Application of Clustering Method in Different Geophysical Parameters for Researching Subsurface Environment

• Autorzy: Le Cuong Van Anh , Nguyen Ngan Nhat Kim , Nguyen Thuan Van

Identyfikatory

DOI

10.29227/IM-2022-02-05

Warianty tytułu

PL

Zastosowanie metody klastrowania w różnych parametrach geofizycznych do badania środowiska podpowierzchniowego

• Języki publikacji: EN

Abstrakty

EN

Safety of construction needs knowledge of physical parameters as stiffness or porosity of the subsurface environment. Combination of different geophysical methods such as electrical resistivity imaging and multichannel analysis of surface waves can provide distributions of resistivity and shear velocity which are responsible for the underground physical parameters. Their joint interpretation can solve individual problems of none-uniqueness of the solutions when expressing two inversion results to describe environment characteristics. In our work, the k-means clustering method can categorize the two parameters into specific zones that can help to interpret the geophysical data effectively. Our workflow consists of two stages in which two independent geophysical data are inverted and the k-means clustering is applied to the two results for achieving the specified groups. The collocated geophysical data are measured in District 9, Ho Chi Minh City, Vietnam. Matching with the geology drillhole information, the joint results generally present layered medium with the upper zone having smaller resistivity and shear velocity values and the bottom zone of stronger stiffness.

PL

Bezpieczeństwo konstrukcji wymaga znajomości parametrów fizycznych, takich jak sztywność czy porowatość środowiska podpowierzchniowego. Połączenie różnych metod geofizycznych, takich jak obrazowanie rezystywności elektrycznej i wielokanałowa analiza fal powierzchniowych, może dostarczyć rozkłady rezystywności i prędkości ścinania, które są odpowiedzialne za parametry fizyczne podziemnych warstw. Ich wspólna interpretacja może rozwiązać indywidualne problemy niejednoznaczności rozwiązań przy wyrażaniu dwóch wyników inwersji do opisu cech środowiska. W naszej pracy metoda grupowania k-średnich może podzielić dwa parametry na określone strefy, co może pomóc w skutecznej interpretacji danych geofizycznych. Nasz przepływ pracy składa się z dwóch etapów, w których dwa niezależne dane geofizyczne są odwracane, a grupowanie k-średnich jest stosowane do dwóch wyników w celu uzyskania określonych grup. Zebrane dane geofizyczne są mierzone w Dystrykcie 9, Ho Chi Minh City, Wietnam. Dopasowując się do informacji uzyskanych z odwiertów geologicznych, wyniki połączeń ogólnie przedstawiają ośrodek warstwowy, w którym górna strefa ma mniejsze wartości rezystywności i prędkości ścinania, a dolna strefa ma większą sztywność.

Słowa kluczowe

EN

Electrical Resistivity Imaging; MASW; K-means Clustering

PL

obrazowanie oporności elektrycznej; MASW; grupowanie K-średnich

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2022
• Tom: no. 2
• Strony: 39--47
• Opis fizyczny: Bibliogr. 41 poz., rys., wykr., zdj.

Twórcy

autor

Le Cuong Van Anh

• University of Science, Ho Chi Minh City, Vietnam
• Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

autor

Nguyen Ngan Nhat Kim

• University of Science, Ho Chi Minh City, Vietnam
• Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

autor

Nguyen Thuan Van

• University of Science, Ho Chi Minh City, Vietnam
• Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

