Aquifer characterization and salinization origin using unsupervised machine learning and 3D gravity inversion modeling, Siwa Oasis, Egypt

Abstract

This study presents an integrated approach, combining machine learning clustering with gravity data analysis, to characterize the region’s aquifer systems. K-means and Self-Organizing Maps (SOM) were applied to well log data, including Gamma Ray (GR), Spontaneous Potential (SP), and resistivity (R), to delineate lithofacies. Three distinct units were identified: clean sand, shaly sand, and clay-rich facies. The SOM algorithm outperformed the clustering of K-means in accurately estimating layer thickness and resolving lithological transitions. A 3D lithofacies model revealed spatial heterogeneity within the NSSA, highlighting clean sand layers as primary groundwater extraction zones. Gravity data analysis using upward continuation and edge-filtering techniques identified dominant NE-SW, NW-SE, and E-W lineaments controlling groundwater flow dynamics. The 3D gravity inversion model revealed density contrasts associated with structural features, providing insights into potential groundwater flow between aquifers. Spatial analysis reveals lower groundwater salinity in the southern part of the Oasis, coinciding with areas of reduced structural complexity. Higher salinity zones in central and northeastern regions show spatial correlation with gravity-derived structural systems, though causal relationships require additional validation through hydrochemical studies. This integrated approach provides critical insights for sustainable groundwater management in structurally complex arid environments. Groundwater salinization in arid oasis environments poses significant challenges for sustainable water resource management. In Siwa Oasis, Egypt, the deep Nubian Sandstone Aquifer System (NSSA) and the shallow Tertiary Carbonate Aquifer (TCA) interact through fault systems. At the same time, the potential leakage from hypersaline surface lakes creates complex hydrogeological conditions that require comprehensive characterization. Despite the critical importance of understanding aquifer connectivity and salinization processes, there remains a significant knowledge gap in the quantitative integration of multiple geophysical datasets for objective aquifer characterization and structural control identification. Traditional methods lack the spatial resolution and objective framework necessary to map lithofacies distributions and identify structural pathways controlling groundwater flow in complex multi-aquifer systems.

Keywords

Aquifer characterization / Salinization origin / Unsupervised machine learning / 3D gravity inversion modeling / Fault and fracture systems

CRediT authorship contribution statement

Mohamed Hamdy Eid: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Methodology, Investigation, Conceptualization. Khouloud Jlaiel: Writing - original draft, Software, Data curation. Mohamed Ayed Elbalawy: Writing - original draft, Visualization, Software. Yetzabbel G. Flores: Writing - original draft, Software, Methodology, Data curation. Ali A. Mohieldain: Visualization, Validation, Software, Data curation. Tamer Nassar: Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Mostafa R. Abukhadra: Writing - review & editing, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Haifa A. Alqhtani: Writing - review & editing, Resources, Funding acquisition. Attila Kovács: Writing - review & editing, Supervision. Péter Szucs: Writing - review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We would like to thank the sustainable development and technologies national program of the Hungarian Academy of Sciences (FFT NP FTA) for funding this research work. The authors acknowledge Princess Nourah bint Abdulrahman University Researchers Supporting Project (Grant No. PNURSP2026R458), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Data availability

The datasets utilized and/or analyzed during the current study are available upon request from the corresponding author.

References

[1] Abdallah, A., 2007. Assessment of salt weathering in Siwa Oasis (The Western Desert of Egypt). Bull. De la Soc. De Geog. De Egypt 80, 66-83.

[2] Abdel-Fattah, M., Saber, M., Kantoush, S.A., Khalil, M.F., Sumi, T., Sefelnasr, A.M., 2017. A hydrological and geomorphometric approach to understanding the generation of wadi flash floods. Water 9, 553. https://doi.org/10.3390/w9070553.

[3] Abdel-Fattah, Z.A., Kora, M.A., Ayyad, S.N., 2013. Facies architecture and depositional development of Middle Miocene carbonate strata at Siwa Oasis, Northwestern Egypt. Facies 59, 505-528. https://doi.org/10.1007/s10347-012-0332-2.

