Self potential data inversion utilizing the Bat optimizing algorithm (BOA) with various application cases

Essa, Khalid S.; Diab, Zein E.; Mehanee, Salah A.

doi:10.1007/s11600-022-00955-9

Artykuł - szczegóły

Tytuł artykułu

Self potential data inversion utilizing the Bat optimizing algorithm (BOA) with various application cases

Autorzy

Essa Khalid S. , Diab Zein E. , Mehanee Salah A.

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00955-9

Warianty tytułu

Języki publikacji

Abstrakty

The Bat optimizing algorithm (BOA) is one of the metaheuristic algorithms and applied here to interpret self-potential (SP) data. The BOA is depending upon a bat echolocation behavior for global optimization, which the global optimum solution reached at the suggested minimum value of the objective function. The best interpretive source parameters for the subsurface structures occurred at the minimal the objective function value (global best solution). The BOA is applied to 2D SP anomaly data to estimate the characteristic source parameters (i.e., the depth to center, amplitude coefficient, origin location, geometric shape factor, and polarization and inclination angle of the causative buried structure). The BOA can be applied to single and multiple source structures in the restricted class of simple geometric shapes, which these bodies help in the validation of the subsurface ore and mineral targets. The stability and efficiency of the proposed BOA have been examined by several synthetic examples. In addition, three different real field examples from Germany and Indonesia have been successfully applied to ore and mineral investigation and geological structure studies. In general, the achieved results are in good agreement with the available borehole data and results mentioned in the literature.

Słowa kluczowe

self-potential mineral exploration ore exploration fault Bat Optimizing Algorithm BOA simple geometric shapes inclined sheet

potencjał własny poszukiwanie surowców mineralnych poszukiwanie rud usterka

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2023

Tom

Vol. 71, no. 2

Strony

567--586

Opis fizyczny

Bibliogr. 69 poz.

Twórcy

autor

Essa Khalid S.

khalid_sa_essa@cu.edu.eg

Geophysics Department, Faculty of Science, Cairo University, P.O. 12613, Giza, Egypt

autor

Diab Zein E.

Geophysics Department, Faculty of Science, Cairo University, P.O. 12613, Giza, Egypt

autor

Mehanee Salah A.

Geophysics Department, Faculty of Science, Cairo University, P.O. 12613, Giza, Egypt

