Citation: | Xue Peipei, Wen Zhang, Liang Xing. Application and development trend of geostatistics in the research of spatial variation of aquifer parameters[J]. Bulletin of Geological Science and Technology, 2022, 41(1): 209-222. doi: 10.19509/j.cnki.dzkq.2022.0015 |
[1] |
施小清, 吴吉春, 袁永生, 等. 渗透系数空间变异性研究[J]. 水科学进展, 2005, 16(2): 210-215. doi: 10.3321/j.issn:1001-6791.2005.02.010
Shi X Q, Wu J C, Yuan Y S, et al. Study on spatial variability of permeability coefficient[J]. Progress in Water Science, 2005, 16(2): 210-215(in Chinese with English abstract). doi: 10.3321/j.issn:1001-6791.2005.02.010
|
[2] |
Freeze R A. A stochastic-conceptual analysis of one-dimensional groundwater flow in nonuniform homogeneous media[J]. Water Resources Research, 1975, 11(5): 725-741. doi: 10.1029/WR011i005p00725
|
[3] |
苗添升, 卢文喜, 欧阳琦, 等. 地下水数值模拟的不确定性分析在水质预测中的应用[J]. 水电能源科学, 2016, 8: 20-23, 44. https://www.cnki.com.cn/Article/CJFDTOTAL-SDNY201608005.htm
Miao T S, Lu W X, Ouyang Q, et al. Application of uncertainty analysis of groundwater numerical simulation in water quality prediction[J]. Water Resources and Power, 2016, 8: 20-23, 44(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SDNY201608005.htm
|
[4] |
彭伏, 常勇, 郑秀清, 等. 地下水模型参数不确定性对晋祠泉流量预测的影响[J]. 水电能源科学, 2018, 10: 53-57. https://www.cnki.com.cn/Article/CJFDTOTAL-SDNY201810013.htm
Peng F, Chang Y, Zheng X Q, et al. Influence of uncertainty of groundwater model parameters on flow prediction of Jinci Spring[J]. Water Resources and Power, 2018, 10: 53-57(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SDNY201810013.htm
|
[5] |
Ritzi R W, Dai Z, Dominic D F, et al. Spatial correlation of permeability in cross-stratified sediment with hierarchical architecture[J]. Water Resources Research, 2004, 40: W03513.
|
[6] |
Neuman S P. Blueprint for perturbative solution of flow and transport in strongly heterogeneous composite media, using fractal and variational multiscale decomposition[J]. Water Resources Research, 2006, 42: W06D04.
|
[7] |
杰夫·卡尔斯, 陈军斌, 程国建, 等. 石油地质统计学[M]. 北京: 石油工业出版社, 2014.
Karls J, Chen J B, Cheng G J, et al. Petroleum geostatistics[M]. Beijing: Petroleum Industry Press, 2014(in Chinese).
|
[8] |
杰夫·卡尔斯, 程国建, 李小和, 等. 地球科学中的不确定性建模[M]. 北京: 石油工业出版社, 2016.
Karls J, Cheng G J, Li X H, et al. Uncertainty modeling in earth sciences[M]. Beijing: Petroleum Industry Press, 2016(in Chinese).
|
[9] |
Matheron G. Traitéde géostatistique appliquée[M]. Paris: Editions Technip, 1962.
|
[10] |
Krige D G. A statistical approach to some basic mine valuation problems on the Witwatersrand[J]. Journal of the Southern African Institute of Mining and Metallurgy, 1951, 52(6): 119-139.
|
[11] |
Matheron G. Principles of geostatistics[J]. Economic Geology, 1963, 58(8): 1246-1266. doi: 10.2113/gsecongeo.58.8.1246
|
[12] |
Journel A G, Huijbregts C J. Mining geostatistics[M]. London: Academic Press, 1978.
|
[13] |
Matheron G. The theory of regionalized variables and its application[M]. [S. l.]: Les Cahiers Du Center De Morphologie Mathématique De Fontainebleau, 1971.
|
[14] |
Matheron G. Le krigeage universe[M]. Paris: École Nationale Supérieure des Mines de Paris, 1969.
|
[15] |
Verly G, David M, Journel A G, et al. Geostatistics for natural resources characterization[M]. Dordrecht: D. Reidel Publishing Company, 1984.
|
[16] |
Journel A G. Nonparametric estimation of spatial distributions[J]. Journal of the International Association for Mathematicl Geology, 1983, 15(3): 445-468. doi: 10.1007/BF01031292
|
[17] |
Matheron G. Recherche de simplification dans un problème de cokrigeage[M]. Punblication N-628. Fontainableau: Centre de Géostatistique, Ecole des Mines de Paris, 1979.
