Integration of geoelectric and geochemical data using Self-Organizing Maps (SOM) to characterize a landfill

Leachates from garbage dumps can significantly compromise their surrounding area. Even if the distance between these and the populated areas could be considerable, the risk of affecting the aquifers for public use is imminent in most cases. For this reason, the delimitation and monitoring of the leachate plume are of significant importance. Geoelectric data (resistivity and IP), and surface methane measurements, are integrated and classified using an unsupervised Neural Network to identify possible risk zones in areas surrounding a landfill. The Neural Network used is a Kohonen type, which generates; as a result, Self-Organizing Classification Maps or SOM (Self-Organizing Map). Two graphic outputs were obtained from the training performed in which groups of neurons that presented a similar behaviour were selected. Contour maps corresponding to the location of these groups and the individual variables were generated to compare the classification obtained and the different anomalies associated with each of these variables. Two of the groups resulting from the classification are related to typical values of liquids percolated in the landfill for the parameters evaluated individually. In this way, a precise delimitation of the affected areas in the studied landfill was obtained, integrating the input variables via SOMs. The location of the study area is not detailed for confidentiality reasons.Comment: 11 pages, 7 figure

Modeling of soil weathering on hillslopes : coping with nonlinearity and coupled processes using a data-driven approach

Orientadores: Carlos Roberto de Souza Filho, Michael James FriedelTese (doutorado) - Universidade Estadual de Campinas, Instituto de GeociênciasResumo: Esta tese de doutorado tem como objetivo aprofundar o conhecimento sobre as relações das propriedades físico-quimicas do solo com a morfometria do relevo, buscando quantificar essas relações para a construção de modelos conceituais e preditivos. Mapas auto-organizáveis e modelos de sistemas de informação geográfica foram utilizados para investigar as relações não lineares associadas ao intemperismo químico e físico, fatores associados a fenômenos hidrológicos e à evolução dos solos. Três estudos de caso são apresentados: o intemperismo químico de solo no estado do Paraná (22 variáveis e 304 amostras), o transporte físico de sedimentos em Poços de Caldas (9 variáveis e 29 amostras), e hidroquímica de aqüíferos na Formação Serra Geral no Estado do Paraná (27 variáveis e 976 amostras). O método combinando simulação estocástica e mineração de dados permitiu explorar as relações entre relevo, granulometria e geoquímica dos solos. Regiões mais elevadas e com morfometria convexa apresentaram alta denudação de elementos móveis (e.g., Ca) e baixa de elementos pouco móveis (e.g., Al). O mesmo padrão foi observado para granulometria de solos, ou seja, alta proporção de areia em áreas altas e convexas da bacia e altos teores de argila, com baixa condutividade hidráulica, em regiões convexas próximas aos canais de drenagem. O comportamento espacial da hidroquímica das águas do aqüífero Serra Geral apontou áreas de potencial conectividade entre aqüíferos, áreas de recarga recente e de alto tempo de residência. Foram construídos modelos preditivos não tendenciosos das propriedades do solo em subsuperfície partindo da premissa de que o intemperismo e a morfometria se relacionam através de um processo duplamente dependente, onde a denudação física e química atua no delineamento do relevo e a morfometria do terreno é um fator que caracteriza as condições físico-químicas do soloAbstract: This Doctoral thesis aims to explore the relationship between soil physical-chemical properties and relief morphometry, and quantifying these relationships to build conceptual and predictive models. Self-organizing maps and Geographic Information Systems modeling are here used to investigate nonlinear correlations associated with chemical and physical denudation; which are factors connected with hydrological phenomena and soil evolution. Three study cases are presented: soil chemical weathering within the limits of the Parana State, southern Brazil (22 variables and 304 samples), physical transport of sediments in the alkaline intrusive complex of Poços de Caldas, southeastern Brazil (9 variables and 29 samples), and hydrochemistry of Serra Geral aquifers also in the Parana State (27 variables and 976 samples). The method combining stochastic simulation and data mining allows exploring the relationships between topography, soil texture and soil geochemistry. In the Parana State, higher regions and areas with convex morphometry shows, respectively, higher and lower denudation rates of mobile (e.g., Ca) and less mobile (e.g., Al) elements. The same pattern is observed for soil particle size. In this case, high proportion of sand is found in highlands and convex areas inside the basin, and high clay content, with low hydraulic conductivity, occurs in convex regions, near drainage channels. The spatial behavior of the Serra Geral aquifer?s hydrochemistry pointed out to areas with potential connectivity with the Guarani aquifer system, recent recharge areas, and long-standing waters. Predictive, unbiased models are built for soil properties on the premise that weathering and morphology are related through a two-way dependent process, where the physical and chemical denudation delineates the elevations of the land surface, and terrain morphometry is a factor that characterizes the physical-chemical conditions of the soilDoutoradoGeologia e Recursos NaturaisDoutor em Ciência

Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map

The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial knowledge into the algorithm. The performance of the GEO3DSOM is compared to the performance of the standard SOM in analyzing an artificial data set and a hydrochemical data set. The hydrochemical data set consists of 131 groundwater samples collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. Both techniques succeed very well in providing more insight in the groundwater quality data set, visualizing the relationships between variables, highlighting the main differences between groups of samples and pointing out anomalous wells and well screens. The GEO3DSOM however has the advantage to provide an increased resolution while still maintaining a good generalization of the data set

A self-organizing map approach to characterize hydrogeology of the fractured Serra-Geral transboundary aquifer

