Abstract. The information on geological structure as well as on the degree of contamination and geometrical parameters of a pollutant in oil-contaminated areas is necessary for risk assessment, planning of oil products recovery and territory remediation. Geophysical methods are actively used for solving such problems. The work considers the site on the Volga River bank, where soils are contaminated with petroleum products. The aim of the work is to delineate the distribution area of petroleum products. In order to achieve the goal, the set of near-surface geophysical methods (vertical electric sounding, seismic survey) and gas geochemistry were implemented. The results of a new approach to characterization of contaminated sites by RGB-data synthesis have been demonstrated as one of the ways of data interpretation. The method is based on the generalization of the available materials by optically mixing of the data of three spatially distributed characteristics presented in the form of three channels – red, green, and blue – for the purpose of localizing the lenses of gravity-mobile and immobilized oil products. According to the results of the qualitative interpretation of geophysical information, the authors have built a scheme with the proposed contour of oil products distribution in the studied territory. The proposed method can be used for the delineation of oil spills along with the sufficient information obtained by geophysical or other methods (at least three) at the stage of determining the spread of contamination for the sites. This approach can speed up the interpretation process, as such maps overlaying sets the color distribution of different petrophysical characteristics of the soils for the selected depth level, and also eases the task of determination of coordinates when correlating various anomalies, identified by different methods.

For citation: Mingaleva T.A., Shakuro S.V., Senchina N.P., Egorov A.S. Application of RGB-synthesis for complex interpretation of geophysical data in the study of areas contaminated by oil products. Geosistemy perehodnykh zon = Geosystems of Transition Zones, 2023, vol. 7, no. 1, pp. 75–85. (In Russ., abstr. in Engl.).
https://doi.org/10.30730/gtrz.2023.7.1.075-085, https://www.elibrary.ru/gytyub

Для цитирования: Мингалева Т.А., Шакуро С.В., Сенчина Н.П., Егоров А.С. Применение RGB-синтеза для комплексной интерпретации данных геофизических методов при изучении территорий, загрязненных нефтепродуктами. Геосистемы переходных зон, 2023, т. 7, № 1, с. 75–85.
https://doi.org/10.30730/gtrz.2023.7.1.075-085, https://www.elibrary.ru/gytyub

1. Titov K.V., Il’in Yu.T., Konosavskii P.K., Muslimov A.V., Rybal’chenko O.V., Orlova O.G., Meno A. 2012. Transformation of physical properties of sand contaminated with oil products upon bacterial impact. Geoekologiya, 5: 455–469 (In Russ.). EDN: PFJIDJ

2. Abdel Aal G.Z., Slater L.D., Atekwana E.A. 2006. Induced-polarization measurements on unconsolidated sediments from a site of active hydrocarbon biodegradation. Geophysics, 71: H13–H24. https://doi.org/10.1190/1.2187760

3. Atekwana E.A., Atekwana Eliot A., Werkema D.D., Allen J.P., Smart L.A., Duris J.W., Cassidy D.P., Sauck W.A., Rossbach S. 2004. Evidence for microbial enhanced electrical conductivity in hydrocarbon-contaminated sediments. Geophysical Research Letters, 31: L23501.1–L23501.4. https://doi.org/10.1029/2004gl021359

4. Personna Y.R., Ntarlagiannis D., Slater L., Yee N., O’Brien M., Hubbard S. 2008. Spectral induced polarization and electrodic potential monitoring of microbially mediated iron sul?de transformations. J. of Geophysical Research, 113(2): C02020.1–C02020.13. https://doi.org/10.1029/2007jg000614

5. Lelievre P.G., Farquharson C.G. 2016. Integrated imaging for mineral exploration. In: Integrated imaging of the Earth: Theory and applications, p. 61–95. (Wiley Online Library, Geophysical Monograph Series). https://doi.org/10.1002/9781118929063.ch8

6. Atekwana E.A., Atekwana Eliot A. 2010. Geophysical signatures of microbial activity at hydrocarbon contaminated sites: A review. Surveys in Geophysics, 31: 247–283. https://doi.org/10.1007/s10712-009-9089-8