|
[18] |
Matheron G. Pourune analyse krigeante des données régionalisées[M]. [S. l.]: Centre de Geostatistique, Report N-732, Fontainebleau, 1982.
|
[19] |
Remy N, Boucher A, Wu J. Applied geostatistics with SGeMS: A user's guide[M]. Cambridge: Cambridge University Press, 2009.
|
[20] |
Soares A. Geostatistics Tróia '92[M]. Dordrecht: Kluwer Academic Publishers, 1993.
|
[21] |
Strebelle S B. Sequential simulation drawing structures from training images[D]. State of California: Stanford University, 2000.
|
[22] |
Strebelle S B, Journel A G. Reservoir modeling using multiple-point statistics[C]//Anon. SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 2001. [S. l.]: [s. n.], 2001.
|
[23] |
Shirangi M G. Closed-loop field development with multipoint geostatistics and statistical performance assessment[J]. Journal of Computational Physics, 2019, 390(1): 249-264.
|
[24] |
Goovaerts P. Geostatistics for natural resources evaluation[M]. Oxford: Oxford University Press, 1997.
|
[25] |
Journel A, Isaaks E. Conditional indicator simulation: Application to a Saskatchewan uranium deposit[J]. Journal of the International Association for Mathematical Geology, 1984, 16(7): 685-718. doi: 10.1007/BF01033030
|
[26] |
Scheidt C, Li L, Caers J. Quantifying uncertainty in subsurface systems[M]. New York: American Geophysical Union and John Wiley & Sons, Inc., 2018.
|
[27] |
刘晓晨, 陆永潮, 杜学斌, 等. 层序格架约束下的地质统计学反演在薄砂体预测中的应用[J]. 地质科技通报, 2020, 39(3): 99-109. doi: 10.19509/j.cnki.dzkq.2020.0311
Liu X C, Lu Y C, Du X B, et al. Application of geostatistical inversion constrained by sequence framework in thin bedded sand body prediction[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 99-109(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2020.0311
|
[28] |
Chen X H, Song J X, Wang W K, et al. Spatial variability of specific yield and vertical hydraulic conductivity in a highly permeable alluvial aquifer[J]. Journal of Hydrology, 2010, 388: 379-388. doi: 10.1016/j.jhydrol.2010.05.017
|
[29] |
Wang W, Wang Y, Sun Q M, et al. Spatial variation of saturated hydraulic conductivity of a loess slope in the South Jingyang Plateau, China[J]. Engineering Geology, 2018, 236(6): 70-78.
|
[30] |
Godoy V A, Zuquette L V, Gómez-Hernández J. Spatial variability of hydraulic conductivity and solute transport parameters and their spatial correlations to soil properties[J]. Geoderma, 2019, 339(1): 59-69.
|
[31] |
Christine E H, Andrew T F, Chris R R, et al. Spatial and temporal variations in streambed hydraulic conductivity quantified with time series thermal methods[J]. Journal of Hydrology, 2010, 389: 276-288. doi: 10.1016/j.jhydrol.2010.05.046
|
[32] |
Dewandel B, Jeanpert J, Ladouche B, et al. Inferring the heterogeneity, transmissivity and hydraulic conductivity of crystalline aquifers from a detailed water-table map[J]. Journal of Hydrology, 2017, 550: 118-129. doi: 10.1016/j.jhydrol.2017.03.075
|
[33] |
Benoit S, Ghysels G, Gommers K, et al. Characterization of spatially variable riverbed hydraulic conductivity using electrical resistivity tomography and induced polarization[J]. Hydrogeology Journal, 2019, 27: 395-407. doi: 10.1007/s10040-018-1862-7
|
[34] |
Schilling O S, James Irvine D, Franssen H J H, et al. Estimating the spatial extent of unsaturated zones in heterogeneous river aquifer systems[J]. Water Resources Research, 2017, 53(10): 583-602.
|
[35] |
Berton G. Comparison between two interpolation methods: Kriging and EPH[J]. International Conference on Mathematical Modelling in Physical Sciences, 2018, 1141(1): 27-31.
|
[36] |
Mishra P N, Scheuermann A, Li L. Evaluation of hydraulic conductivity functions of saturated soft soils[J]. International Journal of Geomechanics, 2020, 20(11): 04020214. doi: 10.1061/(ASCE)GM.1943-5622.0001847
|
[37] |
唐攀, 唐菊兴, 林彬, 等. 传统几何法与地质统计学法在矿产资源储量估算中的对比分析[J]. 地质科技情报, 2016, 35(1): 156-160. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201601024.htm
Tang P, Tang J X, Lin B, et al. Comparative research of traditional method and geostatistical in mineral resource/reserve calculation[J]. Geological Science and Technology Information, 2016, 35(1): 156-160(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201601024.htm
|
[38] |
Xue P P, Wen Z, Zhao D J, et al. Determination of hydraulic conductivity and its spatial variability in the Jianghan Plain using a multi-format, multi-method approach[J]. Journal of Hydrology, 2021, 594: 125917. doi: 10.1016/j.jhydrol.2020.125917
|
[39] |
Journel A G, Huijbregts C J. Mining geostatistics[M]. New York: Academic Press, 1978.