[4] Abdel-Mogheeth, S.M., 1996. Groundwater hazards in Siwa Oasis, Egypt. In:Proceedings of the UNESCO/NWRC/ACSAD Workshops on Wadi Hydrology and Groundwater Protection. Cairo, Egypt, pp. 113-118.

[5] Abdulaziz, A.M., Faid, A.M., 2015. Evaluation of the groundwater resources potential of Siwa Oasis using three-dimensional multilayer groundwater flow model, Mersa Matruh Governorate, Egypt. Arab. J. Geosci. 8, 659-675. https://doi.org/10.1007/s12517-013-1199-4.

[6] Abou El-Magd, I., Faid, A., 2007. Hydrogeological and environmental studies on Siwa Oasis, Egypt using GIS technique, Egypt. J. Soil Sci. 47, 435-450.

[7] Abraham, E.M., Alile, O.M., 2019. Modelling subsurface geologic structures at the Ikogosi geothermal field, southwestern Nigeria, using gravity, magnetics and seismic interferometry techniques. J. Geophys. Eng. 16, 729-741. https://doi.org/10.1093/jge/gxz034.

[8] Afifi, A.H., 2005. Assessment of the hydrogeological conditions of groundwater in Siwa Oasis, North Western Desert. Ph.D. thesis, Minufiya University, Egypt, p. 98.

[9] Aggour, T.A., Faid, A.M., 2007. Hydrogeology of Siwa Oasis and landuse map, Egypt. J. Remote Sens. Space Sci. 9, 135-156.

[10] Ali, A., Sheng-Chang, C., 2020. Characterization of well logs using K-mean cluster analysis. J. Petrol. Explor. Prod. Technol. 10, 2245-2256. https://doi.org/10.1007/s13202-020-00895-4.

[11] Aly, A.A., Al-Omran, A.M., Alharby, M.M., 2015. The water quality index and hydrochemical characterization of groundwater resources in Hafar Albatin, Saudi Arabia. Arab. J. Geosci. 8, 4177-4190. https://doi.org/10.1007/s12517-014-1463-2.

[12] Aly, A.A., Kishk, F.M., Gaber, H.M., Al-Omran, A.M., 2016. Long-term detection and hydrochemistry of groundwater resources in Egypt: Case study of Siwa Oasis. J. Saudi Soc. Agric. Sci. 15, 67-74. https://doi.org/10.1016/j.jssas.2014.04.003.

[13] Bergen, K.J., Johnson, P.A., De Hoop, M.V., Beroza, G.C., 2019. Machine learning for data-driven discovery in solid Earth geoscience. Science 363, eaau0323. https://doi.org/10.1126/science.aau0323.

[14] Bestagini, P., Lipari, V., Tubaro, S., 2017. A machine learning approach to facies classification using well logs. In: SEG Technical Program Expanded Abstracts 2017. Presented at the SEG Technical Program Expanded Abstracts 2017, Society of Exploration Geophysicists, Houston, Texas, pp. 2137-2142. doi: 10.1190/segam2017-17729805.1

[15] Binley, A., Hubbard, S.S., Huisman, J.A., Revil, A., Robinson, D.A., Singha, K., Slater, L.D., 2015. The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales. Water Resour. Res. 51, 3837-3866. https://doi.org/10.1002/2015WR017016.

[16] Blakely, R.J., 1996. Potential Theory in Gravity and Magnetic Applications. Cambridge University Press, pp. 441. doi: 10.1017/CBO9780511549816.

[17] Carreira, V.R., Bijani, R., Ponte-Neto, C.F., 2024. Reconstruction of lithofacies using a supervised Self-Organizing Map: Application in pseudo-wells based on a synthetic geologic cross-section. Artif. Intell. Geosci. 5, 100072. https://doi.org/10.1016/j.aiig.2024.100072.

