Hydrogeology Journal

, Volume 20, Issue 6, pp 1061–1080 | Cite as

Data-driven modeling for groundwater exploration in fractured crystalline terrain, northeast Brazil

  • Michael James Friedel
  • Oderson Antônio de Souza Filho
  • Fabio Iwashita
  • Adalene Moreira Silva
  • Sueli Yoshinaga
Paper

Abstract

It is not possible, using numerical methods, to model groundwater flow and transport in the fractured crystalline rock of northeastern Brazil. As an alternative, the usefulness of self-organizing map (SOM), k-means clustering, and Davies-Bouldin techniques to conceptualize the hydrogeology was evaluated. Also estimated was the well yield and groundwater quality across the Juá region. This process relies on relations in the underlying multivariate density function associated with a sparse local set of hydrogeologic (electrical conductivity, geology, temperature, and well yield) and a complete regional set of airborne geophysical (electromagnetic, magnetic, and radiometric) and satellite spectrometric measurements. Resampling of the regional well yield and electrical conductivity estimates provides sufficient resolution to construct variograms for stochastic modeling of the hydrogeologic variables. The combination of these stochastic maps provides a way to identify potential drilling targets for future groundwater development. The data-driven estimation approach, when applied to available airborne electromagnetic and water-well hydrogeologic measurements, provides a low-cost alternative to numerical groundwater flow modeling. In addition to fractured rock environments, the alternative modeling framework can provide spatial parameter estimates and associated variograms for constraints to improve the traditional calibration of equivalent groundwater-porous-media models.

Keywords

Brazil Fractured rocks Geophysical methods Self-organizing map Well yield 

Modélisation sous pilotage des données pour l’exploration des eaux souterraines dans un terrain cristallin fracturé (Nord-Est du Brésil)

Résumé

Il n’est pas possible, en utilisant les méthodes numériques, de modéliser écoulements et transferts souterrains dans le massif cristallin fracturé du Nord-Est du Brésil. Comme solution de remplacement, on a évalué l’utilité de la carte auto-adaptative (CAA), regroupement selon les k-moyennes, et celle des techniques de Davies-Bouldin de conceptualisation de l’hydrogéologie. Ont été également estimés le débit des puits et la qualité de l’eau sur la région de Juá. Cette méthode est basée sur les relations à l’intérieur de la fonction de densité des multi variables sous-jacentes, associées à une série locale de données hydrogéologiques (conductivité électrique, géologie, température et production des puits) dispersées et un ensemble régional complet de mesures de géophysique aéroportée (électromagnétiques, magnétiques et radiométriques) et de spectrométrie par satellite. Le ré-échantillonnage du rendement des puits de la région et des estimations de conductivité électrique est assez précis pour construire des variogrammes en vue d’une modélisation stochastique des paramètres hydrogéologiques. La combinaison de ces cartes stochastiques livre un moyen d’identification des cibles potentielles de foration en vue du développement futur des eaux souterraines. L’approche par estimation sous pilotage des données, quand elle est appliquée aux mesures disponibles d’électromagnétisme aéroporté et d’hydrogéologie des puits, offre une alternative à bas coût à la modélisation numérique de l’écoulement souterrain. Au-delà des milieux rocheux fracturés, le cadre du modèle alternatif peut fournir une estimation spatiale des paramètres et des variogrammes des contraintes associées afin d’améliorer le calage classique des modèles équivalents de milieux poreux aquifères.

Modelado de datos empleados para la exploración de agua subterránea en terrenos cristalinos fracturados, noreste de Brasil

