Abstract
Fracture or fault can control several geological or geophysical events and exploration, where the fault associated with mineralization or fluid flow can be used as a source for Self-Potential (SP) anomaly. The 2-D inclined sheet can be used for modeling for fault interpretation using SP data. In this paper, the improved crow search algorithm (ICSA) using Levy flight is proposed for SP data inversion in determining SP model parameters. In order to evaluate ICSA, the ICSA is compared with standard crow search algorithm (CSA) for determining synthetic SP data that contains multiple anomalies with inclined sheet type structures. It was found that CSA is more explorative than ICSA, and both algorithms can estimate the posterior distribution model (PDM) of SP data. Using uncertainty analysis within the applied threshold in the objective function, both algorithms are reliable to determine PDM. Furthermore, ICSA is tested and implemented to both synthetic and field of SP anomalies for providing the posterior distribution model of SP parameters. The experimental results demonstrate that the ICSA is feasible and effective for determining model parameters and its uncertainty of mono- and multi-SP anomalies. Furthermore, estimating both model parameter and its uncertainty are sufficient for validation with previous researchers. Finally, the interpretation of multiple anomalies in SP anomaly crossing the Grindulu Fault in Pacitan, East Java, Indonesia, is analyzed.
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11 January 2021
In subtitle “Tambakrejo SP anomaly, Indonesia”, page 710, for describing Fig. 13 written: “Besides those two faults, the inversion data result also produce four other minor faults with different position (Fig. 13 lower panel)” should be revised as “Besides those two faults, the inversion data result also produce seven other minor faults with different position (Fig. 13 lower panel)”.
References
Abdelazeem M, Gobashy M, Khalil MH, Abdrabou M (2019) A complete model parameter optimization from self-potential data using Whale algorithm. J Appl Geophys 170:103825. https://doi.org/10.1016/j.jappgeo.2019.103825
Agarwal A, Chandra A, Shalivahan S, Singh RK (2018) Grey wolf optimizer: a new strategy to invert geophysical data sets. Geophys Prospect 66:1215–1226. https://doi.org/10.1111/1365-2478.12640
Alzwar M, Akbar N, Bachri S (1992) Peta Geologi Lembar Garut dan Pameungpeuk, Jawa. Pusat Penelitian dan Pengembangan Geologi, Bandung
Arora T, Linde N, Revil A, Castermant (2007) Non-intrusive characterization of the redox potential of landfill leachate plumes from self-potential data. J Contam Hydrol 92:274–292
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001
Balkaya Ç (2013) An implementation of differential evolution algorithm for inversion of geoelectrical data. J Appl Geophys 98:160–175. https://doi.org/10.1016/j.jappgeo.2013.08.019
Biswas A (2016) A comparative performance of least-square method and very fast simulated annealing global optimization method for interpretation of self-potential anomaly over 2-D inclined sheet type structure. J Geol Soc India 88:493–502. https://doi.org/10.1007/s12594-016-0512-8
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. https://doi.org/10.1016/j.oregeorev.2017.10.024
Biswas A (2018) Inversion of amplitude from the 2-D analytic signal of self-potential anomalies. Minerals. https://doi.org/10.5772/intechopen.79111
Biswas A, Sharma SP (2014a) Optimization of self-potential interpretation of 2-D inclined sheet-type structures based on very fast simulated annealing and analysis of ambiguity. J Appl Geophys 105:235–247. https://doi.org/10.1016/j.jappgeo.2014.03.023
Biswas A, Sharma SP (2014b) Resolution of multiple sheet-type structures in self-potential measurement. J Earth Syst Sci 123:809–825. https://doi.org/10.1007/s12040-014-0432-1
