtaining the detection threshold of these networks. Therefore, an indirect approach based on the signal-to-noise ratio (SNR) in the time domain, with parameterization in the frequency domain is applied. Based on SNR method, the source signature is simulated by a simple source model called a circular fault model. Thus, the signal is estimated via the Brune function as most common models for circular faults. While, to determine the noise, the real data of 13 accelerometer stations of the TDMMO and seven joint stations with the BHRC are used. In this respect, the Power Spectral Density (PSD) of noise is calculated using PQLX software in the frequency domain and then is transferred to the time domain by the Parsville theorem. Eventually, the SNR value is acquired for each station by dividing these two quantities. As a result, the minimum detectable magnitude in at least five stations with an SNR larger than 5 is 3.0 for S-waves and 3.3 for P-waves, which frequently occurs in the center of the network. Another finding of these studies is to analyze the effect of spatial variations of the noise on the detection ability. For this, a constant noise is allotted to all stations, lowest observed noise level, as a result of which, the smallest magnitude detectable is 1.7 for S-waves and 2.2 for P-waves. At last, the sensitivity of the detection capability to three fundamental parameters, including stress drop, focal depth and reduced time, which are assumed as constant values within the network, are investigated. In fact, these parameters are strongly affected by uncertainty and are not absolute values. Consequently, the impact of their changes was studied. In our case, it is implied that the variation in the stress drop has no effect on the detection threshold, but the focal depth and the reduced time are effectual. A 15 km variation in the focal depth, the detectable magnitude changes by 0.3 units, and by changing the reduced time from 0.015 s to 0.035 s, the detectable magnitude varies by 0.4 units in M _{w}. | ||

کلیدواژهها | ||

Detection threshold؛ PSD؛ PQLX software؛ SNR؛ Spectrum | ||

عنوان مقاله [English] | ||

The Magnitude Threshold for Detecting Recorded Earthquakes in Tehran's Accelerometer Networks | ||

نویسندگان [English] | ||

Mehrasa Masih^{1}؛ Hossein Kianimehr^{2}؛ Zaher Hossein Shomali^{3}؛ Esmaeil Bayramnejad^{4} |
||

^{1}Department of Seismology, Institute of Geophysics, University of Tehran, Tehran, Iran. E-mail: mehrasa.masih@ut.ac.ir | ||

^{2}Iranian Seismological Center, Kerman, Iran. E-mail: kianimehrhossein@gmail.com | ||

^{3}Corresponding Author, Department of Seismology, Institute of Geophysics, University of Tehran, Tehran, Iran. E-mail: shomali@ut.ac.ir | ||

^{4}Department of Seismology, Institute of Geophysics, University of Tehran, Tehran, Iran. E-mail: ebayram@ut.ac.ir | ||

چکیده [English] | ||

Due to the population growth in metropolitan regions such as Tehran and the existence of the underground constructions, the importance of seismic investigation is evident to reduce damages caused by probable earthquakes. Accordingly, the precise detection of micro to medium earthquakes is effective tool for tracking fault dynamics in seismic cycles, as well as for earthquake prediction and seismic hazard assessment. In this study, the recorded ambient noise at Tehran Disaster Mitigation and Management Organization (TDMMO) as well as Road, Housing and Urban Development Research Center (BHRC) networks as an accelerometer network installed in Tehran city, have been used on the point of characterizing the noise spectrum for each station as a function of time for obtaining the detection threshold of these networks. Therefore, an indirect approach based on the signal-to-noise ratio (SNR) in the time domain, with parameterization in the frequency domain is applied. Based on SNR method, the source signature is simulated by a simple source model called a circular fault model. Thus, the signal is estimated via the Brune function as most common models for circular faults. While, to determine the noise, the real data of 13 accelerometer stations of the TDMMO and seven joint stations with the BHRC are used. In this respect, the Power Spectral Density (PSD) of noise is calculated using PQLX software in the frequency domain and then is transferred to the time domain by the Parsville theorem. Eventually, the SNR value is acquired for each station by dividing these two quantities. As a result, the minimum detectable magnitude in at least five stations with an SNR larger than 5 is 3.0 for S-waves and 3.3 for P-waves, which frequently occurs in the center of the network. Another finding of these studies is to analyze the effect of spatial variations of the noise on the detection ability. For this, a constant noise is allotted to all stations, lowest observed noise level, as a result of which, the smallest magnitude detectable is 1.7 for S-waves and 2.2 for P-waves. At last, the sensitivity of the detection capability to three fundamental parameters, including stress drop, focal depth and reduced time, which are assumed as constant values within the network, are investigated. In fact, these parameters are strongly affected by uncertainty and are not absolute values. Consequently, the impact of their changes was studied. In our case, it is implied that the variation in the stress drop has no effect on the detection threshold, but the focal depth and the reduced time are effectual. A 15 km variation in the focal depth, the detectable magnitude changes by 0.3 units, and by changing the reduced time from 0.015 s to 0.035 s, the detectable magnitude varies by 0.4 units in M _{w}. | ||

کلیدواژهها [English] | ||

Detection threshold, PSD, PQLX software, SNR, Spectrum | ||

مراجع | ||

Aki, K., & Richards, P.G. (1980). Quantitative Seismology: Theory and Methods. W. H. Freeman, San Francisco. Atkinson, G.M. (1993). Earthquake source spectra in eastern north America. Bobbio, A., Vassallo, M., & Festa, G. (2009). A local magnitude scale for southern Italy. Bulletin of the Seismological Society of Boore, D. M., & Boatwright, J. (1984). Average body-wave radiation coefficients. Brune, J. N. (1970). Tectonic stress and the spectra of seismic shear waves from earthquakes. Brune, J. N. (1971). Tectonic stress and the spectra of seismic shear waves from earthquakes. Ding, L. (2015). Stress drop and its uncertainty for earthquakes M 3.8-5.5 in central California and Oklahoma. Incorporated Research Institutions for Seismology. (2017). Software Downloads – PQLX, https://ds.iris.edu/ds/nodes/dmc/software/downloads/pqlx/. Malagnini, L., & Dreger, D.S. (2016). Generalized free-surface effect and random vibration theory: a new tool for computing moment magnitudes of small earthquakes using borehole data. Margheriti, L., & Zollo, A. (2010). High-resolution multi-disciplinary monitoring of active fault test-site areas in Italy, Final report S5-DPC-INGV Project, http://dcp-s5.rm.ingv.it/en/S5.html (last accessed October 2011), pp. 14. Memarian, H., Kamranzad, F., & Zare, M. (2020). Earthquake risk assessment for Tehran, Iran. Ringdal, F. (1975). On the estimation of seismic detection thresholds. Vassallo, M., Festa, G.F. & Bobbio, A. (2012). Seismic Ambient Noise Analysis in Southern Italy. Weidle, C., Wenzel, F., & Ismail-Zadeh, A. (2006). t Yazdani, A., Kowsari, M., & Amani, S. (2016). Development of a regional attenuation relationship for Alborz, Iran. Zafarani, H., Ghorbani-Tanha, A.K., Rahimian, M., & Noorza, A. (2008). Seismic response analysis of Milad tower in Tehran, Iran, under site-specific simulated ground motions. | ||

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