Resumen

No es posible, usando métodos numéricos modelar el flujo y transporte de agua subterránea en las rocas cristalinas fracturadas del noreste de Brasil. Como alternativa se evaluó la utilidad de mapas autoorganizados (SOM), agrupamiento de las k medias y técnicas de Davies-Bouldin para conceptualizar la hidrogeología. También se estimó el rendimiento de los pozos y la calidad del agua subterránea a través de la región Juá. Este proceso depende de las relaciones en la función multivariada de densidad subyacente asociada con un conjunto local disperso de datos hidrogeológicos (conductividad eléctrica, geología, temperatura, y rendimiento de pozos) y un conjunto completo regional de mediciones de la geofísica aérea (electromagnéticas, magnéticas y radiométricas) y mediciones espectrométricas satelitales. El remuestreado del rendimiento regional de pozos y las estimaciones de conductividad eléctrica proporciona una suficiente resolución para construir variogramas para el modelado estocástico de las variables hidrogeológicas. La combinación de estos mapas estocásticos proporciona una forma para identificar los potenciales objetivos de perforación para el futuro desarrollo de agua subterránea. El enfoque de estimación forzado por los datos empleados, cuando se aplica a las medidas electromagnéticas aéreas e hidrogeológicas de pozos de agua, proporciona una alternativa de bajo costo para el modelado numérico del flujo de aguas subterráneas. Además de los ambientes de rocas fracturadas, el esquema del modelado alternativo puede proveer la estimación de parámetros espaciales y variogramas asociados para las limitaciones para mejorar la calibración tradicional de los modelos equivalentes de agua subterránea en medios porosos.

巴西东北部破碎结晶带地下水勘探的数据驱动模拟

摘要

在巴西东北部裂隙结晶岩带,利用数值方法模拟地下水的流动和迁移几乎是不可能的。作为一种替代方案,对利用自组织映射, k平均值聚类和Davies-Bouldin技术概念化水文地质学进行了评价。同时,对Juá区域的水井涌水量和地下水水质也进行了评价。这个过程依赖于与当地稀疏的水文地质单元(电导率,地质,温度和水井涌水量)相关联的底层多变量密度函数和区域完整的航空物探(电磁,磁和放射性测量)及卫星光谱测量的关系。对区域水井涌水量和电导率评估的重采样为建立水文地质变量随机模型的变异函数提供了足够的分辨率。这些随机网络的结合为未来地下水的开发确定潜在的钻孔目标提供了一种方法。数据驱动评估方法被应用到现有航空电磁和水井水文地质测量时,为地下水流数值模拟提供了一个低成本的替代方案。除了裂隙岩体环境外,这个替代模型的框架可以为约束条件提供空间参数评估和相关联的变异函数,进而改善对相当于地下水多孔介质模型进行校准的传统方法。

Modelação baseada em dados para exploração de água subterrânea em terrenos cristalinos fraturados, nordeste do Brasil

Resumo

Nas rochas cristalinas fraturadas do nordeste do Brasil não é possível modelar o fluxo e transporte de água subterrânea usando métodos numéricos. Como alternativa, avaliou-se a aplicabilidade das técnicas de mapa auto-organizado (SOM), agrupamento k-means, e técnicas Davies-Bouldin para a concetualização da hidrogeologia. Também foram estimadas a produtividade das captações e a qualidade das águas subterrâneas em toda a região de Juá. Este processo baseia-se nas relações subjacentes da função densidade multivariada associada a um conjunto esparso de dados hidrogeológicos locais (condutividade elétrica, geologia, temperatura e produtividade), num conjunto completo de dados regionais de geofísica aérea (eletromagnética, magnética e radiométrica) e em medições espetrométricas de satélite. A reamostragem das estimativas de produtividade e condutividade elétrica fornece uma resolução que permite a construção de variogramas para a modelação estocástica das variáveis hidrogeológicas. A combinação destes mapas estocásticos proporciona um caminho para a identificação de potenciais locais de perfuração para a exploração futura de águas subterrâneas. A abordagem de estimação baseada em dados, quando aplicada a medições disponíveis de eletromagnética aérea e a dados hidrogeológicos a nível da sondagem, fornece uma alternativa de baixo custo à modelação numérica de fluxo. Além de ambientes de rochas fraturadas, este quadro alternativo de modelação pode fornecer estimativas espaciais de parâmetros e variogramas associados para criar restrições que possam servir para melhorar a calibração tradicional de modelos de fluxo em meios porosos equivalentes.