[18] Chang, H.C., Kopaska-Merkel, D.C., Chen, H.C., 2002. Identification of lithofacies using Kohonen self-organizing maps. Comput. Geosci. 28, 223-229. https://doi.org/10.1016/S0098-3004(01)00067-X.

[19] Fung, C.C., Wong, K.W., Eren, H., 1997. Modular artificial neural network for prediction of petrophysical properties from well log data. IEEE Trans. Instrum. Meas. 46, 1295-1299. https://doi.org/10.1109/19.668276.

[20] Clarke, G.K.C., 1969. Optimum second-derivative and downward-continuation filters. Geophysics 34, 424-437. https://doi.org/10.1190/1.1440020.

[21] Cooper, G.R.J., Cowan, D.R., 2006. Enhancing potential field data using filters based on the local phase. Comput. Geosci. 32, 1585-1591. https://doi.org/10.1016/j.cageo.2006.02.016.

[22] Dahab, K.A., 2004. Impact of the present groundwater exploitation system on the Nubia sandstone aquifer in Siwa Oasis, Western Desert, Egypt. In: on Geochemistry. Alex.Univ., Egypt, pp.319-337.

[23] Díaz Gómez, R., Pasternack, G.B., Guillon, H., Byrne, C.F., Schwindt, S., Larrieu, K.G., Solis, S.S., 2022. Mapping subaerial sand-gravel-cobble fluvial sediment facies using airborne lidar and machine learning. Geomorphology 401, 108106. https://doi.org/10.1016/j.geomorph.2021.108106.

[24] Döll, P., Müller Schmied, H., Schuh, C., Portmann, F.T., Eicker, A., 2014. Global-scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites. Water Resour. Res. 50, 5698-5720. https://doi.org/10.1002/2014WR015595.

[25] Eid, M.H., Eissa, M., Mohamed, E.A., Ramadan, H.S., Czuppon, G., Kovács, A., Szucs, P., 2024a. Application of stable isotopes, mixing models, and K-means cluster analysis to detect recharge and salinity origins in Siwa Oasis, Egypt. Groundw. Sustain. Dev. 25, 101124. https://doi.org/10.1016/j.gsd.2024.101124.

[26] Eid, M.H., Shebl, A., Eissa, M., Mohamed, E.A., Fahil, A.S., Ramadan, H.S., Abukhadra, M.R., El-Sherbeeny, A.M., Kovacs, A., Szucs, P., 2024b. Comprehensive approach integrating remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems. Heliyon 10, e32992. https://doi.org/10.1016/j.heliyon.2024.e32992.

[27] Eissa, M., 2023. Geochemical, natural radiogenic and isotopic implications for Nubian sandstone groundwater assessment in Kharga Oasis, western desert, Egypt. Egypt. J. Desert Res. 73, 629-653. https://doi.org/10.21608/ejdr.2024.251259.1158.

[28] El Hossary, M.F.M., 2013. Investigating the development challenges to Siwa Oasis, northwestern desert, Egypt. NY Sci. J. 6, 55-61.

[29] Elbeih, S.F., Zaghloul, E.A., 2021. Hydrologeological and hydrological conditions of Dakhla Oasis. In: Iwasaki E., Negm A.M., Elbeih S.F. (Eds.),Sustainable Water Solutions in the Western Desert, Egypt: Dakhla Oasis, Earth and Environmental Sciences Library. Springer International Publishing, Cham, pp. 185-201. https://doi.org/10.1007/978-3-030-64005-7_11.

[30] Eldosouky, A.M., Eleraki, M., Mansour, A., Saada, S.A., Zamzam, S., 2024. Geological controls of mineralization occurrences in the Egyptian Eastern Desert using advanced integration of remote sensing and magnetic data. Sci. Rep. 14, 16700. https://doi.org/10.1038/s41598-024-66924-y.

