Thesis (M. S.)--Stanford University, 2001. / Submitted to the Department of Geological and Environmental Sciences. Copyright by the author.

A lucratividade da indústria mineira é diretamente dependente do planejamento adequado de todas as fases de extração e beneficiamento de minério. Cada passo deste processo por sua vez, também é dependente dos resultados da fase anterior. As usinas de beneficiamento, por exemplo, devem ser alimentadas por um material o mais homogêneo possível, o que é garantido por um planejamento adequado de lavra e de forma muito eficaz por pilhas de homogeneização. No entanto, os métodos de estimativa comumente utilizados para prever os teores dos blocos que formam esses sistemas, não medem adequadamente a incerteza associada a este processo de estimativa. Este estudo avalia a eficácia da simulação geoestatística na previsão da variabilidade in situ dos teores e planejamento de pilhas de homogeneização. O método gera múltiplos cenários equiprováveis para o depósito que devem reproduzir os parâmetros estatísticos das amostras, o histograma e o variograma do fenômeno. Para um dado plano de lavra, o conjunto de cenários simulados irá gerar um conjunto de valores equiprováveis para cada pilha de homogeneização. A metodologia proposta foi aplicada a um depósito de fosfato na região central do Brasil. Neste caso de estudo, os teores de P2O5 previstos pelo plano de lavra de curto prazo e amostrados na área industrial foram comparados com o grupo de valores gerados para as pilhas por meio da simulação. A reprodução dos resultados demonstrou a aplicabilidade da metodologia para o depósito em questão. Além disto, a simulação geoestatística demonstrou ser uma ferramenta muito eficiente na previsão da variabilidade in situ dos teores e planejamento de pilhas de homogeneização. / Profits in mineral industry strongly depend on proper planning at all stages of mining and ore processing, and every step of these processes depends on the results from the previous stage. These chains of events can be illustrated, for instance, by the processing plant. To maximize ore recovery, among various factors, the processing plant must be fed by a material as homogeneous as possible minimizing the variance of the head grades that can be achieved initially by a proper mining plan and optimal schedule and more effectively by homogenization piles. The key factor is to know the input grade variance to design a blending system capable of attenuating it. Estimation methods commonly used to predict block grades which form the geological model used for mine planning do not adequately measure the variance associated with these estimates. This study evaluates geostatistical simulation in predicting in situ grades variability and planning blending piles. The method generates multiple, equally probable grade scenarios for the deposit, which reproduce the values of the samples, the histogram, and the variograma of the attribute being simulated. For a given mining plan, the set of simulated scenarios will generate a group of equiprobable values for each homogenization pile. These results provide the means to assess a range of possible values each pile can have. To validate the suggested methodology, the contents of P2O5 obtained by the short-term mining plan and sampled at the processing plant from a large phosphate mine in central Brazil were compared against the simulated values. The results matched adequately demonstrating that geostatistical simulation and pile emulation methodology are efficient tools that could be applied in predicting grades in situ variability and in planning blending piles.