Biswas A, Sharma SP (2015) Interpretation of self-potential anomaly over idealized bodies and analysis of ambiguity using very fast simulated annealing global optimization technique. Near Surface Geophys 13:179–195. https://doi.org/10.3997/1873-0604.2015005
Biswas A, Sharma SP (2016) Integrated geophysical studies to elicit the subsurface structures associated with Uranium mineralization around South Purulia Shear Zone, India: a review. Ore Geol Rev Metallogeny Indian Shield 72:1307–1326. https://doi.org/10.1016/j.oregeorev.2014.12.015
Biswas A, Sharma SP (2017) Interpretation of self-potential anomaly over 2-D inclined thick sheet structures and analysis of uncertainty using very fast simulated annealing global optimization. Acta Geod Geophys 52:439–455. https://doi.org/10.1007/s40328-016-0176-2
Biswas A, Mandal A, Sharma SP, Mohanty WK (2014) Delineation of subsurface structures using self-potential, gravity, and resistivity surveys from South Purulia Shear Zone, India: Implication to uranium mineralization. Interpretation 2:T103–T110. https://doi.org/10.1190/INT-2013-0170.1
Chengliang D, Yixiang C, Yongsheng Z, Bo C (2020) Application of a mathematical method in geophysics: separating anomalies of horizontal gradients of the spontaneous potential field based on first-order difference. J Appl Geophys 176:104009. https://doi.org/10.1016/j.jappgeo.2020.104009
Colominas MA, Schlotthauer G, Torres ME (2014) Improved complete ensemble EMD: a suitable tool for biomedical signal processing. Biomed Signal Process Control 14:19–29
Di Maio R, Piegari E, Rani P, Avella A (2016a) Self-Potential data inversion through the integration of spectral analysis and tomographic approaches. Geophys J Int 206:1204–1220. https://doi.org/10.1093/gji/ggw200
Di Maio R, Rani P, Piegari E, Milano L (2016b) Self-potential data inversion through a Genetic-Price algorithm. Comput Geosci 94:86–95. https://doi.org/10.1016/j.cageo.2016.06.005
Di Maio R, Piegari E, Rani P (2017) Source depth estimation of self-potential anomalies by spectral methods. J Appl Geophys 136:315–325. https://doi.org/10.1016/j.jappgeo.2016.11.011
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. https://doi.org/10.1016/j.jappgeo.2019.02.004
Diambama AD, Anggraini A, Nukman M, Lühr B-G, Suryanto W (2019) Velocity structure of the earthquake zone of the M6.3 Yogyakarta earthquake 2006 from a seismic tomography study. Geophys J Int 216:439–452. https://doi.org/10.1093/gji/ggy430
Diaz P, Perez-Cisneros M, Cuevas E, Avalos O, Gálvez J, Hinojosa S, Zaldivar D (2018) An improved crow search algorithm applied to energy problems. Energies. https://doi.org/10.3390/en11030571
Ekinci YL, Balkaya Ç, Göktürkler G (2020) Global optimization of near-surface potential field anomalies through metaheuristics. In: Biswas A, Sharma SP (eds) Advances in modeling and interpretation in near surface geophysics, Springer Geophysics. Springer, Cham, pp 155–188. https://doi.org/10.1007/978-3-030-28909-6_7
Essa KS (2020) Self potential data interpretation utilizing the particle swarm method for the finite 2D inclined dike: mineralized zones delineation. Acta Geod Geophys. https://doi.org/10.1007/s40328-020-00289-2
Fajriani, Srigutomo W, 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
Fernández-Martínez JL, García Gonzalo E, Fernández Álvarez JP, Kuzma HA, Menéndez-Pérez CO (2010a) PSO: a powerful algorithm to solve geophysical inverse problems: Application to a 1D-DC resistivity case. J Appl Geophys 71:13–25. https://doi.org/10.1016/j.jappgeo.2010.02.001
Fernández-Martínez JL, García-Gonzalo E, Fernández-Muñiz Z, Mariethoz G, Mukerji T (2010b) Posterior sampling using particle swarm optimizers and model reduction techniques. Int J Appl Evolu Comput 1:27–48. https://doi.org/10.4018/IJAEC
Fernández-Martínez JL, Fernández-Muñiz Z, Pallero JLG, Pedruelo-González LM (2013) From Bayes to Tarantola: new insights to understand uncertainty in inverse problems. J Appl Geophys 98:62–72. https://doi.org/10.1016/j.jappgeo.2013.07.005
