Jina Jeong

Non-parametric simulations-based conditional stochastic predictions of geologic heterogeneities and leakage potentials for hypothetical CO2 sequestration sites

The present study focuses on understanding the leakage potentials of the stored supercritical CO2 plume through caprocks generated in geostatistically created heterogeneous media. For this purpose, two hypothetical cases with different geostatistical features were developed, and two conditional geostatistical simulation models (i.e., sequential indicator simulation or SISIM and generalized coupled Markov chain or GCMC) were applied for the stochastic characterizations of the heterogeneities. Then, predictive CO2 plume migration simulations based on stochastic realizations were performed and summarized. In the geostatistical simulations, the results from the GCMC model showed better performance than those of the SISIM model for the strongly non-stationary case, while SISIM models showed reasonable performance for the weakly non-stationary case in terms of low-permeability lenses characterization. In the subsequent predictive simulations of CO2 plume migration, the observations in the geostatistical simulations were confirmed and the GCMC-based predictions showed underestimations in CO2 leakage in the stationary case, while the SISIM-based predictions showed considerable overestimations in the non-stationary case. The overall results suggest that: (1) proper characterization of low-permeability layering is significantly important in the prediction of CO2 plume behavior, especially for the leakage potential of CO2 and (2) appropriate geostatistical techniques must be selectively employed considering the degree of stationarity of the targeting fields to minimize the uncertainties in the predictions.

A shallow water table fluctuation model in response to precipitation with consideration of unsaturated gravitational flow

A precise estimation of groundwater fluctuation is studied by considering delayed recharge flux (DRF) and unsaturated zone drainage (UZD). Both DRF and UZD are due to gravitational flow impeded in the unsaturated zone, which may nonnegligibly affect groundwater level changes. In the validation, a previous model without the consideration of unsaturated flow is benchmarked. The model is calibrated using multi-year groundwater data, and consistent model parameter statistics are obtained and validated. The estimation capability of the new model is superior to the benchmarked model as indicated by the significantly improved representation of groundwater level with physically interpretable model parameters.

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model

In this study, a data-driven method for predicting CO2 leaks and associated concentrations from geological CO2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems.

A subagging regression method for estimating the qualitative and quantitative state of groundwater