|
[40] |
杨江州, 周旭, 程东亚, 等. 贵州省不同地貌类型区的MOD16蒸散发变化特征[J]. 水土保持研究, 2019, 26(2): 216-222. https://www.cnki.com.cn/Article/CJFDTOTAL-STBY201902034.htm
Yang J Z, Zhou X, Cheng D Y, et al. Variation characteristics of MOD16 evapotranspiration in different geomorphic areas of Guizhou Province[J]. Study on Soil and Water Conservation, 2019, 26(2): 216-222(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-STBY201902034.htm
|
[41] |
Shukla K, Kumar P S, Mann G, et al. Mapping spatial distribution of particulate matter using Kriging and inverse distance weighting at supersites of megacity Delhi[J]. Sustainable Cities and Society, 2020, 54: 101997. doi: 10.1016/j.scs.2019.101997
|
[42] |
Sagar B S D, Cheng Q M, Agterberg F. Handbook of mathematical geosciences: Fifty years of IAMG[M]. Cham: Springer International Publishing AG, 2018.
|
[43] |
Pham T G, Kappas M, Huynh C V, et al. Application of ordinary Kriging and regression Kriging method for soil properties mapping in hilly region of Central Vietnam[J]. ISPRS Int. J. Geo-Inf., 2019, 8(3): 147. doi: 10.3390/ijgi8030147
|
[44] |
Zhu Q, Lin H S. Comparing ordinary Kriging and regression Kriging for soil properties in contrasting landscapes[J]. Pedosphere, 2010, 20(5): 594-606. doi: 10.1016/S1002-0160(10)60049-5
|
[45] |
Moustapha M, Bourinet J M, Guillaume B, et al. Comparative study of Kriging and support vector regression for structural engineering applications[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2018, 4(2): 04018005. doi: 10.1061/AJRUA6.0000950
|
[46] |
徐谢亲, 祝明霞. 基于GIS的江西省气温空间插值方法比较[J]. 绿色科技, 2021, 23(10): 21-24. doi: 10.3969/j.issn.1674-9944.2021.10.007
Xu X Q, Zhu M X. Comparison of spatial interpolation methods of air temperature in Jiangxi Province based on GIS[J]. Green Science and Technology, 2021, 23(10): 21-24(in Chinese with English abstract). doi: 10.3969/j.issn.1674-9944.2021.10.007
|
[47] |
Echard B, Gayton N, Lemaire M. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo simulation[J]. Structural Safety, 2011, 33(2): 145-154. doi: 10.1016/j.strusafe.2011.01.002
|
[48] |
贺辰戋, 欧阳婷萍, 彭莎莎. 广州市表层土壤磁学性质的空间插值方法比较[J]. 热带地理, 2020, 40(5): 904-918. https://www.cnki.com.cn/Article/CJFDTOTAL-RDDD202005013.htm
He C J, Ouyang T P, Peng S S. Comparison of spatial interpolation methods for magnetic properties of topsoil in Guangzhou[J]. Tropical Geography, 2020, 40(5): 904-918(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-RDDD202005013.htm
|
[49] |
王伟, 宋渊娟, 黄静, 等. 利用高压压汞实验研究致密砂岩孔喉结构分形特征[J]. 地质科技通报, 2021, 40(4): 22-30, 48. doi: 10.19509/j.cnki.dzkq.2021.0402
Wang W, Song Y J, Huang J, et al. Study on fractal characteristics of pore throat structure of tight sandstone by high pressure mercury injection experiment[J]. Bulletin of Geological Science and Technology, 2021, 40(4): 22-30, 48(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2021.0402
|
[50] |
Illman W A, Zhu J, Craig A J, et al. Comparison of aquifer characterization approaches through steady state groundwater model validation: A controlled laboratory sandbox study[J]. Water Resources Research, 2010, 46(4): 475-478.
|
[51] |
Arslan H. Spatial and temporal mapping of groundwater salinity using ordinary Kriging and indicator Kriging: The case of Bafra Plain, Turkey[J]. Agricultural Water Management, 2012, 113: 57-63. doi: 10.1016/j.agwat.2012.06.015
|
[52] |
Delbari M, Amiri M, Motlagh M B, et al. Assessing groundwater quality for irrigation using indicator Kriging method[J]. Appl. Water Sci., 2016, 6: 371-381.
