Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
first last
cannonical link button


Acta Geophysica

Tytuł artykułu

Noise Reduction in Radon Monitoring Data Using Kalman Filter and Application of Results in Earthquake Precursory Process Research

Autorzy Namvaran, M.  Negarestni, A. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN Monitoring the concentration of radon gas is an established method for geophysical analyses and research, particularly in earthquake studies. A continuous radon monitoring station was implemented in Jooshan hotspring, Kerman province, south east Iran. The location was carefully chosen as a widely reported earthquake-prone zone. A common issue during monitoring of radon gas concentration is the possibility of noise disturbance by different environmental and instrumental parameters. A systematic mathematical analysis aiming at reducing such noises from data is reported here; for the first time, the Kalman filter (KF) has been used for radon gas concentration monitoring. The filtering is incorporated based on several seismic parameters of the area under study. A novel anomaly defined as “radon concentration spike crossing” is also introduced and successfully used in the study. Furthermore, for the first time, a mathematical pattern of a relationship between the radius of potential precursory phenomena and the distance between epicenter and the monitoring station is reported and statistically analyzed.
Słowa kluczowe
PL anomalia radonu   filtr Kalmana   redukcja szumów   aktywność sejsmiczna  
EN radon anomaly   Kalman filter   noise reduction   effective precursory ratio   seismic activity  
Wydawca Instytut Geofizyki PAN
Czasopismo Acta Geophysica
Rocznik 2015
Tom Vol. 63, no. 2
Strony 329--351
Opis fizyczny Bibliogr. 32 poz., rys., tab., wykr.
autor Namvaran, M.
autor Negarestni, A.
[1] Ali Yalım, H., A. Sandıkcıoğlu, O. Ertuğrul, and A. Yıldız (2012), Determination of the relationship between radon anomalies and earthquakes in well waters on the Akşehir-Simav Fault System in Afyonkarahisar province, Turkey, J. Environ. Radioactiv. 110, 7-12, DOI: 10.1016/j.jenvrad.2012.01.015.
[2] Belouchrani, A., K. Abed-Meraim, J.-F. Cardoso, and E. Moulines (1997), A blind source separation technique using second-order statistics, IEEE Trans. Signal Process. 45, 2, 434-444, DOI: 10.1109/78.554307.
[3] Berberian, M., J.A. Jackson, M. Ghorashi, and M.H. Kadjar (1984), Field and teleseismic observations of the 1981 Golbaf–Sirch earthquakes in SE Iran, Geophys. J. Roy. Astr. Soc. 77, 3, 809-838, DOI: 10.1111/j.1365-246X.1984.tb02223.x.
[4] Berberian, M., J.A. Jackson, E. Fielding, B.E. Parsons, K. Priestley, M. Qorashi, M. Talebian, R. Walker, T.J. Wright, and C. Baker (2001), The 1998 March 14 Fandoqa earthquake (Mw 6.6) in Kerman province, southeast Iran: Re‐rupture of the 1981 Sirch earthquake fault, triggering of slip on adjacent thrusts and the active tectonics of the Gowk fault zone, Geophys. J. Int. 146, 2, 371-398, DOI: 10.1046/j.1365-246x.2001.01459.x.
[5] Brabec, M., and K. Jílek (2007), State-space dynamic model for estimation of radon entry rate, based on Kalman filtering, J. Environ. Radioactiv. 98, 3, 285-297, DOI: 10.1016/j.jenvrad.2007.05.006.
[6] Brailean, J.C., R.P. Kleihorst, S. Efstratiadis, A.K. Katsaggelos, and R.L. Lagendijk (1995), Noise reduction filters for dynamic image sequences: a review, Proc. IEEE 83, 9, 1272-1292, DOI: 10.1109/5.406412.
[7] Cichocki, A., and S.-I. Amari (2002), Adaptive Blind Signal and Image Processing. Learning Algorithms and Applications, John Wiley & Sons, Chichester.
[8] Dobrovolsky, I.P., S.I. Zubkov, and V.I. Miachkin (1979), Estimation of the size of earthquake preparation zones, Pure Appl. Geophys. 117, 5, 1025-1044, DOI: 10.1007/BF00876083.
[9] Evensen, G. (2003), The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam. 53, 4, 343-367, DOI: 10.1007/s10236-003-0036-9.
[10] Finkelstein, M., S. Brenner, L. Eppelbaum, and E. Ne’Eman (1998), Identification of anomalous radon concentrations due to geodynamic processes by elimination of Rn variations caused by other factors, Geophys. J. Int. 133, 2, 407-412, DOI: 10.1046/j.1365-246X.1998.00502.x.
[11] Fujimoto, M., and Y.Ariki (2000), Noisy speech recognition using noise reduction method based on Kalman filter. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP’2000, 5-9 June 2000, Istanbul, Turkey, Vol. 3, 1727-1730, DOI: 10.1109/icassp. 2000.862085.