A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.RésuméUne méthode de régression par sous-échantillonnage et agrégation (subbaging) (SBR) pour l’analyse des données relatives aux eaux souterraines se rapportant à l’évaluation des tendances associée à l’incertitude est proposée. La méthode SBR est validée en considérant des données synthétiques de manière compétitive vis-à-vis de méthodes conventionnelles robustes et non robustes. A partir des résultats, on vérifie que les précisions estimées de la méthode SBR sont cohérentes et supérieures à celles des autres méthodes, et que les incertitudes sont estimées de manière raisonnable : les autres ne disposent pas d’option d’analyse des incertitudes. Pour avancer dans le processus de validation, des données réelles relatives aux eaux souterraines sont utilisées et analysées en les comparant au processus gaussien de régression (GPR). Dans tous les cas, la tendance et les incertitudes associées sont estimées de manière raisonnable à la fois par SBR et par GPR, indépendamment des données gaussiennes ou non gaussiennes biaisées. Cependant, on s’attend à ce que le GPR ait une limitation dans ses applications à des données fortement entachées de valeurs aberrantes en raison de sa non-robustesse. A partir des mises en œuvre, on détermine que la méthode SBR a un potentiel pour faire l’objet de développement en tant qu’outil efficace de détection d’anomalies ou d’identification de valeurs aberrantes dans des données relatives à l’état des eaux souterraines telles que le niveau piézométrique ou la concentration en contaminants.ResumenSe propone un método de regresión de agregación de submuestreos (SBR) para el análisis de datos de agua subterránea relacionados con la incertidumbre asociada a la estimación de tendencias. El método SBR se valida competitivamente frente a los datos sintéticos de otros métodos robustos y no robustos convencionales. A partir de los resultados, se verifica que las precisiones de estimación del método SBR son consistentes y superiores a las de otros métodos, y las incertidumbres son razonablemente estimadas; los otros no tienen la opción del análisis de incertidumbres. Además para validar, se emplean los datos reales del agua subterránea y se analizan comparativamente con la regresión del proceso gaussiano (GPR). En todos los casos, la tendencia y las incertidumbres asociadas son razonablemente estimadas tanto por SBR como por GPR, independientemente de los datos gaussianos sesgados o no gaussianos. Sin embargo, se espera que GPR tenga una limitación en aplicaciones a datos gravemente corrompidos por valores atípicos debido a su no robustez. A partir de las implementaciones, se determina que el método SBR tiene el potencial de ser desarrollado como una herramienta eficaz de detección de anomalías o identificación de valores atípicos en datos de agua subterránea tales como el nivel de agua subterránea y la concentración de contaminantes.摘要本文提出了有关涉及趋势-估算不确定性分析地下水数据的子样品集聚(subagging) 回归法。相对于其它常规的强健的和非强健的方法,子样品集聚回归法经过了综合数据的验证。结果证实,子样品集聚回归法的估算精度始终如一,优于其它方法的估算精度,合理地估算了不确定性;其它方法没有不确定性选项。为了进一步进行验证,采用实际的地下水数据,并与高斯过程回归法对这些数据进行了对比分析。在所有情况中,无论是否高斯或者非高斯偏斜数据,利用子样品集聚回归法和高斯过程回归法对趋势和相关不确定性进行了合理估算。然而,预计高斯过程回归法在应用中对由于异常值的非-稳健性造成的严重损坏数据有局限。从实施的情况看,子样品集聚回归法作为地下水状况数据诸如地下水位和污染物含量异常探测或异常值识别的一个有效工具,具有进一步发展的潜力。ResumoUm método de regressão (RAS) por agregação de subamostra (subagging) foi proposto para a análise de dados de águas subterrâneas referentes à incerteza associada à estimativa de tendência. O método RAS foi validado contra dados sintéticos competitivamente com outros métodos convencionais robustos e não robustos. A partir dos resultados, verificou-se que as precisões de estimativa do método RAS foram consistentes e superiores às de outros métodos, e as incertezas foram razoavelmente estimadas; os demais não possuem opção de análise de incerteza. Para validar além, os dados de águas subterrâneas reais foram empregados e analisados comparativamente com a regressão de processo Gaussiano (RPG). Para todos os casos, a tendência e as incertezas associadas foram razoavelmente estimadas tanto pela RAS quanto pela RPG, independentemente de dados Gaussianos ou não Gaussianos. No entanto, espera-se que a RPG tenha uma limitação nas aplicações de dados gravemente corrompidos por dados discrepantes devido à sua não robustez. A partir das implementações, foi determinado que o método RAS tem potencial para ser desenvolvido como uma ferramenta eficaz de detecção de anomalias ou identificação de valores espúrios em dados de águas subterrâneas, tais como o nível das águas subterrâneas e a concentração de contaminantes.

A novel data assimilation methodology for predicting lithology based on sequence labeling algorithms

A hidden Markov model (HMM) and a conditional random fields (CRFs) model for lithological predictions based on multiple geophysical well-logging data are derived for dealing with directional nonstationarity through bidirectional training and conditioning. The developed models were benchmarked against their conventional counterparts, and hypothetical boreholes with the corresponding synthetic geophysical data including artificial errors were employed. In the three test scenarios devised, the average fitness and unfitness values of the developed CRFs model and HMM are 0.84 and 0.071 and 0.81 and 0.084, respectively, while those of the conventional CRFs model and HMM are 0.78 and 0.091 and 0.77 and 0.099, respectively. Comparisons of their predictabilities show that the models designed for directional nonstationarity clearly perform better than the conventional models for all tested examples. Among them, the developed linear-chain CRFs model showed the best or close to the best performance with high predictability and a low training data requirement.

A Characterization of Oil Sand Reservoir and Selections of Optimal SAGD Locations Based on Stochastic Geostatistical Predictions

In the study, three-dimensional geostatistical simulations on McMurray Formation which is the largest oil sand reservoir in Athabasca area, Canada were performed, and the optimal site for steam assisted gravity drainage (SAGD) was selected based on the predictions. In the selection, the factors related to the vertical extendibility of steam chamber were considered as the criteria for an optimal site. For the predictions, 110 borehole data acquired from the study area were analyzed in the Markovian transition probability (TP) framework and three-dimensional distributions of the composing media were predicted stochastically through an existing TP based geostatistical model. The potential of a specific medium at a position within the prediction domain was estimated from the ensemble probability based on the multiple realizations. From the ensemble map, the cumulative thickness of the permeable media (i.e. Breccia and Sand) was analyzed and the locations with the highest potential for SAGD applications were delineated. As a supportive criterion for an optimal SAGD site, mean vertical extension of a unit permeable media was also delineated through transition rate based computations. The mean vertical extension of a permeable media show rough agreement with the cumulative thickness in their general distribution. However, the distributions show distinctive disagreement at a few locations where the cumulative thickness was higher due to highly alternating juxtaposition of the permeable and the less permeable media. This observation implies that the cumulative thickness alone may not be a sufficient criterion for an optimal SAGD site and the mean vertical extension of the permeable media needs to be jointly considered for the sound selections.