Fernández-Muñiz Z, Hassan K, Fernández-Martínez JL (2019) Data kit inversion and uncertainty analysis. J Appl Geophys. https://doi.org/10.1016/j.jappgeo.2018.12.022
Giang NV, Kochanek K, Vu NT, Duan NB (2018) Landfill leachate assessment by hydrological and geophysical data: case study NamSon, Hanoi. Vietnam. J Mater Cycles Waste Manag 20:1648–1662. https://doi.org/10.1007/s10163-018-0732-7
Gobashy M, Abdelazeem M, Abdrabou M, Khalil MH (2019) Estimating model parameters from self-potential anomaly of 2D inclined sheet using whale optimization algorithm: applications to mineral exploration and tracing shear zones. Nat Resour Res. https://doi.org/10.1007/s11053-019-09526-0
Gultaf H, Sapiie B, Syaiful M, Bahtiar A, Fauzan AP (2015) Paleostress analysis of Grindulu Fault in Pacitan and surrounding area its implication to regional tectonic of East Java. In: Proc. 39th Ann. Conv. Indon. Petroleum Assoc. (IPA). Presented at the 39th Ann. Conv. Indon. Petroleum Assoc. (IPA), IPA, Jakarta, pp. IPA15-G-059, 34p
Hardman RFP, Booth JE (1991) The significance of normal faults in the exploration and production of North Sea hydrocarbons. Geol Soc Lond Special Publ 56:1–13. https://doi.org/10.1144/GSL.SP.1991.056.01.01
Hussain K, Salleh MNM, Cheng S, Shi Y (2019) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl 31:7665–7683. https://doi.org/10.1007/s00521-018-3592-0
Ikard SJ, Revil A, Schmutz M, Karaoulis M, Jardani A, Mooney M (2014) Characterization of focused seepage through an earthfill dam using geoelectrical methods. Groundwater 52:952–965. https://doi.org/10.1111/gwat.12151
Laby DA, Sungkono, Santosa BJ, Bahri AS (2016) RR-PSO: fast and robust algorithm to invert Rayleigh waves dispersion. Contemp Eng Sci 9:735–741. https://doi.org/10.12988/ces.2016.6685
Li X, Yin M (2012) Application of differential evolution algorithm on self-potential data. PLoS ONE. https://doi.org/10.1371/journal.pone.0051199
Mauri G, Williams-Jones G, Saracco G (2011) MWTmat—application of multiscale wavelet tomography on potential fields. Comput Geosci Geospatial Cyberinfrastruct Polar ResearchGeospatial Cyberinfrastruct Polar Res 37:1825–1835. https://doi.org/10.1016/j.cageo.2011.04.005
Mehanee SA (2014) An efficient regularized inversion approach for self-potential data interpretation of ore exploration using a mix of logarithmic and non-logarithmic model parameters. Ore Geol Rev 57:87–115. https://doi.org/10.1016/j.oregeorev.2013.09.002
Mehanee SA (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. https://doi.org/10.1186/s40623-014-0174-y
Monteiro Santos FA (2010) Inversion of self-potential of idealized bodies’ anomalies using particle swarm optimization. Comput Geosci 36:1185–1190. https://doi.org/10.1016/j.cageo.2010.01.011
Monteiro Santos FA, Almeida EP, Castro R, Nolasco R, Mendes-Victor L (2002) A hydrogeological investigation using EM34 and SP surveys. Earth Planet Sp 54:655–662. https://doi.org/10.1186/BF03353053
Pace B, Visini F, Scotti O, Peruzza L (2018) Preface: linking faults to seismic hazard assessment in Europe. Natural Hazards Earth Syst Sci 18:1349–1350. https://doi.org/10.5194/nhess-18-1349-2018
Ramadhani I, Sungkono S (2019) A new approach to model parameter determination of self-potential data using memory-based hybrid dragonfly algorithm. Int J Adv Sci Eng Inf Technol 9:1772–1782
Revil A, Jardani A (2013) The Self-Potential Method: Theory and Applications in Environmental Geosciences. Cambridge University Press, Cambridge
Rilling G, Flandrin P, Goncalves P (2005) Empirical mode decomposition, fractional Gaussian noise and Hurst exponent estimation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP’05). Presented at the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP’05), p. iv/489-iv/492 Vol. 4. https://doi.org/10.1109/ICASSP.2005.1416052
Roy IG (2019) On studying flow through a fracture using self-potential anomaly: application to shallow aquifer recharge at Vilarelho da Raia, northern Portugal. Acta Geod Geophys 54:225–242. https://doi.org/10.1007/s40328-019-00256-6