Bibliografia

1. Abdelrahman EM, Saber HS, Essa KS, Fouda MA (2004) A least-squares approach to depth determination from numerical horizontal self-potential gradients. Pure Appl Geophys 161:399–411
2. Abdelrahman EM, Essa KS, Abo-Ezz ER, Soliman KS (2006) Self-potential data interpretation using standard deviations of depths computed from moving average residual anomalies. Geophys Prospect 54:409–423
3. Abdelrahman EM, El-Araby TM, Essa KS (2009) Shape and depth determinations from second moving average residual self-potential anomalies. J Geophys Eng 6:43–52
4. Al-Garani MA (2010) Interpretation of spontaneous potential anomalies from some simple geometrically shaped bodies using neural network inversion. Acta Geophys 58:143–162
5. Alzwar M, Akbar N, Bachri S (1992) Geological Map of Garut and Pameungpeuk sheet, Jawa, Scale 1: 100,000. 2nd edn, Geological survey Centre, Geology Agency, Bandung
6. Banerjee B (1971) Quantitative interpretation of self-potential anomalies of some specific geometric bodies. Pure Appl Geophys 90:138–152
7. Biswas A (2017) A review on modeling, inversion and interpretation of self-potential in mineral exploration and tracing paleo-shear zones. Ore Geol Rev 91:21–56
8. Biswas A, Mandal A, Sharma SP, Mohanty WK (2014) Delineation of subsurface structure using self-potential, gravity and resistivity surveys from south Purulia Shear zone, India: implication to uranium mineralization. Interpretation 2:T103–T110
9. Di Maio R, Piegari E, Rani P, Avella A (2016) Self-Potential data inversion through the integration of spectral analysis and tomographic approaches. Geophys J Int 206:204–1220
10. Di Maio R, Piegari E, Rani P (2017) Source depth estimation of self-potential anomalies by spectral methods. J Appl Geophys 136:315–325
11. Di Maio R, Piegari E, Rani P, Carbonari R, Vitagliano E, Milano L (2019) Quantitative interpretation of multiple self-potential anomaly sources by a global optimization approach. J Appl Geophys 162:152–163
12. Drahor MG (2004) Application of the self-potential method to archaeological prospection: some case histories. Archaeol Prospect 11:77–105
13. Elhussein M (2021) A novel approach to self-potential data interpretation in support of mineral resource development. Nat Resour Res 30:97–127
14. Emmermann R, Lauterjung J (1997) The German continental deep drilling program KTB: overview and major results. J Geophys Res-Solid Earth 102(B8):18179–18201
15. Eppelbaum LV (2021) Review of processing and interpretation of self-potential anomalies: transfer of methodologies developed in magnetic prospecting. Geosciences 11:194
16. Essa KS (2011) A new algorithm for gravity or self-potential data interpretation. J Geophys Eng 8:434–446
17. Essa KS (2019) A particle swarm optimization method for interpreting self-potential anomalies. J Geophys Eng 16:463–477
18. Essa KS (2020) Self potential data interpretation utilizing the particle swarm method for the finite 2D inclined dike: mineralized zones delineation. Acta Geod Geophys 55:203–221
19. Essa KS, Diab ZE (2022) Source parameters estimation from gravity data using bat algorithm with application to geothermal and volcanic activity. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-022-04263-z
20. Essa KS, Elhussein M (2017) A new approach for the interpretation of self-potential data by 2-D inclined plate. J Appl Geophys 136:455–461
21. Essa KS, Mahanee S, Smith PD (2008) A new inversion algorithm for estimating the best fitting parameters of some geometrically simple body to measured self-potential anomalies. Explor Geophys 39:155–163
22. Essa KS, Diab ZE, Elhussein M (2020) Self-potential data interpretation for two co-axial structures utilizing the RMS Parameter. J Environ Eng Geophys 25:15–23
23. Essa KS, Mehanee S (2007) A rapid algorithm for self-potential data inversion with application to mineral exploration, In: 19th International geophysical conference and exhibition, Australian society of exploration geophysicists, Perth, Australia
24. Fajriani WS, Pratomo PM (2017) Interpretation of Self-Potential anomalies for investigating fault using the Levenberg-Marquardt method: a study case in Pinggirsari, West Java, Indonesia. IOP Conf Ser: Earth Environ Sci 62:012004. https://doi.org/10.1088/1755-1315/62/1/012004
25. Fister IJ (2013) A comprehensive review of bat algorithms and their hybridization. M.Sc. thesis; University of Maribor, Slovenia
26. Fister IJ, Fister I, Yang XS (2013) A Hybrid Bat Algorithm: Electrotech Rev 80:1–7
27. Fournier C (1989) Spontaneous potentials and resistivity surveys applied to hydrogeology in a volcanic area: case history of the Chaine des Puys (Puyde-Dome, France). Geophys Prospect 37:647–668
28. Gao Y, Hao M, Wang Y, Dang L, Guo Y (2021) Multi-scale coal fire detection based on an improved active contour model from Landsat-8 satellite and UAV images. ISPRS Int J Geo-Inf 10:449
29. Göktürkler G, Balkaya C (2012) Inversion of self-potential anomalies caused by simple geometry bodies using global optimization algorithms. J Geophys Eng 9:498–507
30. Haryono A, Sungkono Agustin R, Santosa BJ, Widodo A, Ramadhany B (2020) Model parameter estimation and its uncertainty for 2-D inclined sheet structure in self-potential data using crow search algorithm. Acta Geod Geophys 55:691–715
31. Heinze T, Limbrock JK, Pudasaini SP, Kemna A (2019) Relating mass movement with electrical self-potential signals. Geophys J Int 216:55–60
32. Hirschmann G, Duyster J, Harms U, Kontny A, Lapp M, Hde W, Zulauf G (1997) The KTB superdeep borehole: petrography and structure of a 9-km-deep crustal section. Geol Rundsch 86:S3–S14
33. Hunter L, Powers M (2008) Geophysical investigations of earthen dams: An overview, In: 21st SAGEEP, p 1083–1096
34. Ikard SJ, Revil A, Jardani A, Woodruff WF, Parekh M, Mooney M (2012) Saline pulse test monitoring with the self-potential method to non-intrusively determine the velocity of the pore water in leaking areas of earth dams and embankments. Water Resour Res 48:W04201