A Preliminary Study of Enhanced Predictability of Non-Parametric Geostatistical Simulation through History Matching Technique

ABSTRACT In the present study, an enhanced subsurface prediction algorithm based on a non-parametric geostatistical model and ahistory matching technique through Gibbs sampler is developed and the iterative prediction improvement procedure isproposed. The developed model is applied to a simple two-dimensional synthetic case where domain is composed of threedifferent hydrogeologic media with 500 m × 40 m scale. In the application, it is assumed that there are 4 independentpumping tests performed at different vertical interval and the history curves are acquired through numerical modeling.With two hypothetical borehole information and pumping test data, the proposed prediction model is applied iterativelyand continuous improvements of the predictions with reduced uncertainties of the media distribution are observed. Fromthe results and the qualitative/quantitative analysis, it is concluded that the proposed model is good for the subsurfaceprediction improvements where the history data is available as a supportive information. Once the proposed model be amatured technique, it is believed that the model can be applied to many groundwater, geothermal, gas and oil problemswith conventional fluid flow simulators. However, the overall development is still in its preliminary step and furtherconsiderations needs to be incorporated to be a viable and practical prediction technique including multi-dimensionalverifications, global optimization, etc. which have not been resolved in the present study. Key words :Geostatistical simulation, History matching, Groundwater, Subsurface prediction, Gibbs sampler

CO2 Leakage-Induced Contamination in Shallow Potable Aquifer and Associated Health Risk Assessment

Leakage of stored CO2 from a designated deep reservoir could contaminate overlying shallow potable aquifers by dissolution of arsenic-bearing minerals. To elucidate CO2 leakage-induced arsenic contamination, 2D multispecies reactive transport models were developed and CO2 leakage processes were simulated in the shallow groundwater aquifer. Throughout a series of numerical simulations, it was revealed that the movement of leaked CO2 was primarily governed by local flow fields within the shallow potable aquifer. The induced low-pH plume caused dissolution of aquifer minerals and sequentially increased permeabilities of the aquifer; in particular, the most drastic increase in permeability appeared at the rear margin of CO2 plume where two different types of groundwater mixed. The distribution of total arsenic ( As) plume was similar to the one for the arsenopyrite dissolution. The breakthrough curve of As monitored at the municipal well was utilized to quantify the human health risk. In addition, sensitivity studies were conducted with different sorption rates of arsenic species, CO2 leakage rates, and horizontal permeability in the aquifer. In conclusion, the human health risk was influenced by the shape of As plume, which was, in turn, affected by the characteristics of CO2 plume behavior such as horizontal permeability and CO2 leakage rate.

A subagging regression method for estimating the qualitative and quantitative state of groundwater@@@Méthode de régression par sous-échantillonnage et agrégation (subbaging) pour estimer l’état qualitatif et quantitative des eaux souterraines@@@Un método regresión por submuestreo para estimar el estado cualitativo y cuantitativo del agua subterránea@@@估算地下水定性和定量状态的集成回归法@@@Um método de regressão por agregação de subamostra para estimar o estado qualitativo e quantitativo das águas subterrâneas