|
[53] |
Lee S Y, Carle S F, Fogg G E, et al. Geologic heterogeneity and a comparison of two geostatistical models: Sequential Gaussian and transition probability-based geostatistical simulation[J]. Advances in Water Resources, 2007, 30: 1914-1932. doi: 10.1016/j.advwatres.2007.03.005
|
[54] |
Piccini C, Marchetti A, Farina R, et al. Application of indicator Kriging to evaluate the probability of exceeding nitrate contamination thresholds[J]. International Journal of Environmental Research, 2012, 6(4): 853-862.
|
[55] |
Bradaï A, Douaoui A, Bettahar N, et al. Improving the prediction accuracy of groundwater salinity mapping using indicator Kriging method[J]. Journal of Irrigation and Drainage Engineering, 2016, 142(7): 04016023. doi: 10.1061/(ASCE)IR.1943-4774.0001019
|
[56] |
徐英, 葛洲, 王娟, 等. 基于指示Kriging法的土壤盐渍化与地下水埋深关系研究[J]. 农业工程学报, 2019(1): 123-130. https://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201901016.htm
Xu Y, Ge Z, Wang J, et al. Study on the relationship between soil salinization and groundwater depth based on indicator Kriging method[J]. Journal of Agricultural Engineering, 2019, (1): 123-130(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201901016.htm
|
[57] |
Adhikary P P, Dash C J, Bej R, et al, Chandrasekharan H. Indicator and probability Kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India[J]. Environmental Monitoring and Assessment, 2011, 176: 663-676. doi: 10.1007/s10661-010-1611-4
|
[58] |
Gratiet L L, Garnier J. Recursive co-Kriging model for design of computer experiments with multiple levels of fidelity[J]. International Journal for Uncertainty Quantification, 2014, 4(5): 365-386. doi: 10.1615/Int.J.UncertaintyQuantification.2014006914
|
[59] |
Kanankege K S T, Alkhamis M A, Pheeps N B D, et al. A probability co-Kriging model to account for reporting Bias and recognize areas at high risk for Zera Mussels and Eurasian watermilfoil invasions in Minnesota[J]. Front. Vet. Sci., 2018, 4: 231. doi: 10.3389/fvets.2017.00231
|
[60] |
Mendes M P, Ribeiro L. Nitrate probability mapping in the northern aquifer alluvial system of the river Tagus(Portugal) using disjunctive Kriging[J]. Science of the Total Environment, 2010, 408(5): 1021-1034. doi: 10.1016/j.scitotenv.2009.10.069
|
[61] |
吴双红, 刘泉, 戚俊杰, 等. 基于水力走时反演刻画裂隙含水层非均质性[J]. 地质科技通报, 2021, 40(1): 175-183. doi: 10.19509/j.cnki.dzkq.2021.0015
Wu S H, Li Q, Qi J J, et al. Depicting the heterogeneity of fractured aquifer based on hydraulic travel time inversion[J]. Bulletin of Geological Science and Technology, 2021, 40(1): 175-183(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2021.0015
|
[62] |
Asa E, Saafi M, Membah J, et al. Comparison of linear and nonlinear Kriging methods for characterization and interpolation of soil data[J]. Journal of Computing in Civil Engineering, 2012, 26(1): 11-18. doi: 10.1061/(ASCE)CP.1943-5487.0000118
|
[63] |
Yamamoto J K. Correcting the smoothing effect of ordinary Kriging estimates[J]. Mathematical Geology, 2005, 37(1): 69-94. doi: 10.1007/s11004-005-8748-7
|
[64] |
施小清, 姜蓓蕾, 卞锦宇, 等. 以地质统计方法推估上海第三承压含水层渗透系数的分布[J]. 工程勘察, 2009, 1: 36-41. https://www.cnki.com.cn/Article/CJFDTOTAL-GCKC200901011.htm
Shi X Q, Jiang B L, Bian J Y, et al. Geostatistical analysis for estimating the spatial variability of hydraulic conductivity in the third confined aquifer of Shanghai City[J]. Geotechnical Investigation & Surveying, 2009, 1: 36-41(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GCKC200901011.htm
|
[65] |
Ochie K I, Rotimi O J. Geostatistics-Kriging and co-Kriging methods in reservoir characterization of hydrocarbon rock deposits[J]. SPE Nigeria Annual International Conference and Exhibition, 2018, 8: SPE-193483-MS.