7. Sauck W.A. 2000. A model for the resistivity structure of LNAPL plumes and their environs in sandy sediments. J. of Applied Geophysics, 44(2–3): 151–165. https://doi.org/10.1016/S0926-9851(99)00021-X

8. Garg S., Newell C.J., Kulkarni P.R., King D.C., Adamson D.T., Renno M.I., Sale T. 2017. Overview of natural source zone depletion: Processes, controlling factors, and composition change. Groundwater Monitoring and Remediation, 37(3). https://doi.org/10.1111/gwmr.12219

9. Spichak V.V., Bezruk I.A., Goydina A.G. 2015. Construction of the three-dimensional claster petrophysical models of geological medium based on the geophysical data collected along reference profiles. Prospect and protection of mineral resources, 4: 41–45. (In Russ.). EDN: TOVFLJ

10. Castro D., Branco R.M.G. 2003. 4-D ground penetrating radar monitoring of a hydrocarbon leakage site in Fortaleza (Brazil) during its remediation process: A case history. J. of Applied Geophysics, 54(1): 127–144. https://doi.org/10.1016/j.jappgeo.2003.08.021

11. Orozco A.F., Ciampi P., Katona T., Censini M., Papini M.P., Deidda G.P., Cassiani G. 2021. Delineation of hydrocarbon contaminants with multi-frequency complex conductivity imaging. Science of the Total Environment, 768: 144997. https://doi.org/10.1016/j.scitotenv.2021.144997

12. Orozco A.F., Micic V., Bucker M., Gallistl J., Hofmann Th., Nguyen F. 2019. Complex-conductivity monitoring to delineate aquifer pore clogging during nanoparticles injection. Geophysical Journal International, 218(3): 1838–1852. https://doi.org/10.1093/gji/ggz255

13. Golizdra G.Y. 1980. Statement of the problem of comprehensive interpretation of gravity fields and seismic observations. Izv., Physics of the Solid Earth, 16: 535–539.

14. Haber E., Oldenburg D. 1997. Joint inversion: a structural approach. Inverse Problems, 13(1): 63–77. https://doi.org/10.1088/0266-5611/13/1/006

15. Hu W., Abubakar A., Habashy T.M. 2009. Joint electromagnetic and seismic inversion using structural constraints. Geophysics, 74(6): R99–R109. https://doi.org/10.1190/1.3246586

16. Daniliev S.M., Danilieva N.A., Isakova E.P., Ashkar G.K. 2020. Research of cracking at facing stone deposit using the georadar method. Izvestiya TPU = Bulletin of the Tomsk Polytechnic University, 331(9): 140–145. (In Russ.). https://doi.org/10.18799/24131830/2020/9/2816

17. Glazunov V.V., Shtengel V.G., Nedyalkov V.S., Efimova N.N., Danilev S.M. 2018. Combined investigation by thermal imaging and georadar scanning for voids detection under reinforced concrete slabs of fastening soil slopes of hydraulic structures. In: Engineering and Mining Geophysics, 2018: 1–11. (European Association of Geoscientists & Engineers: Conf. paper). https://doi.org/10.3997/2214-4609.201800474

18. Miller A.A., Gorelik G.D., Budanov L.M. 2019. Substantiation of the optimal GIS complex for the allocation of water-containing reservoirs on the example of the analysis of well logging results in the Leningrad region. In: Engineering and Mining Geophysics, 2019: 1–8. (European Association of Geoscientists & Engineers: Conf. paper). https://doi.org/10.3997/2214-4609.201901693

19. Danileva N., Danilev S., Bolshakova N. 2020. Isolation of brine aquifers in carbonate rocks of above-salt sediments by a limited set of geophysical studies of wells. In: Engineering and Mining Geophysics, Saint Petersburg, 2020: 1–5. (European Association of Geoscientists & Engineers: Conf. paper). https://doi.org/10.3997/2214-4609.202053066