References

  1. Adaptive Informatics Research Center (2010) SOM Toolbox, Helsinki University of Technology, Laboratory of Computer and Information Science, Adaptive Informatics Research Center, Helsinki. Available at http://www.cis.hut.fi/projects/somtoolbox/. Accessed on 3 March 2012
  2. Brauchler, R, Hu R, Dietrich P, Sauter M (2011) A field assessment of high-resolution aquifer characterization based on hydraulic travel time and hydraulic attenuation tomography. Water Resour Res 47: W03503 doi:10.1029/2010WR009635 CrossRefGoogle Scholar
  3. Breiman L (1996) Bagging predictors. Machine Learning 24:123–140Google Scholar
  4. Christophersen N, Hooper RP (1992) Multivariate analysis of stream water chemical data the use of principal components analysis of the end-member mixing problem. Water Resour Res 28:99–107CrossRefGoogle Scholar
  5. Cirilo, JA (2008) Políticas públicas de recursos hídricos para o semi-árido [Public policies for water resources in semi-arid regions]. Estud Av 22(63):1–5. Available at http://dx.doi.org/10.1590/S0103-40142008000200005. Accessed on 3 March 2012
  6. Cordell L; McCafferty AE (1989) A terracing operator for physical property mapping with potential field data. Geophysics 54(17):621–634Google Scholar
  7. Coriolano ACF, Sá EFJ, Silva CCN (2000) Structural and neotectonic control in the location of water wells in semi-arid crystalline terrains: a preliminary approach in the eastern domain of Rio Grande do Norte State, northeastern Brazil. Rev Brasil Geoci vol 30, pp 350–352. Available at http://ojs.c3sl.ufpr.br/ojs2/index.php/rbg/article/view/10660/7819. Accessed on 1 April 2010
  8. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans PAMI 1:224–227Google Scholar
  9. Deutsch CV, Journel AG (1998) GSLIB: Geostatistical Software Library and user's guide, 2nd edn. Oxford University Press, New York, 369 ppGoogle Scholar
  10. Dickson BL, Giblin AM (2007) An evaluation of methods for imputation of missing trace element data in groundwaters. Geochem Explor Environ Anal 7(2):173–178CrossRefGoogle Scholar
  11. Dietrich, P, Tronicke J (2009) Integrated analysis and interpretation of cross-hole P- and S-wave tomograms: a case study. Near Surf Geophys 7:101–109 doi:10.3997/1873-0604.2008041 Google Scholar
  12. Duckworth K, Calvert HT, Juigalli J (1991) A method for obtaining depth estimates from the geometry of Slingram profiles. Geophysics 56 (10): 1543–1552CrossRefGoogle Scholar
  13. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. In: Monographs on statistics and applied probability, vol 57. Chapman and Hall, London, 436 ppGoogle Scholar
  14. Fessant F, Midenet S (2002) Self-organizing map for data imputation and correction in surveys. Neural Comput Appl10:300–310CrossRefGoogle Scholar
  15. Fraser DC (1978) Resistivity mapping with an airborne multicoil electromagnetic system. Geophysics 43:144–172CrossRefGoogle Scholar
  16. Fraser S, Dickson BL (2007) A new method for data integration and integrated data interpretation: self-organizing maps. In: Milkereit B (ed) Proceedings of Exploration 07: Fifth Decennial International Conference on Mineral Exploration. Toronto, September 2007, pp 907–910Google Scholar
  17. Friedel MJ (2011) Modeling hydrologic and geomorphologic responses across post-fire landscapes using a self-organizing map approach. Environ Model Softw 26(12):1660–1674CrossRefGoogle Scholar
  18. Frischknecht, FC, Labson, VF, Spies, BR, Anderson, WL (1989) Profiling methods using small sources. In: Nabighian MN (ed) Electromagnetic methods in applied geophysics. Soc. Expl. Geophys., Tulsa, OK, pp 105–270Google Scholar