|
[66] |
Wang L Q, Dai L J, Li L J, et al. Multivariable coKriging prediction and source analysis of potentially toxic elements(Cr, Cu, Cd, Pb, and Zn) in surface sediments from Dongting Lake, China[J]. Ecological Indicators, 2018, 94(1): 312-319.
|
[67] |
Giraldo R, Herrera L, Leiva V. CoKriging prediction using as secondary variable a functional random field with application in environmental pollution[J]. Mathematics, 2020, 8(8): 1305. doi: 10.3390/math8081305
|
[68] |
Kontoudis G P, Stilwell D J. A Comparison of Kriging and coKriging for estimation of underwater acoustic communication performance[J]. Proceedings of the International Conference on Underwater Networks & Systems, 2019, 29: 1-8.
|
[69] |
Rostami A A, Karimi V, Khatibi R, et al. An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by Kriging and co-Kriging models[J]. Journal of Environmental Management, 2020, 270(15): 110843.
|
[70] |
Archie G E. The electrical resistivity log as an aid in determining some reservoir characteristics[J]. Petroleum Reservoir Simulation, 1942, 146(1): 54-62.
|
[71] |
Purvance D T, Andricevic R. On electrical hydraulic conductivity correlation in aquifers[J]. Water Resources Research, 2000, 36(10): 2905-2913. doi: 10.1029/2000WR900165
|
[72] |
Yeboah-Forson A, Whitman D. Electrical resistivity characterization of anisotropy in the biscayne aquifer[J]. Groundwater, 2014, 52(5): 728-736. doi: 10.1111/gwat.12107
|
[73] |
Etete B I, Noiki F R, Aizebeokhai A P, et al. Estimation of hydraulic parameters from vertical electrical resistivity sounding[J]. Journal of Informatics and Mathematical Sciences, 2017, 9(2): 285-296.
|
[74] |
Mawer C, Parsekian A, Pidlisecky A, et al. Characterizing heterogeneity in infiltration rates during managed aquifer recharge[J]. Groundwater, 2016, 54(6): 818-829. doi: 10.1111/gwat.12423
|
[75] |
Marsan D, Azimmah A, Adli D P, et al. Aquifer characterization using 2D electrical resistivity imaging in Kidangpananjung, Cililin District, West Java[J]. Earth and Environmental Science, 2017, 62: 012014.
|
[76] |
Almadani S, Ibrahim E, Al-Amri A, et al. Delineation of a fractured granite aquifer in the Alwadeen area, Southwest Saudi Arabia using a geoelectrical resistivity survey[J]. Arabian Journal of Geosciences, 2019, 12: 449. doi: 10.1007/s12517-019-4646-z
|
[77] |
Idrysy E I, Smedt F D. A comparative study of hydraulic conductivity estimations using geostatistics[J]. Hydrogeology Journal, 2007, 15: 459-470. doi: 10.1007/s10040-007-0166-0
|
[78] |
Kitanidis P K. Quasi-linear geostatistical theory for inversing[J]. Water Resources Research, 1995, 31(10): 2411-2419. doi: 10.1029/95WR01945
|
[79] |
Yeh T C J, Zhang J Q. A geostatistical inverse method for variably saturated flow in the vadose zone[J]. Water Resources Research, 1996, 32(9): 2757-2766. doi: 10.1029/96WR01497
|
[80] |
Bailey R, Baù D. Ensemble smoother assimilation of hydraulic head and return flow data to estimate hydraulic conductivity distribution[J]. Water Resources Research, 2010, 46(12): W12543.
|
[81] |
Jiang L, Bai L, Zhao Y, et al. Combining InSAR and hydraulic head measurements to estimate aquifer parameters and storage variations of confined aquifer system in Cangzhou, North China Plain[J]. Water Resources Research, 2018, 54(10): 8234-8252. doi: 10.1029/2017WR022126
|
[82] |
Yoon G L, Cho H Y, Kim Y S, et al. Hydraulic gradient reduction effects on sand-water mixture flows caused by electro-magnetic force generation[J]. Journal of Coastal Research, 2018, 85: 1141-1145. doi: 10.2112/SI85-229.1
|
[83] |
王福刚, 张佳慧, 于吉洋, 等. 不同水力梯度对渗透系数影响研究[J]. 实验技术与管理, 2015, 32(6): 25-28. doi: 10.3969/j.issn.1002-4956.2015.06.008
Wang F G, Zhang J H, Yu J Y, et al. Research on influence of different hydraulic gradient on hydraulic conductivity[J]. Experimental Technology and Mangement, 2015, 32(6): 25-28(in Chinese with English abstract). doi: 10.3969/j.issn.1002-4956.2015.06.008
|
[84] |
Almeida J A. Stochastic simulation methods for characterization of lithoclasses in carbonate reservoirs[J]. Earth Sci. Rev., 2010, 101: 250-270. doi: 10.1016/j.earscirev.2010.05.002
|
[85] |
Hoffman Z R, Kivanc K, DiMarzio C A. Single image structured illumination(SISIM) for in vivo imaging[C]//Three-dimensional and multidimensinal microscopy: Image acquisition and processing ⅩⅩⅤ. San Francisco: SPIE Digital Library, 2018.