A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.RésuméUne méthode de régression par sous-échantillonnage et agrégation (subbaging) (SBR) pour l’analyse des données relatives aux eaux souterraines se rapportant à l’évaluation des tendances associée à l’incertitude est proposée. La méthode SBR est validée en considérant des données synthétiques de manière compétitive vis-à-vis de méthodes conventionnelles robustes et non robustes. A partir des résultats, on vérifie que les précisions estimées de la méthode SBR sont cohérentes et supérieures à celles des autres méthodes, et que les incertitudes sont estimées de manière raisonnable : les autres ne disposent pas d’option d’analyse des incertitudes. Pour avancer dans le processus de validation, des données réelles relatives aux eaux souterraines sont utilisées et analysées en les comparant au processus gaussien de régression (GPR). Dans tous les cas, la tendance et les incertitudes associées sont estimées de manière raisonnable à la fois par SBR et par GPR, indépendamment des données gaussiennes ou non gaussiennes biaisées. Cependant, on s’attend à ce que le GPR ait une limitation dans ses applications à des données fortement entachées de valeurs aberrantes en raison de sa non-robustesse. A partir des mises en œuvre, on détermine que la méthode SBR a un potentiel pour faire l’objet de développement en tant qu’outil efficace de détection d’anomalies ou d’identification de valeurs aberrantes dans des données relatives à l’état des eaux souterraines telles que le niveau piézométrique ou la concentration en contaminants.ResumenSe propone un método de regresión de agregación de submuestreos (SBR) para el análisis de datos de agua subterránea relacionados con la incertidumbre asociada a la estimación de tendencias. El método SBR se valida competitivamente frente a los datos sintéticos de otros métodos robustos y no robustos convencionales. A partir de los resultados, se verifica que las precisiones de estimación del método SBR son consistentes y superiores a las de otros métodos, y las incertidumbres son razonablemente estimadas; los otros no tienen la opción del análisis de incertidumbres. Además para validar, se emplean los datos reales del agua subterránea y se analizan comparativamente con la regresión del proceso gaussiano (GPR). En todos los casos, la tendencia y las incertidumbres asociadas son razonablemente estimadas tanto por SBR como por GPR, independientemente de los datos gaussianos sesgados o no gaussianos. Sin embargo, se espera que GPR tenga una limitación en aplicaciones a datos gravemente corrompidos por valores atípicos debido a su no robustez. A partir de las implementaciones, se determina que el método SBR tiene el potencial de ser desarrollado como una herramienta eficaz de detección de anomalías o identificación de valores atípicos en datos de agua subterránea tales como el nivel de agua subterránea y la concentración de contaminantes.摘要本文提出了有关涉及趋势-估算不确定性分析地下水数据的子样品集聚(subagging) 回归法。相对于其它常规的强健的和非强健的方法,子样品集聚回归法经过了综合数据的验证。结果证实,子样品集聚回归法的估算精度始终如一,优于其它方法的估算精度,合理地估算了不确定性;其它方法没有不确定性选项。为了进一步进行验证,采用实际的地下水数据,并与高斯过程回归法对这些数据进行了对比分析。在所有情况中,无论是否高斯或者非高斯偏斜数据,利用子样品集聚回归法和高斯过程回归法对趋势和相关不确定性进行了合理估算。然而,预计高斯过程回归法在应用中对由于异常值的非-稳健性造成的严重损坏数据有局限。从实施的情况看,子样品集聚回归法作为地下水状况数据诸如地下水位和污染物含量异常探测或异常值识别的一个有效工具,具有进一步发展的潜力。ResumoUm método de regressão (RAS) por agregação de subamostra (subagging) foi proposto para a análise de dados de águas subterrâneas referentes à incerteza associada à estimativa de tendência. O método RAS foi validado contra dados sintéticos competitivamente com outros métodos convencionais robustos e não robustos. A partir dos resultados, verificou-se que as precisões de estimativa do método RAS foram consistentes e superiores às de outros métodos, e as incertezas foram razoavelmente estimadas; os demais não possuem opção de análise de incerteza. Para validar além, os dados de águas subterrâneas reais foram empregados e analisados comparativamente com a regressão de processo Gaussiano (RPG). Para todos os casos, a tendência e as incertezas associadas foram razoavelmente estimadas tanto pela RAS quanto pela RPG, independentemente de dados Gaussianos ou não Gaussianos. No entanto, espera-se que a RPG tenha uma limitação nas aplicações de dados gravemente corrompidos por dados discrepantes devido à sua não robustez. A partir das implementações, foi determinado que o método RAS tem potencial para ser desenvolvido como uma ferramenta eficaz de detecção de anomalias ou identificação de valores espúrios em dados de águas subterrâneas, tais como o nível das águas subterrâneas e a concentração de contaminantes.