20. Movchan I.B., Shaygallyamova Z.I., Yakovleva A.A. 2022. Identification of structural control factors of primary gold ore occurrences by method of unmanned aeromagnetic survey by the example of the Neryungrisky district of Yakutia. J. of Mining Institute, 254: 217–233. (In Russ.&Engl.). https://doi.org/10.31897/PMI.2022.23

21. Abubakar A., Gao G., Habashy T.M., Liu J. 2012. Joint inversion approaches for geophysical electromagnetic and elastic full-waveform data. Inverse Problems, 28(5): 055016. https://doi.org/10.1088/0266-5611/28/5/055016

22. Colombo D., De Stefano M. 2007. Geophysical modeling via simultaneous joint inversion of seismic, gravity, and electromagnetic data: Application to prestack depth imaging. The Leading Edge, 26: 326–331. https://doi.org/10.1190/1.2715057

23. Moorkamp M., Jones A.G., Fishwick S. 2010. Joint inversion of receiver functions, surface wave dispersion, and magnetotelluric data. J. Geophysical Research: Solid Earth, 115(B4). https://doi.org/10.1029/2009jb006369

24. Spichak V.V. 2009. Modern approaches to integrated geophysical data inversion. Geophysics, 5: 10–19. (In Russ.). EDN: SAVPXR

25. Spichak B.V., Bezruk I.A., Popova I.V. 2008. Deep cluster petrophysical sections construction using geophysical data for assessment of hydrocarbon potential. Geophysics, 5: 43–45. (In Russ.). EDN: SBKPNR

26. Agostinetti N.P., Bodin Th. 2018. Flexible coupling in joint inversions: A Bayesian structure decoupling algorithm. J. Geophysical Research: Solid Earth, 123: 8798–8826. https://doi.org/10.1029/2018JB016079

27. Di Giuseppe M.G., Troiano A., Patella D., Piochi M., Carlino S. 2018. A geophysical k-means cluster analysis of the Solfatara-Pisciarelli volcano-geothermal system, Campi Flegrei (Naples, Italy). J. of Applied Geophysics, 156: 44–54. https://doi.org/10.1016/j.jappgeo.2017.06.001

28. Yu Siwei, Ma Jianwei. 2020. Data-driven geophysics: from dictionary learning to deep learning. ArXiv abs/2007.06183. https://doi.org/10.48550/arXiv.2007.06183

29. Bedrosian P.A., Maercklin N., Weckmann U., Bartov Y., Ryberg T., Ritter O. 2007. Lithology-derived structure classification from the joint interpretation of magnetotelluric and seismic models. Geophysical Journal International, 170(2): 737–748. https://doi.org/10.1111/j.1365-246X.2007.03440.x

30. Akhmetshin E.M., Kolpak E.P., Sulimova E.A., Kireev V.S., Samarina E.A., Solodilova N.Z. 2017. Clustering as a criterion for the success of modern industrial enterprises. International Journal of Applied Business and Economic Research, 15(23): 221–231.

31. Nikitin A.A., Bulychev A.A. 2015. [ Complex analysis and complex interpretation of geophysical fields: textbook]. Moscow: VNIIGeosystem, 94 p. (In Russ.).

32. Kotyrba B., Schmidt V. 2014. Combination of seismic and resistivity tomography for the detection of abandoned mine workings in Munster/Westfalen, Germany: Improved data interpretation by cluster analysis. Near Surface Geophysics, 12(3): 415–426. https://doi.org/10.3997/1873-0604.2013056

33. RGB-sintez dlya kompleksnoj interpretacii dannyh nesejsmicheskih metodov [RGB synthesis for the complex interpretation of data of non-seismic methods]: State registration certificate of the computer program No. 2021616804 RF. 2021. Authors: Grigoriev G.K., Mingaleva T.A., Senchina N.P. Appl. 23.04.2021; No. 2021615994; publ. 27.04.2021, Bull. No. 5. (In Russ.).

34. Digital catalogs of geological maps. Russian Geological Research Institute (VSEGEI). (In Russ.). URL: https://vsegei.ru/ru/info/catalog_ggk/ (accessed 21.09.2022).