A lucratividade da indústria mineira é diretamente dependente do planejamento adequado de todas as fases de extração e beneficiamento de minério. Cada passo deste processo por sua vez, também é dependente dos resultados da fase anterior. As usinas de beneficiamento, por exemplo, devem ser alimentadas por um material o mais homogêneo possível, o que é garantido por um planejamento adequado de lavra e de forma muito eficaz por pilhas de homogeneização. No entanto, os métodos de estimativa comumente utilizados para prever os teores dos blocos que formam esses sistemas, não medem adequadamente a incerteza associada a este processo de estimativa. Este estudo avalia a eficácia da simulação geoestatística na previsão da variabilidade in situ dos teores e planejamento de pilhas de homogeneização. O método gera múltiplos cenários equiprováveis para o depósito que devem reproduzir os parâmetros estatísticos das amostras, o histograma e o variograma do fenômeno. Para um dado plano de lavra, o conjunto de cenários simulados irá gerar um conjunto de valores equiprováveis para cada pilha de homogeneização. A metodologia proposta foi aplicada a um depósito de fosfato na região central do Brasil. Neste caso de estudo, os teores de P2O5 previstos pelo plano de lavra de curto prazo e amostrados na área industrial foram comparados com o grupo de valores gerados para as pilhas por meio da simulação. A reprodução dos resultados demonstrou a aplicabilidade da metodologia para o depósito em questão. Além disto, a simulação geoestatística demonstrou ser uma ferramenta muito eficiente na previsão da variabilidade in situ dos teores e planejamento de pilhas de homogeneização. / Profits in mineral industry strongly depend on proper planning at all stages of mining and ore processing, and every step of these processes depends on the results from the previous stage. These chains of events can be illustrated, for instance, by the processing plant. To maximize ore recovery, among various factors, the processing plant must be fed by a material as homogeneous as possible minimizing the variance of the head grades that can be achieved initially by a proper mining plan and optimal schedule and more effectively by homogenization piles. The key factor is to know the input grade variance to design a blending system capable of attenuating it. Estimation methods commonly used to predict block grades which form the geological model used for mine planning do not adequately measure the variance associated with these estimates. This study evaluates geostatistical simulation in predicting in situ grades variability and planning blending piles. The method generates multiple, equally probable grade scenarios for the deposit, which reproduce the values of the samples, the histogram, and the variograma of the attribute being simulated. For a given mining plan, the set of simulated scenarios will generate a group of equiprobable values for each homogenization pile. These results provide the means to assess a range of possible values each pile can have. To validate the suggested methodology, the contents of P2O5 obtained by the short-term mining plan and sampled at the processing plant from a large phosphate mine in central Brazil were compared against the simulated values. The results matched adequately demonstrating that geostatistical simulation and pile emulation methodology are efficient tools that could be applied in predicting grades in situ variability and in planning blending piles.

O objetivo desse trabalho foi estabelecer planos de amostragem com redução no tamanho amostral, a partir de conjuntos de dados com dependência espacial, que fossem eficientes na predição de localizações não amostradas e que gerassem estimativas eficientes de características relacionadas na predição espacial. Esses planos amostrais reduzidos foram obtidos por processos de otimização denominados recozimento simulado e algoritmo genético híbrido, considerando a média da variância da predição espacial, obtida pelo método de interpolação chamado krigagem, como função objetivo minimizada. Para isso, utilizaram-se conjuntos de dados simulados, com diferentes valores de alcance e efeito pepita, cujo intuito foi identificar a influência que esses parâmetros exercem na escolha da configuração amostral otimizada. Para cada conjunto de dados simulados, foram obtidas amostras pelos processos de otimização e seus resultados foram comparados aos esquemas de amostragem: aleatório, sistemático, sistemático centrado adicionado de delineamentos menores e sistemático centrado adicionado de pontos próximos. Os resultados mostraram que os planos de amostragem otimizados, principalmente os planos obtidos pelo algoritmo genético híbrido, produziram menores estimativas para a média da variância da krigagem e melhores estimativas para a porcentagem e soma de valores preditos acima do terceiro quartil e do percentil 90, que s~ao características relacionadas na predição espacial. Observou-se, também, que o aumento do tamanho amostral produziu melhores estimativas para todos os resultados analisados e, independente do valor de alcance e efeito pepita, a amostragem otimizada pelo algoritmo genético híbrido produziu melhores resultados. Além disso, obtiveram-se conjuntos amostrais reduzidos de 128 parcelas pelo algoritmo genético híbrido, pelo processo de recozimento simulado e pelas amostragens aleatória e sistemática, para a propriedade química teor de potássio pertencente ao conjunto de dados, com 256 parcelas, de um experimento de agricultura de precisão em uma área experimental. Por intermédio dos dados resultantes dessas amostragens, realizou-se uma analise geoestatística para identificar o comportamento de dependência espacial da variável potássio na área e foram feitas predições espaciais do potássio em localizações n~ao amostradas nessa mesma área. Em todos os esquemas de amostragem utilizados, os valores preditos foram classificados segundo o critério de adubação do potássio no Paraná em culturas de soja (EMATER - Empresa Paranaense de Assistência Técnica e Extensão Rural, 1998). Esses resultados foram comparados com a analise realizada no conjunto de dados inicial e observou-se uma maior similaridade desses resultados com os obtidos pela analise realizada através dos dados da amostragem obtida pelo algoritmo genético híbrido. Assim, tiveram-se evidências de que a redução em 50% do tamanho amostral do conjunto de dados da variável potássio, utilizando nessa redução uma amostragem obtida pelo algoritmo genético híbrido, produziu resultados eficientes para a classificação de adubação de potássio na área em estudo, reduzindo em 50% os custos 8 com analise química do solo, sem grande perda de eficiência nas conclusões obtidas pela predição espacial. / The aim of this work was to establish plans for sampling with reduced in the sample size, from sets of dependent spatial data, and they are eficients in terms of prediction of the nonsampled observations and prediction of linear targets. These plans were obtained by sampling reduced processes optimization of the algorithm called simulated annealing and hybrid genetic algorithm, considering the average kriging variance as objective function to be minimised. Therefore, it was used simulated data sets, With diferent values of range and nugget efect, whose aim was to identify the in uence that these exercise parameters in choosing the sample configuration foptimized. For each set of data simulated samples were obtained through the optimization process and its results were compared to sampling schemes: random, systematic, lattice plus in ll and lattice plus close pairs. The results show that the sampling plans optimized, especially the plans obtained by hybrid genetic algorithm, produced lower estimates for the average kriging variance and best estimates for the percentage and amount of predicted values above the third quartile and the 90 percentile, which are characteristics related to spatial prediction. It was also observed that the increase of sample size produces best estimate for all results analyzed and independent of the value range and nugget efect, sampling optimized by the hybrid genetic algorithm produced better results. In addition, sets up sampling reduced of 128 samples by sampling schemes: hybrid genetic algorithm, simulated annealing, random and systematic, for the property belonging to the chemical potassium set data, with 256 samples, an experiment of precision agriculture, in an experimental area. Through these sampling data, an analysis was carried out geostatistics to identify the behavior of spatial dependence of the variable potassium in the area under study and predictions were made of potassium space in locations not sampled that same area. In all sampling schemes used, the predicted values were classified at the discretion of the potassium fertilization in the cultivation of soybeans in Parana (EMATER-PARANA, 1998). These results were compared with the analysis in the initial set of data and there was a greater similarity of these results with those obtained by the analysis performed by data obtained by the sampling hybrid genetic algorithm. So there has been evidence that the reduction by 50% of the sample size of the data set of variable potassium, using this reduction a sample obtained by the hybrid genetic algorithm, produced efective results for classification of potassium fertilizer in the area under study, reducing by 50% the costs with chemical analysis of soil, without much loss of eficiency in the conclusions obtained by predicting space.