35. Kawada Y, Kasaya T (2018) Self-potential mapping using an autonomous underwater vehicle for the Sunrise deposit, Izu-Ogasawara arc, southern Japan. Earth Planets Space 70:142–156
36. Khari M, Sinha A, Verdu E, Crespo RG (2020) Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimization. Soft Comput 24:9143–9160
37. Kukemilks K, Wagner J-F (2021) Detection of preferential water flow by electrical resistivity tomography and self-potential method. Appl Sci 11:4224
38. Mehanee S (2014) An efficient regularized inversion approach for self-potential data interpretation of ore exploration using a mix of logarithmic and nonlogarithmic model parameters. Ore Geol Rev 57:87–115
39. Mehanee S (2015) Tracing of paleo-shear zones by self-potential data inversion: case studies from the KTB, Rittsteig, and Grossensees graphite bearing fault planes. Earth Planets Space 67:14
40. Mehanee SA (2021) Simultaneous joint inversion of gravity and self- potential data measured along profile: theory, numerical examples, and a case study from mineral exploration with cross validation from electromagnetic data. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2021.3071973
41. Mehanee SA, Essa KS (2015) 2.5D regularized inversion for the interpretation of residual gravity data by a dipping thin sheet: numerical examples and case studies with an insight on sensitivity and non-uniqueness. Earth, Planets Space 67:130. https://doi.org/10.1186/s40623-015-0283-2
42. Meiser P (1962) A method of quantitative interpretation of self-potential measurements. Geophys Prospect 10:203–218
43. Minsley BJ, Sogade J, Morgan FD (2007) Three-dimensional self-potential inversion for subsurface DNAPL contaminant detection at the Savannah river site. South Carolina Water Resour Res 43:W04429
44. Minsley BJ, Coles DA, Vichabian Y, Morgan FD (2008) Minimization of self-potential survey mis-ties acquired with multiple reference locations. Geophysics 73:F71–F81
45. Oliveti I, Cardarelli E (2017) 2D approach for modeling self-potential anomalies: application to synthetic and real data. Boll Geof Teor Appl 58:415–430
46. Pekşen E, Yas T, Kayman AY, Özkan C (2011) Application of particle swarm optimization on self-potential data. J Appl Geophys 75:305–318. https://doi.org/10.1016/j.jappgeo.2011.07.0
47. Poormirzaee R, Sarmady S, Sharghi Y (2019) A new inversion method using a modified bat algorithm for analysis of seismic refraction data in dam site investigation. JEEG 24:201–214
48. Poormirzaee R (2017) Applying bat metaheuristic algorithm for building shear wave velocity models from surface wave dispersion curves, In: 23rd European meeting of environmental and engineering geophysics, Sweden, p 1–5
49. Rao AD, Babu RHV (1983) Quantitative interpretation of self potential anomalies due to two-dimensional sheet-like bodies. Geophysics 48:1659–1664
50. Revil A, Jardani J, Dupont JP (2008) Reply to comment by D. Gibert and P. Sailhac on "Self-potential signals associated with preferential groundwater flow pathways in sinkholes". J Geophys Res. https://doi.org/10.1029/2007JB005396
51. Rittgers JB, Revil A, Karaoulis M, Mooney MA, Slater LD, Atekwana EA (2013) Self-potential signals generated by the corrosion of buried metallic objects with application to contaminant plumes. Geophysics 78(5):EN65–EN82. https://doi.org/10.1190/geo2013-0033.1
52. Santos FAM (2010) Inversion of self-potential of idealized bodies’ anomalies using particle swarm optimization. Comput Geosci 36:1185–1190
53. Shao Z, Wang D, Wang Y, Zhong X, Zhong Y, Song W (2017) Experimental study of the self-potential anomaly caused by coal fires. J Appl Geophys 145:124–132
54. Sharma SP, Biswas A (2013) Interpretation of self-potential anomaly over a 2D inclined structure using very fast simulated-annealing global optimization—an insight about ambiguity. Geophysics 78(3):WB3–WB15
55. Sharma SP, Kaikkonen P (1998) Two-dimensional non-linear inversion of VLF-R data using simulated annealing. Geophys J Int 133:649–668
56. Sill WR (1983) Self-potential modeling from primary flows. Geophysics 48:76–86
57. Soueid Ahmed A, Jardani A, Revil A, Dupont JP (2013) SP2DINV: A 2D forward and inverse code for streaming potential problems. Comput Geosci 59:9–16
58. Soueid Ahmed A, Jardani A, Revil A, Dupont JP (2016) Specific storage and hydraulic conductivity tomography through the joint inversion of hydraulic heads and self-potential data. Adv Water Resour 89:80–90
59. Srivastava S, Agarwal BNP (2009) Interpretation of self-potential anomalies by enhanced local wave number technique. J Appl Geophys 68:259–268
60. Srivastava S, Datta D, Agarwal BN, Mehta S (2014) Applications of Ant Colony Optimization in determination of source parameters from total gradient of potential fields. Near Surf Geophys 12(3):373–390. https://doi.org/10.1002/nsg.123001
61. Stoll J, Bigalke J, Grabner EW (1995) Electrochemical modelling of self-potential anomalies. Surv Geophys 16:107–120
62. Sundararajan N, Srinivasa Rao P, Sunitha V (1998) An analytical method to interpret self-potential anomalies caused by 2D inclined sheets. Geophysics 63:1551–1555
63. Sungkono, (2020) An efficient global optimization method for self-potential data inversion using micro-differential evolution. J Earth Syst Sci 129:178
64. Vichabian Y, Morgan FD (2002) Self potentials in cave detection. Lead Edge 21:866–871
65. Wynn JC, Sherwood SI (1984) The self-potential (SP) method: an inexpensive reconnaissance archaeological mapping tool. J Field Archaeol 11:195–204
66. Xie J, Cui Y, Niu Q (2021) Coupled inversion of hydraulic and self-potential data from transient outflow experiments to estimate soil petrophysical properties. Vadose Zone J 20:e20157
67. Yang XS (2010) A new metaheuristic bat-inspired algorithm, In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, p 65-74
68. Yang XS, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspir Com 5:141–149
69. Yüngül S (1950) Interpretation of spontaneous polarization anomalies caused by spherical ore bodies. Geophysics 15:237–246

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-88a88c7c-5977-470d-92f0-69f92f76bacb