Un método regresión por submuestreo para estimar el estado cualitativo y cuantitativo del agua subterránea

A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.RésuméUne méthode de régression par sous-échantillonnage et agrégation (subbaging) (SBR) pour l’analyse des données relatives aux eaux souterraines se rapportant à l’évaluation des tendances associée à l’incertitude est proposée. La méthode SBR est validée en considérant des données synthétiques de manière compétitive vis-à-vis de méthodes conventionnelles robustes et non robustes. A partir des résultats, on vérifie que les précisions estimées de la méthode SBR sont cohérentes et supérieures à celles des autres méthodes, et que les incertitudes sont estimées de manière raisonnable : les autres ne disposent pas d’option d’analyse des incertitudes. Pour avancer dans le processus de validation, des données réelles relatives aux eaux souterraines sont utilisées et analysées en les comparant au processus gaussien de régression (GPR). Dans tous les cas, la tendance et les incertitudes associées sont estimées de manière raisonnable à la fois par SBR et par GPR, indépendamment des données gaussiennes ou non gaussiennes biaisées. Cependant, on s’attend à ce que le GPR ait une limitation dans ses applications à des données fortement entachées de valeurs aberrantes en raison de sa non-robustesse. A partir des mises en œuvre, on détermine que la méthode SBR a un potentiel pour faire l’objet de développement en tant qu’outil efficace de détection d’anomalies ou d’identification de valeurs aberrantes dans des données relatives à l’état des eaux souterraines telles que le niveau piézométrique ou la concentration en contaminants.ResumenSe propone un método de regresión de agregación de submuestreos (SBR) para el análisis de datos de agua subterránea relacionados con la incertidumbre asociada a la estimación de tendencias. El método SBR se valida competitivamente frente a los datos sintéticos de otros métodos robustos y no robustos convencionales. A partir de los resultados, se verifica que las precisiones de estimación del método SBR son consistentes y superiores a las de otros métodos, y las incertidumbres son razonablemente estimadas; los otros no tienen la opción del análisis de incertidumbres. Además para validar, se emplean los datos reales del agua subterránea y se analizan comparativamente con la regresión del proceso gaussiano (GPR). En todos los casos, la tendencia y las incertidumbres asociadas son razonablemente estimadas tanto por SBR como por GPR, independientemente de los datos gaussianos sesgados o no gaussianos. Sin embargo, se espera que GPR tenga una limitación en aplicaciones a datos gravemente corrompidos por valores atípicos debido a su no robustez. A partir de las implementaciones, se determina que el método SBR tiene el potencial de ser desarrollado como una herramienta eficaz de detección de anomalías o identificación de valores atípicos en datos de agua subterránea tales como el nivel de agua subterránea y la concentración de contaminantes.摘要本文提出了有关涉及趋势-估算不确定性分析地下水数据的子样品集聚(subagging) 回归法。相对于其它常规的强健的和非强健的方法,子样品集聚回归法经过了综合数据的验证。结果证实,子样品集聚回归法的估算精度始终如一,优于其它方法的估算精度,合理地估算了不确定性;其它方法没有不确定性选项。为了进一步进行验证,采用实际的地下水数据,并与高斯过程回归法对这些数据进行了对比分析。在所有情况中,无论是否高斯或者非高斯偏斜数据,利用子样品集聚回归法和高斯过程回归法对趋势和相关不确定性进行了合理估算。然而,预计高斯过程回归法在应用中对由于异常值的非-稳健性造成的严重损坏数据有局限。从实施的情况看,子样品集聚回归法作为地下水状况数据诸如地下水位和污染物含量异常探测或异常值识别的一个有效工具,具有进一步发展的潜力。ResumoUm método de regressão (RAS) por agregação de subamostra (subagging) foi proposto para a análise de dados de águas subterrâneas referentes à incerteza associada à estimativa de tendência. O método RAS foi validado contra dados sintéticos competitivamente com outros métodos convencionais robustos e não robustos. A partir dos resultados, verificou-se que as precisões de estimativa do método RAS foram consistentes e superiores às de outros métodos, e as incertezas foram razoavelmente estimadas; os demais não possuem opção de análise de incerteza. Para validar além, os dados de águas subterrâneas reais foram empregados e analisados comparativamente com a regressão de processo Gaussiano (RPG). Para todos os casos, a tendência e as incertezas associadas foram razoavelmente estimadas tanto pela RAS quanto pela RPG, independentemente de dados Gaussianos ou não Gaussianos. No entanto, espera-se que a RPG tenha uma limitação nas aplicações de dados gravemente corrompidos por dados discrepantes devido à sua não robustez. A partir das implementações, foi determinado que o método RAS tem potencial para ser desenvolvido como uma ferramenta eficaz de detecção de anomalias ou identificação de valores espúrios em dados de águas subterrâneas, tais como o nível das águas subterrâneas e a concentração de contaminantes.