  19. Glantz MH (1993) Forecasting El Niño: science's gift to the 21st century. In: Glantz MH (ed) Workshop on Usable Science: Food Security, Early Warning and El Niño. Budapest, Hungary, 25–28 October, National Center for Atmospheric Research, Boulder, pp 3–11Google Scholar
  20. Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New YorkGoogle Scholar
  21. Hastie T, Tibshirani R, Friedman J (2002) The elements of statistical learning, Springer, Berlin, 533 ppGoogle Scholar
  22. Hildenbrand TG, Raines GL, Fitterman DV (1990) National airborne geophysics program. In: Fitterman DV (ed) Developments and applications of modern airborne electromagnetic surveys. US Geol Surv Bull 1925, pp 3–5Google Scholar
  23. Hong YS, Rosen MR (2001) Intelligent characterisation and diagnosis of the groundwater quality in an urban fractured-rock aquifer using an artificial neural network. Urban Water 3 (3):193–204CrossRefGoogle Scholar
  24. Iwashita F, Friedel MJ, Souza-Filho CR, Fraser SJ, (2011a) Hillslope chemical weathering across Paraná, Brazil: a data mining-GIS hybrid approach. Geomorphology 132(3–4):167–175CrossRefGoogle Scholar
  25. Iwashita F, Friedel MJ, Rebeiro GF, Fraser SJ (2011b) Intelligent estimation of hydrogeologic properties. Geoderma 171:1–10Google Scholar
  26. Junninen H, Niska H, Tuppurainen K, Ruuskanen J, Kolehmainen M (2004) Methods for imputation for missing values in air quality data sets. Atmos Environ 38:2895–2907CrossRefGoogle Scholar
  27. Kalteh AM, Berndtsson R (2007) Interpolating monthly precipitation by self-organizing map (SOM) and multilayer perceptron (MLP). Hydrol Sci J 52(2):305–317CrossRefGoogle Scholar
  28. Kalteh AM, Hjorth P (2009) Imputation of missing values in precipitation-runoff process database. Hydrol Res 40(4):420–432CrossRefGoogle Scholar
  29. Kohonen T (1984) Self-organization and associative memory. Springer Series in Information Sciences, vol 8. Springer, HeidelbergGoogle Scholar
  30. Kohonen T (2001) Self-organizing maps: Third Extended Edition, Springer Series in Information Sciences, 30. Springer, Heidelberg 253 ppGoogle Scholar
  31. Lasa Engenharia, Prospecções S/A (2001) Projeto aerogeofísico água subterrânea no nordeste do Brasil, Blocos Juá (CE), Samambaia (PE) e Serrinha (RN). Relatório final do levantamento e processamento dos dados magnetométricos e eletromagnetométricos e seleção das anomalias eletromagnéticas [Northeastern Brazil groundwater aerogeophysical project: final report of the survey and processing of magnetometric and electromagnetometric data and selection of the electromagnetic anomalies]. Cooperação Canadá-Brasil, 3 vols, 3 CD-Roms, Lasa, Rio De Janeiro, BrazilGoogle Scholar
  32. Malek MA, Harun S, Shamsuddin SM, Mohamad I (2008) Imputation of time series data via Kohonen self organizing maps in the presence of missing data. Eng Technol 41:501–506Google Scholar
  33. Maritz JS (1981) Distribution-free statistical methods. Chapman and Hall, LondonGoogle Scholar
  34. McQueen J (1969) Some methods for classification and analysis of multivariate observations. 5th Berkeley Symposium on Mathematics, Statistics and Probability, vol 1. Berkeley, CA, 1969 pp 281–298Google Scholar
  35. Meng Q, Hu H, Yu Q (2006) The application of an airborne electromagnetic system in groundwater resource and salinization studies in Jilin, China. J Environ Eng Geophys 11(2): 103–109CrossRefGoogle Scholar
  36. National Oceanic and Atmospheric Administation (2011) NOAA La Niña. http://www.elnino.noaa.gov/lanina.html. Accessed 12 June 2011
  37. Neves MA (2005) Análise integrada aplicada à exploração de água subterrânea na bacia do Rio Jundiaí. PhD Thesis, Universidade Estadual Paulista, Rio Claro, Brazil 200 ppGoogle Scholar
  38. Peeters L, Bação F, Lobo V, Dassargues A (2006) Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s self-organizing map. Hydrol Earth Syst Sci Discuss 3(4): 1487–1516CrossRefGoogle Scholar