Groundwater level models have an important role in the development and application of water management and policies. Understanding the temporal and spatial variations of groundwater levels in the Gorran water protection area in Nynäshamn is important for developing management strategies. Geostatistical analysis with several different methods was used to compare groundwater level records for 13 observation wells in Gorran from 1994 to 2012. The performance of the different methods was evaluated by using mean absolute error (MAE) and root mean square error (RMSE) metrics. The results showed that geostatistical methods had a higher general accuracy when utilizing the Empirical Bayesian kriging (EBK) method in Gorran. EBK was the best method with highest precision and lowest mean absolute error. Cross-validation was also applied to evaluate the best (smallest) root mean square error (RMSE). A predicted potentiometric groundwater level was estimated from the basis of the available digital elevation model to extend the observation area within the same geological specification. / Grundvattennivåns modeller har en viktig roll i utvecklingen och tillämpningen av vattenförvaltning och politik. Genom att förstå de tidsmässiga och geografiska variationer grundvattennivån i Gorran skyddat område är viktigt för att utveckla hanteringens strategier.Geostatistical analys med olika modeller som Inverse Distanse Weighted, Radial Basic Funktion och kriging modeller användes för att jämföra grundvattennivåns rekord under 13 observations brunnar 1994 till 2012. Prestandan hos metoder utvärderades med hjälp av medelabsolutfelet (MAE) och rot medelkvadratfelet (RMSE). Resultatet visade att geostatistiska metoder hade högre noggrannhet och Empirical Bayesian kriging (EBK) var den bästa metoden med högsta precision och lägst medelabsolutfel. Cross-validering applicerades också för att utvärdera de bästa minsta kvadratiska medelvärdet (RMSE). Förutsatt potentiometrisk grundvattennivå genomfördes från digital höjdmodell för att förlänga observations område inom samma geologiska specifikation.

