Abstract
Groundwater flow in the Grasberg open-pit mine is governed by fractured flow media. Groundwater modeling in fractured media requires detailed hydraulic conductivity (K) value distribution to illustrate hydrogeological conditions of the Grasberg open-pit mine properly. The value of K is estimated by using the hydraulic conductivity (HC) system method based on rock quality designation (RQD), lithology permeability index (LPI), depth index (DI), and gouge content designation (GCD) data. Accordingly, all parameters must be available, but in this case, this information are only partially available. This paper proposes a method to solve this problem with artificial neural network (ANN) through a five approach scenario to find the K value with a model of incomplete empirical parameters. The artificial neural network back propagation (ANNBP) method is used to estimate the distributed K value based on the distributed observed RQD and LPI, as shown in the block model. This paper uses five optimum architectural schemes consisting of learning rate selection, momentum coefficient, number of nodes, number of hidden layers, activation method (sigmoid, tangent hyperbolic and Gaussian). Verification of the results of the K value is approached by the statistical approaches determination coefficient (R2), correlation coefficient (R), standard error of estimate (SEE), root-mean-squared (RMS), and normal root mean square (NRMS). ANNBP modeling used data for training and testing based on packer test and slug test as many as 49 points. Through five scenarios, it was found that scenario with learning rates of 10–5, momentum coefficient 0.1, number of nodes 10, number of hidden layer 5, with hyperbolic tangent activation methods is the most optimum result with R2 = 0.822, the accuracy of predicting/validating results is expressed by using R2 = 0.863, with R = 0.929, and the error percentage = 3.67%. The K value prediction results are used as an input for groundwater modeling. The modeling results show a significant correlation with high validity between the data extracted from the model and field observation. In a 3D model, the K value distribution is similar to the field RQD data distribution. Furthermore, the K value distribution results are clustered in order to understand the relationship between geological as well as geotechnical data. The highest K values are found in volcanic breccia rocks (DLMVB), volcanic sediments (DLMVS), volcanic andesite (DLMVA) and quaternary glacial till. The geological condition is confirmed by the average rock RQD value between 12.4 and 39.6%.
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References
Silaen H, Pramuji, Ginting A, Widyanto D, Waromi I (2011) Hydrogeological and pore water pressure characterization at south west sector of Grasberg open pit, Papua. In: Proceedings JCM Makassar the 36th HAGI and 40th IAGI annual convention and exhibition
Leech S, McGann M (2007) Open pit slope depressurization using horizontal drains—a case study newmont. https://www.imwa.info/docs/imwa_2008/IMWA2008_035_Leech.pdf. Download on 16 July 2014
Widodo LE, Cahyadi TA, Syihab Z, Notosiswoyo S, Iskandar I, Rustamaji H (2018) Development of drain hole design optimisation: a conceptual model for open pit mine slope drainage system with fractured media using a multi-stage genetic algorithm. Environ Earth Sci 77:721
Rahardjo H, Hritzuk KJ, Leong EC, Rezaur RB (2003) Effectiveness of horizontal drains for slope stability. Eng Geol 69:295–308
Cahyadi TA, Widodo LE, Syihab Z, Notosiswoyo S, Widijanto E (2017) Hydraulic conductivity modelling of fractured rock at grasberg surface mine, Papua-Indonesia. J Eng Sci Technol 49(1):37–57
Hsu S, Lo H, Chi S, Ku C (2011) Rock mass hydraulic conductivity estimated by two empirical models. In: Dikinya O (ed) Developments in hydraulic conductivity research. InTech, New York, pp 134–158
Lavenue AM, Pickens JF (1992) Application of a coupled adjoin sensitivity and kriging approach to calibrate a groundwater flow model. Water Resour Res 28(6):1543–1569
McKinney DC, Loucks DP (1992) Network design for predicting groundwater contamination. Water Resour Res 28(1):133–147
Eggleston JR, Rojstaczer SA, Peirce JJ (1996) Identification of hydraulic conductivity structure in sand and gravel aquifer: Cape Cod data set. Water Resour Res 32(5):1209–1222
Fabbri P (1997) Transmissivity in the geothermal Euganean basin: a geostatistical analysis. Ground Water 35(5):881–887
Iskandar I, Koike K (2011) Distinguishing potential sources of arsenic released to groundwater around a fault zone containing a minesite. Environ Earth Sci 63:595–608
Wu J, Zheng C, Chien CC (2005) Cost effective sampling network design for contaminant plume monitoring under general hydrogeological conditions. J Contam Hydrol 77:41–65
Maedeh PA, Mehrdadi N, Bidhendi GRN, Abyaneh HZ (2013) Application of artificial neural network to predict total dissolved solids variations in groundwater of Tehran Plain, Iran. Int J Environ Sustain 2(1):10–20
Nasr M, Zahran HF (2014) Using of pH as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network. Egypt J Aquat Res 40:111–115
Mohammadi AA, Ghaderpoori M, Yousefi M, Rahmatipoor M, Javan S (2016) Prediction and modeling of fluoride concentrations in groundwater resources using an artificial neural network: a case study in Khaf. Environ Health Eng Manag J 3(4):217–224