|
[86] |
Oyeyemi K D, Olowokere M T, Aizebeokhai A P. Correction to: Building 3D lithofacies and depositional models using sequential indicator simulation(SISIM) method: A case history in western Niger Delta[J]. Arabian Journal for Science and Engineering, 2018, 43: 3775-3792. doi: 10.1007/s13369-018-3212-4
|
[87] |
Weissmann G S, Carle S F, Fogg G E. Three dimensional hydrofacies modeling based on soil surveys and transition probability geostatistics[J]. Water Resources Research, 1999, 35(6): 1761-1770. doi: 10.1029/1999WR900048
|
[88] |
dell'Arciprete D, Bersezio R, Felletti F G, et al. Comparison of three geostatistical methods for hydrofacies simulation: A test on alluvial sediments[J]. Hydrogeology Journal, 2012, 20(2): 299-311. doi: 10.1007/s10040-011-0808-0
|
[89] |
Ouellon T, Lefebvre R, Marcotte D, et al. Hydraulic conductivity heterogeneity of a local deltaic aquifer system from the kriged 3D distribution of hydrofacies from borehole logs, Valcartier, Canada[J]. Journal of Hydrology, 2008, 351(1/2): 71-86.
|
[90] |
He Y, Hu K, Li B, et al. Comparison of sequential indicator simulation and transition probability indicator simulation used to model clay content in microscale surface soil[J]. Soil Science, 2009, 174: 395-402. doi: 10.1097/SS.0b013e3181aea77c
|
[91] |
Medina-Ortega P, Morales Casique E, Hernández Espriú A. Sequential indicator simulation for a three dimensional distribution of hydrofacies in a volcano sedimentary aquifer in Mexico City[J]. Hydrogeology Journal, 2019, 27: 2581-2593. doi: 10.1007/s10040-019-02011-1
|
[92] |
许克卫. 沉积相随机建模与确定性建模对比分析[J]. 内蒙古石油化工, 2020(12): 114-117. doi: 10.3969/j.issn.1006-7981.2020.12.046
Xu K W. Comparative analysis of stochastic modeling and deterministic modeling of sedimentary facies[J]. Inner Monglia Petrochemical Industry, 2020(12): 114-117(in Chinese with English abstract). doi: 10.3969/j.issn.1006-7981.2020.12.046
|
[93] |
Escobar G R, Roehl D, Quadros F B, et al. Stochastic modelling of karstic networks of Potiguar Basin, Brazil[J]. Advances in Water Resources, 2021, 156: 104026. doi: 10.1016/j.advwatres.2021.104026
|
[94] |
Colombera L, Mountney N P. Influence of fluvial crevasse-splay deposits on sandbody connectivity: Lessons from geological analogues and stochastic modelling[J]. Marine and Petroleum Geology, 2021, 128: 105060. doi: 10.1016/j.marpetgeo.2021.105060
|
[95] |
葛渊博, 卢文喜, 王梓博, 等. 基于BP神经网络替代模型的地下水污染随机模拟[J/OL]. 中国农村水利水电, 2021. https://kns.cnki.net/kcms/detail/42.1419.TV.20211020.1145.074.html.
Ge Y B, Lu W X, Wang Z B, et al. Random simulation of groundwater pollution based on BP neural network substitution model[J/OL]. China Rural Water and Hydropower, 2021(in Chinese with English abstract).