[12] Hashemi, S.M., A. Negarestani, M. Namvaran, and S.M. Musavi Nasab (2013), An analytical algorithm for designing radon monitoring network to predict the location and magnitude of earthquakes, J. Radioanal. Nucl. Chem. 295, 3, 2249-2262, DOI: 10.1007/s10967-012-2310-0.
[13] Hauksson, E. (1981), Radon content of groundwater as an earthquake precursor: Evaluation of worldwide data and physical basis, J. Geophys. Res. 86, B10, 9397-9410, DOI: 10.1029/JB086iB10p09397.
[14] Hyvärinen, A., and E. Oja (2000), Independent component analysis: algorithms and applications, Neural Networks 13, 4-5, 411-430, DOI: 10.1016/S0893-6080(00)00026-5.
[15] Kleinbauer, R. (2004), Kalman filtering implementation with Matlab, Study Report, Universität Stuttgart, Institute of Geodesy.
[16] Kownacki, C. (2011), Optimization approach to adapt Kalman filters for the realtime application of accelerometer and gyroscope signals’ filtering, Digit. Signal Process. 21, 1, 131-140, DOI: 10.1016/j.dsp.2010.09.001.
[17] Kuo, T., K. Fan, H. Kuochen, Y. Han, H. Chu, and Y. Lee (2006), Anomalous decrease in groundwater radon before the Taiwan M6.8 Chengkung earthquake, J. Environ. Radioactiv. 88, 1, 101-106, DOI: 10.1016/j.jenvrad.2006.01.005.
[18] Lee, T.-W., M.S. Lewicki, M. Girolami, and T.J. Sejnowski (1999), Blind source separation of more sources than mixtures using overcomplete representations, IEEE Signal Process. Lett. 6, 4, 87-90, DOI: 10.1109/97.752062.
[19] Leśniak, A., T. Danek, and M. Wojdyła (2009), Application of Kalman filter to noise reduction in multichannel data, Schedae Informaticae 17, 18, 63-73, DOI:10.2478/v10149-010-0004-3.
[20] Negarestani, A., S. Setayeshi, M. Ghannadi-Maragheh, and B. Akashe (2002), Layered neural networks based analysis of radon concentration and environmental parameters in earthquake prediction, J. Environ. Radioactiv. 62, 3, 225-233, DOI: 10.1016/S0265-931X(01)00165-5.
[21] Ramola, R.C. (2010), Relation between spring water radon anomalies and seismic activity in Garhwal Himalaya, Acta Geophys. 58, 5, 814-827, DOI:10.2478/s11600-009-0047-0.
[22] Richon, P., F. Perrier, J.-C. Sabroux, M. Trique, C. Ferry, V. Voisin, and E. Pili (2004), Spatial and time variations of radon-222 concentration in the atmosphere of a dead-end horizontal tunnel, J. Environ. Radioactiv. 78, 2, 179-198, DOI:10.1016/j.jenvrad.2004.05.001.
[23] Roesser, R.P. (1975), A discrete state-space model for linear image processing, IEEE Trans. Automat. Contr. 20, 1, 1-10, DOI: 10.1109/tac.1975.1100844.
[24] Seyis, C., S. İnan, and T. Streil (2010), Ground and indoor radon measurements in a geothermal area, Acta Geophys. 58, 5, 939-946, DOI: 10.2478/s11600-010-0012-y.
[25] Simon, D. (2001), Kalman filtering, Embedded Sys. Program. 14, 6, 72-79.
[26] Simon, D., and T.L. Chia (2002), Kalman filtering with state equality constraints, IEEE Trans. Aero. Elec. Sys. 38, 1, 128-136, DOI: 10.1109/7.993234.
[27] Spitzer, F. (2001), Principles of Random Walk, 2nd ed., Graduate Texts in Mathematics, Vol. 34, Springer, New York.
[28] Tsunomori, F., and T. Kuo (2010), A mechanism for radon decline prior to the 1978 Izu-Oshima-Kinkai earthquake in Japan, Radiat. Meas. 45, 1, 139-142, DOI:10.1016/j.radmeas.2009.08.003.
[29] Wakita, H., G. Igarashi, and K. Notsu (1991), An anomalous radon decrease in groundwater prior to an M6.0 earthquake: A possible precursor?, Geophys. Res. Lett. 18, 4, 629-632, DOI: 10.1029/91GL00824.
[30] Zhang, K., and A. Hyvärinen (2011), A general linear non-Gaussian state-space model: Identifiability, identification, and applications. In: C.-N. Hsu and W.S. Lee (eds.), JMLR Workshop and Conference Proc., Asian Conf. on Machine Learning 2011, Tokyo, Japan, 113-128.
[31] Zibulevsky, M., and B.A. Pearlmutter (2001), Blind source separation by sparse decomposition in a signal dictionary, Neural Comput. 13, 4, 863-882, DOI:10.1162/089976601300014385.
[32] Zmazek, B., M. Živčić, L. Todorovski, S. Džeroski, J. Vaupotič, and I. Kobal (2005), Radon in soil gas: How to identify anomalies caused by earthquakes, Appl. Geochem. 20 6, 1106-1119, DOI: 10.1016/j.apgeochem. 2005.01.014.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-af105ea5-982f-4e4f-a424-dc8608c7ead4
DOI 10.2478/s11600-014-0218-5