[31] Elnaggar, A., Mosa, A., El-Seedy, M., Biki, F., 2016. Evaluation of soil agricultural productive capability by using remote sensing and GIS techniques in Siwa Oasis, Egypt. J. Soil Sci. Agric. Eng. 7, 547-555. https://doi.org/10.21608/jssae.2016.39785.

[32] El-Sayed, S.A., Allam, K.A., Salama, M.H., El Begawy, H., 2017. Investigation of chemical and radiochemical fingerprints of water resources in Siwa Oasis, Western Desert Egypt. Arab J. Nucl. Sci. Appl. 50, 158-178.

[33] Embaby, A., Youssef, Y.M., Abu El-Magd, S.A., 2024. Delineation of lineaments for groundwater prospecting in hard rocks: inferences from remote sensing and geophysical data. Environ. Earth Sci. 83, 62. https://doi.org/10.1007/s12665-023-11389-x.

[34] Famiglietti, J.S., 2014. The global groundwater crisis. Nature Clim. Change 4, 945-948. https://doi.org/10.1038/nclimate2425.

[35] Ferreira, F.J.F., De Souza, J., De, B.E.S., Bongiolo, A., De Castro, L.G., 2013. Enhancement of the total horizontal gradient of magnetic anomalies using the tilt angle. Geophysics 78, J33-J41. https://doi.org/10.1190/geo2011-0441.1.

[36] Foster, S., Pulido-Bosch, A., Vallejos, Á., Molina, L., Llop, A., MacDonald, A.M., 2018. Impact of irrigated agriculture on groundwater-recharge salinity: a major sustainability concern in semi-arid regions. Hydrogeol. J. 26, 2781-2791. https://doi.org/10.1007/s10040-018-1830-2.

[37] Ghoneim, E., Healey, C., Hemida, M., Shebl, A., Fahil, A., 2023. Integration of geophysical and geospatial techniques to evaluate geothermal energy at Siwa Oasis, western desert, Egypt. Remote Sens. 15, 5094. https://doi.org/10.3390/rs15215094.

[38] Gleeson, T., Befus, K.M., Jasechko, S., Luijendijk, E., Cardenas, M.B., 2016. The global volume and distribution of modern groundwater. Nature Geosci. 9, 161-167. https://doi.org/10.1038/ngeo2590.

[39] Gómez, F., Flores, Y., Vadászi, M., 2022. Comparative analysis of the K-nearest-neighbour method and K-means cluster analysis for lithological interpretation of well logs of the Shushufindi Oilfield, Ecuador. MGPB 37, 155-165. https://doi.org/10.17794/rgn.2022.4.13.

[40] Hamdy Eid, M., Szucs, P., Kovács, A., 2022. Problems threatening sustainability in Siwa Oasis and recommendations for understanding the sources of water quality deterioration. Geosci. Eng. 10, 138-153. https://doi.org/10.33030/geosciences.2022.15.138.

[41] Hasan, M.M.U., Hasan, T., Shahidi, R., James, L., Peters, D., Gosine, R., 2023. Lithofacies identification from wire-line logs using an unsupervised data clustering algorithm. Energies 16, 8116. https://doi.org/10.3390/en16248116.

[42] Hassan, I., Kalin, R.M., White, C.J., Aladejana, J.A., 2019. Hydrostratigraphy and hydraulic characterisation of shallow coastal aquifers, Niger Delta Basin: a strategy for groundwater resource management. Geosciences 9, 470. https://doi.org/10.3390/geosciences9110470.

[43] Hinze, W.J., Kane, M.F., O’Hara, N.W., Reford, M.S., Tanner, J., Weber, C., 1985. The Utility of Regional Gravity and Magnetic Anomaly Maps. Society of Exploration Geophysicists, Tulsa, Oklahoma, pp. 454. doi: 10.1190/1.0931830346.

[44] Hinze, W.J., Von Frese, R.R., Saad, A.H., 2013. Gravity and Magnetic Exploration:Principles, Practices, and Applications. Cambridge University Press, p. 511.