|
[96] |
徐东齐, 孙致学, 任宇飞, 等. 基于地质知识库的辫状河致密储层地质建模[J]. 断块油气田, 2018, 25(1): 57-61. https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT201801012.htm
Xu D Q, Sun Z X, Ren Y F, et al. Geological modeling of braided river tight reservoir based on geological knowledge Database[J]. Fault-Block Oil & Gas Field, 2018, 25(1): 57-61(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT201801012.htm
|
[97] |
王小嘉, 李少华. 近岸水下扇储层三维地质建模方法比较[J]. 鲁东大学学报: 自然科学版, 2021, 37(1): 81-88. doi: 10.3969/j.issn.1673-8020.2021.01.013
Wang X J, Li S H. Comparison of 3D geological modeling methods for nearshore subaqueous fan reservoir[J]. Journal of Ludong University: Natural Science Edition, 2021, 37(1): 81-88(in Chinese with English abstract). doi: 10.3969/j.issn.1673-8020.2021.01.013
|
[98] |
王鸣川, 段太忠, 计秉玉. 多点统计地质建模技术研究进展与应用[J]. 古地理学报, 2017, 19(3): 557-566. https://www.cnki.com.cn/Article/CJFDTOTAL-GDLX201703014.htm
Wang M C, Duan T Z, Ji B Y. Research progress and application of multipoint statistics geological modeling technology[J]. Journal of Palaeogeography, 2017, 19(3): 557-566. https://www.cnki.com.cn/Article/CJFDTOTAL-GDLX201703014.htm
|
[99] |
Cui Z, Chen Q Y, Liu G, et al. Hybrid parallel framework for multiple-point geostatistics on Tianhe-2: A robust solution for large-scale simulation[J]. Computers & Geosciences, 2021, 157: 104923.
|
[100] |
Paithankar A, Chatterjee S. Grade and tonnage uncertainty analysis of an African copper deposit using multiple-point geostatistics and sequential Gaussian simulation[J]. Natural Resources Research, 2018, 27(4): 419-436. doi: 10.1007/s11053-017-9364-1
|
[101] |
Cao Z D, Li L P, Chen K. Bridging iterative ensemble smoother and multiple-point geostatistics for better flow and transport modeling[J]. Journal of Hydrology, 2018, 565: 411-421. doi: 10.1016/j.jhydrol.2018.08.023
|
[102] |
Wang L X, Yin Y S, Feng W J, et al. A training image optimization method in multiple-point geostatistics and its application in geological modeling[J]. Petroleum Exploration and Development, 2019, 46(4): 739-745. doi: 10.1016/S1876-3804(19)60231-4
|
[103] |
陈欢庆, 李文青, 洪垚. 多点地质统计学建模研究进展[J]. 高校地质学报, 2018, 24(4): 593-603. https://www.cnki.com.cn/Article/CJFDTOTAL-GXDX201804012.htm
Chen H Q, Li W Q, Hong Y. Advances in multiple-point geostatistics modeling[J]. Geological Journal of China Universities, 2018, 24(4): 593-603(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GXDX201804012.htm
|
[104] |
Al-Mudhafar W J. How is multiple-point geostatistics of lithofacies modeling assisting for fast history matching? A case study from a sand-rich fluvial depositional environment of Zubair Formation in South Rumaila Oil Field[C]//Offshore Technology Conference. [S. l.]: [s. n.], 2018.
|
[105] |
Chatterjee S, Askari R, Jeng J Y, et al. Stochastic fracture simulation using pixel-based multiple-point geostatistics by integrating seismic radial anisotropy and well data: applications in two hydrology sites[J]. Environmental Earth Sciences, 2020, 79: 515. doi: 10.1007/s12665-020-09258-y
|
[106] |
Babu M N, Venkatesh A, Nair R. Seismic lithofacies distribution modeling using the single normal equation simulation(SNESIM) algorithm of multiple-point geostatistics in Upper Assam Basin, India[J]. International Journal of Mathematical, Engineering and Management Sciences, 2021, 6(3): 805-823. doi: 10.33889/IJMEMS.2021.6.3.048
|
[107] |
Zovi F, Camporese M, Fransssen H H, et al. Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter[J]. Journal of Hydrology, 2017, 548: 208-224. doi: 10.1016/j.jhydrol.2017.02.056
|
[108] |
Feng W J, Yin Y S, Zhang C M, et al. A training image optimal selecting method based on composite correlation coefficient ranking for multiple-point geostatistics[J]. Journal of Petroleum Science and Engineering, 2019, 179: 292-311. doi: 10.1016/j.petrol.2019.04.046
|
[109] |
Mullins J, DerVegt H V, Howell J. Combining process-based models and multiple-point geostatistics for improved reservoir modelling[J]. Petroleum Geoscience, 2021, 27(3): petgeo2020-012.