Bibliografia

• 1. AGI. 2022. MiniSting [Online]. Available: https://www.agiusa.com/ministing 2022].
• 2. AKCA, I. 2016. ELRIS2D: A MATLAB Package for the 2D Inversion of DC Resistivity/IP Data. Acta Geophysica, 64, 443-462.
• 3. CARDARELLI, E., CERCATO, M. & DE DONNO, G. 2014. Characterization of an earth-filled dam through the combined use of electrical resistivity tomography, P-and SH-wave seismic tomography and surface wave data. Journal of Applied Geophysics, 106, 87-95.
• 4. COSTALL, A., HARRIS, B. & PIGOIS, J. 2018. Electrical resistivity imaging and the saline water interface in high-quality coastal aquifers. Surveys in geophysics, 39, 753-816.
• 5. DAVIES, D. L. & BOULDIN, D. W. 1979. A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence.
• 6. DI GIUSEPPE, M. G., TROIANO, A., TROISE, C. & DE NATALE, G. 2014. k-Means clustering as tool for multivariate geophysical data analysis. An application to shallow fault zone imaging. Journal of Applied Geophysics, 101, 108-115.
• 7. EDWARDS, L. 1977. A modified pseudosection for resistivity and IP. Geophysics, 42, 1020-1036.
• 8. GALLARDO, L. A. & MEJU, M. A. 2004. Joint two-dimensional DC resistivity and seismic travel time inversion with cross-gradients constraints. Journal of Geophysical Research: Solid Earth, 109.
• 9. GALLARDO, L. A. & MEJU, M. A. 2011. STRUCTURE‐COUPLED MULTIPHYSICS IMAGING IN GEOPHYSICAL SCIENCES. Reviews of Geophysics.
• 10. GHANATI, R., AZADI, Y. & FAKHIMI, R. 2020. RESIP2DMODE: A MATLAB-Based 2D Resistivity and Induced Polarization Forward Modeling Software. Iranian Journal of Geophysics, 60-78.
• 11. HABER, E. & OLDENBURG, D. 1997. Joint inversion: a structural approach. Inverse problems, 13, 63.
• 12. HAMZAH, U., SAMSUDIN, A. R. & MALIM, E. P. 2007. Groundwater investigation in Kuala Selangor using vertical electrical sounding (VES) surveys. Environmental geology, 51, 1349-1359.
• 13. LE, C. V. A. 2017. Cooperative Inversion of Magnetotelluric and Seismic Data. Doctor of Philosophy, Curtin University.
• 14. LE, C. V. A., DUONG, M. B. & KIEU, T. D. 2020. High–Resolution Seismic Reflection Survey of Holocene Sediment Distribution at Thi Vai River, Ho Chi Minh City, Vietnam. Lecture Notes in Civil Engineering. Springer.
• 15. LE, C. V. A., HARRIS, B. D. & PETHICK, A. M. 2019. New perspectives on Solid Earth Geology from Seismic Texture to Cooperative Inversion. Scientific Reports, 9, 14737.
• 16. LE, C. V. A., HARRIS, B. D., PETHICK, A. M., TAKAM TAKOUGANG, E. M. & HOWE, B. 2016a. Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data. Surveys in Geophysics, 37, 845-896.
• 17. LE, C. V. A., HARRIS, B. D., PETHICK, A. M., TAKOUGANG, E. M. T. & HOWE, B. 2016b. Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data. Surveys in Geophysics, 37, 845-896.
• 18. LINDSTEN, F., OHLSSON, H. & LJUNG, L. 2011. Just relax and come clustering!: A convexification of k-means clustering.
• 19. LOKE, M. & BARKER, R. 1995. Least-squares deconvolution of apparent resistivity pseudosections. Geophysics, 60, 1682-1690.
• 20. LOKE, M. H. 1999. Electrical imaging surveys for environmental and engineering studies. A practical guide to 2-D and 3-D surveys.
• 21. MAHAJAN, A., CHANDRA, S., SARMA, V. & ARORA, B. 2015. Multichannel analysis of surface waves and high-resolution electrical resistivity tomography in detection of subsurface features in northwest Himalaya. Current Science, 2230-2239.
• 22. MATHWORKS. 2014. MATLAB [Online]. Available: http://au.mathworks.com/help/stats/k-means-clustering.html 2014].
• 23. MATLAB. 2019. silhouette [Online]. Available: https://www.mathworks.com/help/stats/silhouette.html 2022].
• 24. MEJU, M. A. 1994. Geophysical data analysis: Understanding inverse problem theory and practice, Society of Exploration Geophysicists.
• 25. MOORKAMP, M. 2017. Integrating Electromagnetic Data with Other Geophysical Observations for Enhanced Imaging of the Earth: A Tutorial and Review. Surveys in Geophysics, 1-28.
• 26. MUFTI, I. R. 1976. Finite-difference resistivity modeling for arbitraily shaped two-dimensional structures. Geophyscs, 41, 62-78.
• 27. NGUYEN, N. K. N., NGUYEN, V. T., VO, M. K., DINH, Q. T. & NGUYEN, Q. D. 2022. Application of electrical imaging and multichannel analysis of surface waves methods to survey the structure foundation at the Districts 2 and 9 of Ho Chi Minh City. Science and Technology Development Journal-Natural Sciences, 6, 1801-1810.
• 28. NGUYEN, Q. T. 2016. The main causes of land subsidence in Ho Chi Minh City. Procedia engineering, 142, 334-341.
• 29. NOVOTNY, O. 1999. Seismic surface waves. In: INSTITUTO DE FISICA, I. D. G. (ed.). Bahia, Salvador.
• 30. PARK, B., KIM, J., LEE, J., KANG, M.-S. & AN, Y.-K. 2018. Underground object classification for urban roads using instantaneous phase analysis of ground-penetrating radar (GPR) data. Remote Sensing, 10, 1417.
• 31. PARK SEISMIC LLC. 2022. ParkSeis (c) (PS) [Online]. Available: https://www.parkseismic.com/psoverview/ 2022].
• 32. SAUVIN, G., BAZIN, S., VANNESTE, M., LECOMTE, I. & PFAFFHUBER, A. Towards joint inversion/interpretation for landslide-prone areas in Norway-integrating geophysics and geotechnique. Near Surface 2011-17th EAGE European Meeting of Environmental and Engineering Geophysics, 2011. European Association of Geoscientists & Engineers, cp-253-00052.
• 33. SCHWAB, F. & KNOPOFF, L. 1972. Fast surface wave and free mode computations. Methods in computational physics: advances in research and applications. Elsevier.
• 34. SEISTRONIX. RAS-24 Exploration Seismograph [Online]. Available: http://www.seistronix.com/ras_g.htm 2022].
• 35. SHARMA, P. 1995. Environmental and Engineering Geophysics.
• 36. SHEN, J., CHANG, S. I., LEE, E. S., DENG, Y. & BROWN, S. J. 2005. Determination of cluster number in clustering microarray data. Applied Mathematics and Computation, 169, 1172-1185.
• 37. SIKANDAR, P., BAKHSH, A., ARSHAD, M. & RANA, T. 2010. The use of vertical electrical sounding resistivity method for the location of low salinity groundwater for irrigation in Chaj and Rachna Doabs. Environmental Earth Sciences, 60, 1113-1129.
• 38. TELFORD, W. M., TELFORD, W., GELDART, L. & SHERIFF, R. E. 1990. Applied geophysics, Cambridge university press.
• 39. WISÉN, R. & CHRISTIANSEN, A. V. 2005. Laterally and mutually constrained inversion of surface wave seismic data and resistivity data. Journal of Environmental and Engineering Geophysics, 10, 251-262.
• 40. XIA, J., MILLER, R. D. & PARK, C. B. 1999. Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves. Geophysics, 64, 691-700.
• 41. ZHAO, T., JAYARAM, V., ROY, A. & MARFURT, K. J. 2015. A comparison of classification techniques for seismic facies recognition. Interpretation.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)