  39. Phillips JD (1997) Potential-field geophysical software for the PC, version 2.2. US Geol Surv Open-File Rep 97–725, 34 ppGoogle Scholar
  40. Pine JG, Minty BRS (2005) Airborne hydrogeophysics. In: Rubin Y, Hubbard SS (eds) Hydrogeophysics. Springer, Amsterdam, pp 333–357CrossRefGoogle Scholar
  41. Pinéo TRG (2005) Integração de Dados Geofísicos, Geológicos e de Sensores Remotos Aplicados à Prospecção de Água Subterrânea em Meio Fissural (Distrito de Juá, Irauçuba/CE) [Integration of geophysical data: application of remote sensing to groundwater exploration in fractured rocks (Village of Jua, Iraucuba municipality, State of Cerra, Brazil)]. MSc Thesis, Universidade Federal do Ceará, Fortaleza, Brazil, 126 ppGoogle Scholar
  42. PROASNE (2007) Northeastern Brazil groundwater project (2000-2004), CIDA Project A-019777-006. Closing Report. http://www.docstoc.com/docs/49302958/Northeastern-Brazil-Groundwater-Project. Accessed 18 April 2012
  43. Rallo R, Ferre-Gine J, Arenas A, Giralt F (2002) Neural virtual sensor for the inferential prediction of product quality form process variables. Comp Chem Eng 26(12):1735–1754Google Scholar
  44. Ritter H, Schulten K (1986) On the stationary State of Kohonen's self-organizing sensory mapping. Biol Cybernet 54:99–106CrossRefGoogle Scholar
  45. Rubinstein RY, Kroese DP (2007) Simulation and the Monte Carlo method, 2nd edn. Wiley, New YorkGoogle Scholar
  46. Sabins FF (1997) Remote sensing: principles and interpretation, 3rd edn. Freeman, New York, 494 ppGoogle Scholar
  47. Silva OAda (2005) Análise de dados aerogeofísicos aplicada à exploração e ao gerenciamento de recursos hídricos subterrâneos [Analysis of airborne geophysical data applied to the exploration and management of groundwater resources]. PhD Thesis, Institute of Geosciences, Universidade Federal da Bahia, Brazil, 88 ppGoogle Scholar
  48. Silva CMSV, Vasconcelos MB, Santiago MMF (2001) Recarga e datação de poços no cristalino [Recharge and dating of drilled wells in the crystalline domain]. In: IV Simpósio de Hidrogeologia do Nordeste, 2001, Recife. Anais do IV Simpósio de Hidrogeologia do Nordeste, ABAS, São Paulo, Brazil, pp 8–14Google Scholar
  49. Silva CMSV, Demétrio JGA, Santiago MMF, Vasconcelos MB, Feitosa FAC (2003) Perfis verticais de temperatura no estudo de conexões entre açude e poços no cristalino [Temperature bore log in the study of connections between reservoirs and drilled wells in the crystalline domain]. In: XV Simpósio Brasileiro de Recursos Hídricos, 2003, Curitiba, Brazil. Anais do XV Simpósio Brasileiro de Recursos Hídricos. CD-RomGoogle Scholar
  50. Souza Filho OA de (1998) Geologia e mapa de previsão de ocorrência de água subterrânea. Folha SA.24-Y-D-V Irauçuba, Ceará [Geology and predictive map of groundwater occurrence, SA.24-Y-D-V Irauçuba sheet]. MSc Thesis, Universidade Federal de Ouro Preto, Brazil, 99 ppGoogle Scholar
  51. Souza Filho OA de (2008) Dados aerogeofísicos e geológicos aplicados à seleção de áreas favoráveis para água subterrânea no domínio cristalino do Ceará, Brasil. PhD Thesis, Universidade Estadual de Campinas, Campinas, Brazil, 150 ppGoogle Scholar
  52. Souza Filho OA de, Ribeiro JÁ, Veríssimo LS, Oliveira RGde, Gomes FEM, Brandão RdeL, Frizzo SJ, Oliveira JFde (2003) Projeto otimização de metodologias para prospecção de águas subterrâneas em rochas cristalinas. Relatório integrado de atividades 1999–2002: Bases para avaliação do projeto [Project for optimization of groundwater prospection methodologies in crystalline rocks: integrated report 1999–2002]. CPRM/REFO, Fortaleza, Brazil, 160 ppGoogle Scholar