[45] Hosseini, S.A., Keshavarz Faraj Khah, N., Kianoush, P., Afzal, P., Shakiba, S., Jamshidi, E., 2023. Boundaries determination in potential field anomaly utilizing analytical signal filtering and its vertical derivative in Qeshm Island SE Iran. Res. Geophys. Sci. 14, 100053. https://doi.org/10.1016/j.ringps.2023.100053.

[46] Ibraheem, I.M., El-Husseiny, A.A., Othman, A.A., 2022. Structural and mineral exploration study at the transition zone between the North and the Central Eastern Desert, Egypt, using airborne magnetic and gamma-ray spectrometric data. Geocarto Int. 37, 13098-13126. https://doi.org/10.1080/10106049.2022.2076915.

[47] Jacobsen, B.H., 1987. A case for upward continuation as a standard separation filter for potential-field maps. Geophysics 52, 1138-1148. https://doi.org/10.1190/1.1442378.

[48] Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H.A., Kumar, V., 2019. Machine learning for the geosciences: challenges and opportunities. IEEE Trans. Knowl. Data Eng. 31, 1544-1554. https://doi.org/10.1109/TKDE.2018.2861006.

[49] Kebede, H., Alemu, A., Nedaw, D., Fisseha, S., 2021. Depth estimates of anomalous subsurface sources using 2D/3D modeling of potential field data: implications for groundwater dynamics in the Ziway-Shala Lakes Basin, Central Main Ethiopian Rift. Heliyon 7, e06843. https://doi.org/10.1016/j.heliyon.2021.e06843.

[50] Kennedy, M., 2015. Chapter 13 - Thin Bed Pays: Dealing with the Limitations of Log Resolution, in: Developments in Petroleum Science. Elsevier, pp. 341-361. doi: 10.1016/B978-0-444-63270-8.00013-X.

[51] Khalifa, M.A., Zaky, A.S., Jovane, L., El-Hewy, A.M., Zahran, E., Kasem, A.M., 2024. The middle Miocene microfacies, cyclicity, and depositional history: Implications on the Marmarica Formation at the Siwa Oasis, Western Desert (Egypt). Minerals 14, 73. https://doi.org/10.3390/min14010073.

[52] Kitanidis, P.K., 2015. Persistent questions of heterogeneity, uncertainty, and scale in subsurface flow and transport. Water Resour. Res. 51, 5888-5904. https://doi.org/10.1002/2015WR017639.

[53] Kohonen, T., 1982. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59-69. https://doi.org/10.1007/BF00337288.

[54] Kumar, U., Pal, S.K., Sahoo, S.D., Narayan, S., Saurabh, Mondal, S., Ganguli, S.S., 2018. Lineament mapping over Sir Creek offshore and its surroundings using high resolution EGM2008 Gravity data: an integrated derivative approach. J. Geol. Soc. India 91, 671-678. https://doi.org/10.1007/s12594-018-0922-x.

[55] Lai, J., Pang, X., Zhao, X., Zhao, Y., Wang, G., Huang, Y., Li, H., Li, Y., 2023. Typical misinterpretations and scientific concepts in well-logging geologic studies. Nat. Gas Ind. B 10, 198-211. https://doi.org/10.1016/j.ngib.2023.02.003.

[56] Liu, J., Wang, X., Suo, Y., Li, S., Zhou, J., 2023. A robust high-resolution potential-field edge detection filter with its application in the Indo-China Peninsula. Tectonophysics 868, 230109. https://doi.org/10.1016/j.tecto.2023.230109.

[57] Luo, X., Sun, J., Zhang, J., Liu, W., 2024. A new method based on multiresolution graph-based clustering for lithofacies analysis of well logging. Comput. Geosci. 28, 491-502. https://doi.org/10.1007/s10596-024-10277-y.