|
[110] |
王恺其, 肖凡. 多点地质统计学的理论、方法、应用及发展现状[J]. 地质科技情报, 2019, 38(6): 257-268. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201906031.htm
Wang K Q, Xiao F. Multiple-ponits geostatistics: A review of theories, methods and application[J]. Geological Science and Technology Information, 2019, 38(6): 257-268(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201906031.htm
|
[111] |
Li B, Rodell M, Kumar S, et al. Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges[J]. Water Resources Research, 2019, 55(9): 7564-7586. doi: 10.1029/2018WR024618
|
[112] |
Tang M, Liu Y M, Durlofsky L J. A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems[J]. Journal of Computational Physics, 2020, 413: 109456. doi: 10.1016/j.jcp.2020.109456
|
[113] |
宗成元, 康学远, 施小清, 等. 基于多点地质统计与集合平滑数据同化方法识别非高斯渗透系数场[J]. 水文地质工程地质, 2020, 47(2): 1-8. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG202002002.htm
Zong C Y, Kang X Y, Shi X Q, et al. Characterization of non-Gaussian hydraulic conductivity fields using multiple-point geostatistics and ensemble smoother with multiple data assimilation method[J]. Hydrogeology & Engineering Geology, 2020, 47(2): 1-8(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG202002002.htm
|
[114] |
鞠磊. 基于多源数据同化的含水层异质性刻画[D]. 杭州: 浙江大学, 2018.
Ju L. Characterization of aquifer heterogeneity based on multi-source data assimilation[D]. Hangzhou: Zhejiang University, 2018(in Chinese with English abstract).
|
[115] |
He Q Z, Barajas-Solano D, Tartakovsky G, et al. Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport[J]. Advances in Water Resources, 2020, 141: 103610 doi: 10.1016/j.advwatres.2020.103610
|
[116] |
Ghorbanidehno H, Kokkinaki A, Lee J, et al. Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology[J]. Journal of Hydrology, 2020, 591: 125266. doi: 10.1016/j.jhydrol.2020.125266
|
[117] |
摆玉龙, 王一朝. 耦合多目标遗传算法的数据同化方法参数优化研究[J]. 遥感技术与应用, 2018, 33(6): 1056-1062. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS201806008.htm
Bai Y L, Wang Y Z. Research on parameter optimization of data assimilation method coupled with multi-objective genetic algorithms[J]. Remote Sensing Technology and Application, 2018, 33(6): 1056-1062(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS201806008.htm
|
[118] |
顾炉华, 赖锡军. 基于EnKF算法的大型河网水量数据同化研究[J]. 水力发电学报, 2021, 40(3): 64-75. https://www.cnki.com.cn/Article/CJFDTOTAL-SFXB202103007.htm
Gu L H, Lai X J. Influence of field observation on effectiveness of data assimilation using EnKF algorithm for large-scale river network[J]. Journal of Hydroelectric Engineering, 2021, 40(3): 64-75(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SFXB202103007.htm
|
[119] |
Li X, Cheng G, Liu S, et al. Heihe watershed allied telemetry experimental research(HiWATER): Scientific objectives and experimental design[J]. Bulletin American Meteorology Society, 2013, 94: 1145-1160. doi: 10.1175/BAMS-D-12-00154.1
|
[120] |
李新, 刘丰, 方苗. 模型与观测的和弦: 地球系统科学中的数据同化[J]. 中国科学: 地球科学, 2020, 50(9): 1185-1194. https://www.cnki.com.cn/Article/CJFDTOTAL-JDXK202009002.htm
Li X, Liu F, Fang M. Chords of model and observation: Data assimilation in Earth system science[J]. Scientia Sinica Terrae, 2020, 50(9): 1185-1194(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-JDXK202009002.htm
|
[121] |
Gardet C, Ravalec M, Gloaguen E. Pattern-based conditional simulation with a raster path: A few techniques to make it more efficient[J]. Stochastic Environmental Research and Risk Assessment, 2016, 30(2): 429-446. doi: 10.1007/s00477-015-1207-1
|
[122] |
罗明, 裴韬. 空间软数据及其插值方法研究进展[J]. 地理科学进展, 2009, 28(5): 663-672. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ200905004.htm
Luo M, Pei T. Review on soft spatial data and its spatial interpolation methods[J]. Progress in Geography, 2009, 28(5): 663-672(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ200905004.htm
|
[123] |
李章林, 吴冲龙, 张夏林, 等. 地质科学大数据背景下的矿体动态建模方法探讨[J]. 地质科技通报, 2020, 39(4): 59-68. doi: 10.19509/j.cnki.dzkq.2020.0408
Li Z L, Wu C L, Zhang X L, et al. Discussion on dynamic orebody modeling with geological science big data[J]. Bulletin of Geological Science and Technology, 2020, 39(4): 59-68(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2020.0408
|
[124] |
陈麒玉, 刘刚, 何珍文, 等. 面向地质大数据的结构-属性一体化三维地质建模技术现状与展望[J]. 地质科技通报, 2020, 39(4): 51-58. doi: 10.19509/j.cnki.dzkq.2020.0407
Chen L Y, Liu G, He Z W, et al. Current situation and prospect of structure-attribute integrated 3D geological modeling technology for geological big data[J]. Bulletin of Geological Science and Technology, 2020, 39(4): 51-58(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2020.0407
|