  53. Souza Filho O.A. de, Oliveira R.G., Ribeiro J.A., Veríssimo L.S., Sá J.U. (2006) Interpretação e modelagens de dados de eletrorresistividade para locações de poços tubulares no aqüífero fissural da área-piloto Juá, Irauçuba-Ceará [Interpretation and modeling of electrical resistivity data for water well location in the fissural aquifer of Juá study-area]. Rev Geol DEGEO/UFC 19(1):7–21Google Scholar
  54. Souza Filho OA de, McCaffertty AE, Silva AM, Perrotta MM (2007a) Ground-water potential: a predictive model from airborne geophysical, radiometric, and remote sensing data, Ceará, Brazil. In: Simpósio Brasileiro de Sensoriamento Remoto (SBSR), Florianópolis, Brazil, 13 April 2007, pp 3593–3595, CD-ROMGoogle Scholar
  55. Souza Filho OA de, Silva AM, McCaffertty AE, Perrotta MM, Deszczpan M, fitterman D (2007b) Geophysical properties associated to Juá district geology, Ceará, Brazil. In: 10th International Congress of the Brazilian Geophysical Society, SBGf, Rio de Janeiro, April 2007, CD-RomGoogle Scholar
  56. Souza Filho OA de, Silva AM, Perrotta MM, McCaffertty AE, (2009) Well-yield as training points to model groundwater favorability in a crystalline region of Brazil’s semi-arid region, Anais XIV Simpósio Brasileiro de Sensoriamento Remoto, Natal, Brazil, 25–30 April 2009, INPE, São José dos Campos, Brazil, pp 4449–4456Google Scholar
  57. Souza Filho OA de, Silva, AM, McCaffertty, AE, Sancevero, SS, Perrotta, MM (2010) Using helicopter electromagnetic data to predict groundwater quality in fractured crystalline bedrock in a semi-arid region, northeast Brazil. Hydrogeol J 18:905–916Google Scholar
  58. Speed R (2002) Airborne geophysics for catchment management: why and where. Explor Geophys 33:51–56CrossRefGoogle Scholar
  59. Telford WM, Geldart LP, Sheriff RE, Keys DA (1976) Applied geophysics. Cambridge University Press, New YorkGoogle Scholar
  60. Ultsch A (2003) Maps for the visualization of high-dimensional data spaces. Proceedings of WSOM ‘03, Fukuoka, Japan, 2003, pp 225–236Google Scholar
  61. Ultsch A, Vetter C (1994) Self-organizing-feature-maps versus statistical clustering methods: a benchmark. Research Report 0994, FG Neuroinformatik and Kuenstliche Intelligenz, University of Marburg, GermanyGoogle Scholar
  62. Veríssimo LS, Feitosa FAC (2002) As Águas Subterrâneas no Nordeste do Brasil: Região de Irauçuba - Estado do Ceará, Brasil [Groundwater in the northeast of Brazil: area of Iraucuba, State of Ceara, Brazil]. XXXII Congresso da Associação Internacional de Hidrogeologia e VI Congresso da Associação Latino-Americana de Hidrologia Subterrânea. Mar Del Plata, Argentina, CD-Rom, pp 889–896Google Scholar
  63. Vesanto J (1999) SOM-based data visualization methods. Intelligent Data Anal 3: 111–126CrossRefGoogle Scholar
  64. Vesanto J, Alhoniemi E (2000) Clustering of the self organized map. IEEE Trans Nerural Networks 11(3):586–600CrossRefGoogle Scholar
  65. Wang S (2003) Application of self-organising maps for data mining with incomplete data sets. Neural Computing Applic 12:42–48CrossRefGoogle Scholar

Copyright information

© Springer-Verlag (outside the USA) 2012

Authors and Affiliations

  • Michael James Friedel
    • 1
  • Oderson Antônio de Souza Filho
    • 2
  • Fabio Iwashita
    • 3
  • Adalene Moreira Silva
    • 4
  • Sueli Yoshinaga
    • 5
  1. 1.Crustal Imaging & Geochemistry Science Center and Center for Computational and Mathematical BiologyUS Geological Survey and University of ColoradoDenverUSA
  2. 2.Geological Survey of Brazil and University of CampinasCuritibaBrazil
  3. 3.Nevada System of Higher EduationDesert Research InstituteRenoUSA
  4. 4.Applied Geophysics Laboratory, Institute of GeosciencesUniversity of BrasiliaBrasíliaBrazil
  5. 5.Institute of GeosciencesUniversity of CampinasCampinasBrazil

Personalised recommendations