Abstract The aim of this work is to understand the exchange of water between the Serra Geral aquifer system (SGAS) and Guarani aquifer system (GAS). The objectives are two-fold. First, introduce the capability of the modified self-organizing maps (MSOM) as an unbiased nonlinear approach to estimate missing values of hydrochemistry and hydraulic transmissivity associated with the SGAS, a transboundary groundwater system spanning parts of four South American countries. Second, identify areas with potential connectivity of the SGAS with the GAS based on analysis of the spatial variability of key elements and comparison with current conceptual models of hydraulic connectivity. The MSOM is employed to calculate correlations (trends) between 27 variables from 1,132 wells. Hydraulic transmissivity is calculated from specific capacity values from well-pump tests in 157 locations. Hydrochemical facies estimates appear unbiased and consistent with current conceptual-connectivity models indicating that vertical fluxes from GAS are influenced by geological structure. The MSOM provides additional spatial estimates revealing new areas with likely connections between the two aquifer systems

Hybrid spectral unmixing : using artificial neural networks for linear/non-linear switching

Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms

Predicting Missing Seismic Velocity Values Using Self-Organizing Maps to Aid the Interpretation of Seismic Reflection Data from the Kevitsa Ni-Cu-PGE Deposit in Northern Finland

We use self-organizing map (SOM) analysis to predict missing seismic velocity values from other available borehole data. The site of this study is the Kevitsa Ni-Cu-PGE deposit within the mafic-ultramafic Kevitsa intrusion in northern Finland. The site has been the target of extensive seismic reflection surveys, which have revealed a series of reflections beneath the Kevitsa resource area. The interpretation of these reflections has been complicated by disparate borehole data, particularly because of the scarce amount of available sonic borehole logs and the varying practices in logging of borehole lithologies. SOM is an unsupervised data mining method based on vector quantization. In this study, SOM is used to predict missing seismic velocities from other geophysical, geochemical, geological, and geotechnical data. For test boreholes, for which measured seismic velocity logs are also available, the correlation between actual measured and predicted velocities is strong to moderate, depending on the parameters included in the SOM analysis. Predicted reflectivity logs, based on measured densities and predicted velocities, show that some contacts between olivine pyroxenite/olivine websterite-dominant host rocks of the Kevitsa disseminated sulfide mineralization—and metaperidotite—earlier extensively used “lithology” label that essentially describes various degrees of alteration of different olivine pyroxenite variants—are reflective, and thus, alteration can potentially cause reflectivity within the Kevitsa intrusion

Data mining of petrophysical and lithogeochemical borehole data to elucidate the origin of seismic reflectivity within the Kevitsa Ni-Cu-PGE -bearing intrusion, northern Finland

The Kevitsa mafic-ultramafic intrusion, located within the Central Lapland Greenstone Belt in northern Finland, hosts a large, disseminated Ni-Cu-PGE sulphide deposit. A three-dimensional seismic reflection survey was conducted over the Kevitsa intrusion in 2010 primarily for open-pit mine planning and for deep mineral exploration purposes. In the Kevitsa three-dimensional seismic data, laterally continuous reflections are observed within a constrained region within the intrusion. In earlier studies, it has been suggested that this internal reflectivity mainly originates from contacts between the tops and more sulphide-rich bottoms of smaller scale, internally differentiated magma layers that represent a spectrum of olivine pyroxenites. However, this interpretation is not unequivocally supported by the borehole data. In this study, data mining, namely the Self-Organizing Map analysis, of extensive Kevitsa borehole data is used to investigate the possible causes for the observed internal reflectivity within the Kevitsa intrusion. Modelling of the effect of mineralization and alteration on the reflectivity properties of Kevitsa rock types, based on average modal compositions of the rock types, is presented to support the results of the Self-Organizing Map analysis. Based on the results, we suggest that the seismic reflectivity observed within the Kevitsa intrusion can possibly be attributed to alteration, and may also be linked to the presence of sulphide minerals.Peer reviewe

Use of self-organizing maps to estimate furrow sediment loss in western U.S.

The area irrigated by furrow irrigation in the U.S. has been steadily decreasing but still represents about 20% of the total irrigated area in the U.S. Furrow irrigation sediment loss is a major water quality issue in the western U.S. and a method for estimating sediment loss is needed to quantify the environmental impacts and estimate effectiveness and economic value of conservation practices. The objective of the study was to investigate the use of the unsupervised machine learning technique Kohonen self-organizing maps (KSOM) to predict furrow sediment loss. Historical published and unpublished data sets containing measurements of furrow irrigation sediment loss in the western U.S. were assembled into a furrow sediment loss data set comprising over 2000 furrows. Despite the immunity of KSOMs to parameter variability, the inherent variability in measured furrow sediment loss limited the ability of a KSOM model to reliability predict furrow sediment loss. Furrow sediment loss was under predicted by 44% on average with a linear regression coefficient of determination of 0.6. The KSOM model was placing little weight on measured sediment loss in the input data set, indicating that it was clustering the data based on input parameters defining hydraulic and soil conditions. This outcome was used to develop a transfer learning approach for predicting furrow sediment loss. The transfer learning approach used a KSOM to cluster data records of similar of hydraulic and soil conditions in the data set. Mean measured sediment loss and furrow flow rate of each cluster was determined based on data set vectors assigned to a cluster by the KSOM. Furrow sediment loss prediction was obtained by applying an input vector to the KSOM to identify the cluster the input vector most closely matches. Then the mean measured sediment loss of the identified cluster was adjusted for any difference between the input vector furrow flow rate and cluster mean furrow flow rate to obtain a prediction of furrow sediment loss. Predicted furrow sediment loss was 16% less than measured sediment loss on average with a coefficient of determination of 0.82. When the data set was randomly split into model development (90%) and validation (10%) data sets the prediction results were similar