Samodra H, Gafoer S, Tjokrosapoetro S (1992) Peta Geologi lembar Pacitan. Jawa, Pusat Penelitian dan Pengembangan Geologi
Saribudak M, Hawkins A (2019) Hydrogeopysical characterization of the Haby Crossing fault, San Antonio, Texas, USA. J Appl Geophys 162:164–173. https://doi.org/10.1016/j.jappgeo.2019.01.009
Sharma PV (1997) Environmental and engineering geophysics. Cambridge University Press, Cambridge
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:WB3–WB15. https://doi.org/10.1190/geo2012-0233.1
Sree Ranjini KS, Murugan S (2017) Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Syst Appl 83:63–78. https://doi.org/10.1016/j.eswa.2017.04.033
Stoll J, Bigalke J, Grabner EW (1995) Electrochemical modelling of self-potential anomalies. Surv Geophys 16:107–120. https://doi.org/10.1007/BF00682715
Sundararajan N, Rao PS, Sunitha V (1998) An analytical method to interpret self-potential anomalies caused by 2-D inclined sheets. Geophysics 63:1551–1555. https://doi.org/10.1190/1.1444451
Sungkono (2020a) Robust interpretation of single and multiple self-potential anomalies via flower pollination algorithm. Arab J Geosci. https://doi.org/10.1007/s12517-020-5079-4
Sungkono (2020b) An efficient global optimization method for self-potential data inversion using micro-differential evolution. J Earth Syst Sci 129:178. https://doi.org/10.1007/s12040-020-01430-z
Sungkono, Santosa BJ (2015) Differential evolution adaptive metropolis sampling method to provide model uncertainty and model selection criteria to determine optimal model for rayleigh wave dispersion. Arab J Geosci 8:7003–7023. https://doi.org/10.1007/s12517-014-1726-y
Sungkono, Warnana DD (2018) Black hole algorithm for determining model parameter in self-potential data. J Appl Geophys 148:189–200. https://doi.org/10.1016/j.jappgeo.2017.11.015
Sungkono, Bahri AS, Warnana DD, Monteiro Santos FA, Santosa BJ (2014a) Fast, simultaneous and robust VLF-EM Data Denoising And Reconstruction Via Multivariate Empirical Mode Decomposition. Comput Geosci 67:125–137. https://doi.org/10.1016/j.cageo.2014.03.007
Sungkono, Husein A, Prasetyo H, Bahri AS, Monteiro Santos FA, Santosa BJ (2014b) The VLF-EM imaging of potential collapse on the LUSI Embankment. J Appl Geophys 109:218–232. https://doi.org/10.1016/j.jappgeo.2014.08.004
Sungkono, Santosa BJ, Bahri AS, Monteiro Santos FA, Iswahyudi (2017) Application of multivariate empirical mode decomposition in the VLF-EM data to identify underground river. Adv Data Sci Adapt Anal 9:1650011–1-23. https://doi.org/10.1142/S2424922X1650011X
Sungkono, Feriadi Y, Husein A, Prasetyo H, Charis M, Irawan D, Rochman JPGN, Bahri AS, Santosa BJ (2018) Assessment of Sidoarjo mud flow embankment stability using very low frequency electromagnetic method. Environ Earth Sci. https://doi.org/10.1007/s12665-018-7333-6
Wolpert DH, Macready WG (1997) No Free Lunc Theorems for optimization. IEEE Trans Evol Comput 1:67–82
Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation. Lecture Notes in Computer Science. Springer, Berlin, pp 240–249
Zlotnicki J, Nishida Y (2003) Review on morphological insights of self-potential anomalies on volcanoes. Surv Geophys 24:291–338. https://doi.org/10.1023/B:GEOP.0000004188.67923.ac
Acknowledgements
Authors would like to thank students on the Geophysics Laboratory, Department of Physics, Institut Teknologi Sepuluh Nopember (ITS), for their help in the data acquisition. This work is supported by the Institute for Research and Community Services of ITS (Grant No: 1060/PKS/ITS/2019).
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Appendix A: Pseudo-code of ICSA or CSA
Appendix A: Pseudo-code of ICSA or CSA
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Haryono, A., Sungkono, Agustin, R. et al. 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 (2020). https://doi.org/10.1007/s40328-020-00321-5
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DOI: https://doi.org/10.1007/s40328-020-00321-5