[58] Masoud, A., Koike, K., 2006. Tectonic architecture through Landsat-7 ETM+/SRTM DEM-derived lineaments and relationship to the hydrogeologic setting in Siwa region, NW Egypt. J. Afr. Earth Sci. 45, 467-477. https://doi.org/10.1016/j.jafrearsci.2006.04.005.

[59] Miller, H.G., Singh, V., 1994. Potential field tilt—a new concept for location of potential field sources. J. Appl. Geophys. 32, 213-217. https://doi.org/10.1016/0926-9851(94)90022-1.

[60] Misak, R.F., Abdel Baki, A.A., El-Hakim, M.S., 1997. On the causes and control of the waterlogging phenomenon, Siwa Oasis, northern Western Desert, Egypt. J. Arid Environ. 37, 23-32. https://doi.org/10.1006/jare.1997.0252.

[61] Mondol, N.H., 2015. Well logging:principles, applications and uncertainties. In: Bjørlykke K. (Ed.), Petroleum Geoscience. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 385-425. https://doi.org/10.1007/978-3-642-34132-8_16.

[62] Mosser, L., Dubrule, O., Blunt, M.J., 2018. Stochastic reconstruction of an oolitic limestone by generative adversarial networks. Transp. Porous Med. 125, 81-103. https://doi.org/10.1007/s11242-018-1039-9.

[63] Nabighian, M.N., 1972. The analytic signal of two-dimensional magnetic bodies with polygonal cross-section: its properties and use for automated anomaly interpretation. Geophysics 37, 507-517. https://doi.org/10.1190/1.1440276.

[64] Nabighian, M.N., Ander, M.E., Grauch, V.J.S., Hansen, R.O., LaFehr, T.R., Li, Y., Pearson, W.C., Peirce, J.W., Phillips, J.D., Ruder, M.E., 2005. Historical development of the gravity method in exploration. Geophysics 70, 63ND-89ND. https://doi.org/10.1190/1.2133785.

[65] Núñez-Demarco, P., Bonilla, A., Sánchez-Bettucci, L., Prezzi, C., 2023. Potential-field filters for gravity and magnetic interpretation: a review. Surv. Geophys. 44, 603-664. https://doi.org/10.1007/s10712-022-09752-x.

[66] Oldenburg, D.W., 1974. The inversion and interpretation of gravity anomalies. Geophysics 39, 526-536. https://doi.org/10.1190/1.1440444.

[67] Pham, L.T., Eldosouky, A.M., Oksum, E., Saada, S.A., 2022. A new high resolution filter for source edge detection of potential field data. Geocarto Int. 37, 3051-3068. https://doi.org/10.1080/10106049.2020.1849414.

[68] Pham, L.T., Oksum, E., Dolmaz, M.N., 2021. GRV_D_inv: a graphical user interface for 3D forward and inverse modeling of gravity data. Geofiz. Zhurnal. 43, 181-193. https://doi.org/10.24028/gzh.0203-3100.v43i1.2021.225546.

[69] Rabeh, T., 2012. Using 3-D magnetic modeling to evaluate subsurface structures of the Siwa Oasis, Western Desert, Egypt. Sci. China Earth Sci. 55, 279-289. https://doi.org/10.1007/s11430-011-4288-9.

[70] Rahman, A.T.M.S., Kono, Y., Hosono, T., 2022. Self-organizing map improves understanding on the hydrochemical processes in aquifer systems. Sci. Total Environ. 846, 157281. https://doi.org/10.1016/j.scitotenv.2022.157281.

[71] Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat, 2019. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195-204. https://doi.org/10.1038/s41586-019-0912-1.

[72] Saito, S., Underwood, M.B., Kubo, Y., andthe Expedition 322 Scientists, 2010. Proceedings of the Integrated Ocean Drilling Program. Volume 322: NanTroSEIZE Stage 2: Subduction Inputs. Integrated Ocean Drilling Program Management International, Inc., for the Integrated Ocean Drilling Program, Tokyo. doi: 10.2204/iodp.proc.322.2010.