Ghose D, Das U, Roy P (2018) Modelling response of runoff and evapotranspiration for predicting water table depth in arid region using dynamic recurrent neural network. Groundw Sustain Dev. https://doi.org/10.1016/j.gsd.2018.01.007
Sun J, Zhao Z, Zhang Y (2011) Determination of three dimensional hydraulic conductivities using a combined analytical/neural network model. Tunn Undergr Space Technol 26:310–319
Herrera E, Garfias J (2013) Characterizing a fractured aquifer in Mexico using geological attributes related to open pit groundwater. Hydrogeol J 21:1323–1338
Mayer JM, Allen DM, Gibson HD, Mackle DC (2014) Application of statistical approach to analyze geological, geotechnical and hydrogeological data at a fracture-rock mine site in Northern Canada. Hydrogeol J. https://doi.org/10.1007/s10040-014-1140-2
Yang FR, Lee CH, Kung WJ, Yeh HF (2009) The impact of tunnelling construction on the hydrogeological environment of “Tseng-Wen Reservoir Transbasin Diversion Project Taiwan. Eng Geol 103:39–58
MacDonald G, Arnold L (1994) Geological and geochemical zoning of the grasberg igneous complex, Irian Jaya, Indonesia. J Geochem Explor 50:143–178
McDowell F (1996) Pliocene Cu-Au bearing igneous intrusions of the Gunung Bijih District, Irian Jaya, Indonesia, K-Ar geochronology. J Geol 104:327–340
Suwardi E, Margotomo W (1998) Geology and hydrothermal characteristics zone alteration, mineralization deposition in contacts intrusion in grasberg copper-gold porphyry-Irian Jaya. In: Bahasa Indonesia, Proceeding IAGI XXVII
Sapiie B (1994) Structural geologic studies along heat road and Grasberg area in the Ertsberg (Gunung Bijih) Mining District, Irian Jaya. Department of Geological Sciences University of Texas at Austin, Indonesia
Antoro B, Margotomo W, Perdana A, Widijanto E, Wiwoho N, Ginting AP, Santosa RGI, Pramuji, Silaen H, Setyadi H, Iribaram F, Mundu S, Garjito W, Sumarwan F, Rohmadi A, Setiadi T, Afwan A, Asrizal, Pahala AR, Prasetyo N (2011) Geology and geotechnic grasberg open pit mining PTFI. Aksara Buana (in Bahasa Indonesia)
Deere DU, Hendron AJ, Patton FD, Cording EJ (1967) Design of surface and near surface construction in rock. In: Proceedings of 8th U.S. symposium. Rock mechanics, AIME, New York, pp 237–302
Schaap MG, Bouten W (1996) Modeling water retention curves of sandy soils using neural networks. Water Resour 32(10):3033–3040
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: foundation, chap 88, vol 1. MIT Press, Cambridge, MA, pp 318–362
Jung SK, McDonald K (2011) Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization. BMC Bioinform 12(1):340
Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice Hall advanced reference series. Prentice Hall, Englewood-Cliffs
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323
Widodo LE, Cahyadi TA, Notosiswoyo S, Widijanto E (2016) Application of clustering system to analyse geological, geotechnical, and hydrogeological data base according to HC-system approach. In: 9th Asian rock mechanic symposium, pp 1175–1183
Warren JE, Root PJ (1963) Behaviour of naturally fractured reservoirs. Sot Per Eng J Trans AIME 228:245–255
Singhal BBS, Gupta RP (1999) Applied hydrogeology of fractured rocks. Kluwer Academic Publishers, Dordrecht
Hydrogeological, Inc (2000) Visual Modflow Software (version 2.8.1)
Guilford JP (1956) Fundamental statistic in psychology and education. McGraw-Hill Book Company, New York
Sarma DD (2009) Goestatistics with application in earth science, 2nd edn. Springer, Berlin
Wei ZQ, Egger P, Descoeudres F (1995) Permeability predictions for jointed rock masses. Int J Rock Mech Miner Sci Geomech 32:251–326
Remy N, Boucher A (2011) SGeMS v2.5b. https://sgems.sourceforge.net/
Pramuji, Silaen H, Ginting A, Widijanto E (2012) Local geology model refinement for dewatering target selection of grasberg open pit mine, Papua. In: The 41st IAGI annual convention and exhibition
Widodo LE, Cahyadi TA, Syihab Z, Notosiswoyo S, Rustamaji HC, Iskandar I (2018) Development of drain hole design optimisation: a conceptual model for open pit mine slope drainage system with fractured media using a multi-stage genetic algorithm. Environ Earth Sci 77:721
Acknowledgements
The authors would like to thank the Research Institution and Community Services of Bandung Institute of Technology for P3MI 2017, 2018, 2019 research funding and UPN “Veteran” Yogyakarta for the support; Dr Amega for being a discussion partner in developing the simulation and optimization model; Mr Eman Widijanto, manager of Grasberg GeoEngineering, for allowing the authors to study the dewatering system at PTFI; and Dr Koesnaryo of the Department of Mining Engineering, UPN ‘Veteran’ Yogyakarta, for allowing the authors to use the visual license of the Visual MODFLOW 2.8.1 for this research. The authors are also grateful to Respati Muhammad, Aldin Ardian and Dr Agus Haris Widayat for proofreading this manuscript.
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Cahyadi, T.A., Syihab, Z., Widodo, L.E. et al. Analysis of hydraulic conductivity of fractured groundwater flow media using artificial neural network back propagation. Neural Comput & Applic 33, 159–179 (2021). https://doi.org/10.1007/s00521-020-04970-z
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DOI: https://doi.org/10.1007/s00521-020-04970-z