[73] Saputelli, L., Celma, R., Boyd, D., Shebl, H., Gomes, J., Bahrini, F., Escorcia, A., Pandey, Y., 2019. Deriving permeability and reservoir rock typing supported with self-organized maps SOM and artificial neural networks ANN - Optimal workflow for enabling core-log integration. In: SPE Reservoir Characterisation and Simulation Conference and Exhibition, SPE, Abu Dhabi, UAE, p. D021S015R001. doi: 10.2118/196704-MS.

[74] Scanlon, B.R., Keese, K.E., Flint, A.L., Flint, L.E., Gaye, C.B., Edmunds, W.M., Simmers, I., 2006. Global synthesis of groundwater recharge in semiarid and arid regions. Hydrol. Process. 20, 3335-3370. https://doi.org/10.1002/hyp.6335.

[75] Schön, J.H., 2015. Physical Properties of Rocks: Fundamentals and Principles of Petrophysics. Elsevier, pp. 2-497.

[76] Shin, Y.H., Choi, K.S., Xu, H., 2006. Three-dimensional forward and inverse models for gravity fields based on the Fast Fourier Transform. Comput. Geosci. 32, 727-738. https://doi.org/10.1016/j.cageo.2005.10.002.

[77] Soliman, M.R.H., 2016. Revealing and interpreting of subsurface tectonic pattern of the area north of Siwa Oasis, western desert, Egypt, using bouguer gravity data. J. Egypt. Geophys. Soc. 14, 17-28. https://doi.org/10.21608/jegs.2016.385648.

[78] Szabó N.P., Kilik, R., Dobróka, M., 2023. Robust reservoir identification by multi-well cluster analysis of wireline logging data. Heliyon 9, e15957. https://doi.org/10.1016/j.heliyon.2023.e15957.

[79] Taylor, R.G., Scanlon, B., Döll, P., Rodell, M., Van Beek, R., Wada, Y., Longuevergne, L., Leblanc, M., Famiglietti, J.S., Edmunds, M., Konikow, L., Green, T.R., Chen, J., Taniguchi, M., Bierkens, M.F.P., MacDonald, A., Fan, Y., Maxwell, R.M., Yechieli, Y., Gurdak, J.J., Allen, D.M., Shamsudduha, M., Hiscock, K., Yeh, P.J.F., Holman, I., Treidel, H., 2013. Ground water and climate change. Nature Clim. Change 3, 322-329. https://doi.org/10.1038/nclimate1744.

[80] Uieda, L., Barbosa, V.C.F., 2012. Robust 3D gravity gradient inversion by planting anomalous densities. Geophysics 77, G55-G66. https://doi.org/10.1190/geo2011-0388.1.

[81] Varhaug, M., 2016. The defining series: basic well log interpretation. Oilfield Rev. 2, 52-53.

[82] Verduzco, B., Fairhead, J.D., Green, C.M., MacKenzie, C., 2004. New insights into magnetic derivatives for structural mapping. Lead. Edge 23, 116-119. https://doi.org/10.1190/1.1651454.

[83] Vesanto, J., Alhoniemi, E., 2000. Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11, 586-600. https://doi.org/10.1109/72.846731.

[84] Wada, Y., Van Beek, L.P.H., Van Kempen, C.M., Reckman, J.W.T.M., Vasak, S., Bierkens, M.F.P., 2010. Global depletion of groundwater resources. Geophys. Res. Lett. 37, 2010GL044571. https://doi.org/10.1029/2010GL044571.

[85] Wang, C.Y., Rui, F., Zhengsheng, Y., Xingjue, S., 1986. Gravity anomaly and density structure of the San Andreas fault zone. Pure Appl. Geophys. 124, 127-140. https://doi.org/10.1007/BF00875722.

[86] Zhang, X., Yu, P., Tang, R., Xiang, Y., Zhao, C.J., 2015. Edge enhancement of potential field data using an enhanced tilt angle. Explor. Geophys. 46, 276-283. https://doi.org/10.1071/EG13104.