Through a discipline termed “comparative sedimentology”, modern carbonate depositional environments have been used extensively as analogs to aid in the interpretation of equivalent fossil systems. Using field samples, GIS and remote sensing data for three isolated carbonate platforms in the Pacific, this thesis seeks to examine relationships between grain texture and grain type and their environment of deposition. The motivation is to highlight relationships that have the potential to better understand facies relations on carbonate platforms, and thereby reduce uncertainty and increase accuracy of subsurface exploration. The results of this study show that on Raivavae, Tubuai, and Bora Bora: French Polynesia grain texture and type of collected sediment samples could be used to predict water depth and relative distance lagoonward from the reef rim with ≥ 73% and ≥ 67% accuracy, respectively. The predictive relationships; however, were largely site specific. The exception being that the same relationship between water depth and the abundance of mud and coral could be used on both Raivavae (accuracy = 81%) and Tubuai (accuracy = 78%). Additionally, the abundance of coral and Halimeda in sediment samples were able to classify samples as belonging to either the platform margin or platform interior environments on Raivavae, Tubuai, and Bora Bora with 75%, 65%, and 65% accuracy, respectively. Overall, the results of this study suggest that the abundance of coral holds potential to be utilized as a proxy for distance from the reef rim on modern and ancient isolated carbonate platforms dating back to the Miocene geological epoch.

No description available.

The aim of this Master’s Thesis is noise map creation and processing of measured data using ArcView 9.2 and its extensions. One - year Student Edition of ArcView 9.2 and its extensions have been provided by ARCDATA PRAHA, s.r.o. Noise maps are created from measured data in the neighborhood of the faculty building and the Moravian Square in Brno. Measurement of geographic position of measuring points is realized by GPS. For creation of noise maps, analysis and processing of measured data ArcGIS extensions - Spatial Analyst and Geostatistical Analyst were used. The last part of this Master’s Thesis is specialized on creation of 3D landscape model using ArcGIS 3D Analyst extension.

The aim of this Master’s Thesis was mapping of electromagnetic fields and processing of measured data using ArcView 9.2 and its extensions. Software ArcView 9.2 with extensions was provided by university to create this Master’s Thesis. Maps of electromagnetic fields are created from measured data in the neighborhood of the faculty building. Measurement of geographic position of measuring points is realized by GPS. For creation of maps and further analysis in ArcView, ArcGIS extensions - Spatial Analyst, Geostatistical Analyst and 3D Analyst were used.

A storage and enhanced coalbed methane (CO2-ECBM) test will store up to 20,000 tons of carbon dioxide in a stacked coal reservoir in southwest Virginia. The test involves two phases of CO2 injection operations. Phase I was conducted from July 2, 2015 to April 15, 2016, and injected a total of 10, 601 tons of CO2. After a reservoir soaking period of seven months, Phase II is scheduled to begin Fall 2016. The design of the monitoring program for the test considered several site-specific factors, including a unique reservoir geometry, challenging surface terrain, simultaneous CBM production activities which complicate the ability to attribute signals to sources. A multi-scale approach to the monitoring design incorporated technologies deployed over different, overlapping spatial and temporal scales selected for the monitoring program include dedicated observation wells, CO2 injection operations monitoring, reservoir pressure and temperature monitoring, gas and formation water composition from offset wells tracer studies, borehole liquid level measurement, microseismic monitoring, surface deformation measurement, and various well logs and tests. Integrated interpretations of monitoring results from Phase I of the test have characterized enhanced permeability, geomechanical variation with depth, and dynamic reservoir injectivity. Results have also led to the development of recommended injection strategy for CO2-ECBM operations. The work presented here describes the development of the monitoring program, including design considerations and rationales for selected technologies, and presents monitoring results and interpretations from Phase I of the test. / Ph. D. / Recent efforts to manage and reduce atmospheric carbon dioxide (CO₂) emissions include the development of technologies for carbon capture, utilization, and storage (CCUS) operations. CCUS technologies are used to capture CO₂ emissions from a power plant or other point source, transport the captured CO₂ to a field site, and inject the CO₂ underground into a geologic reservoir. There it is securely stored within a deep, sealed geologic formation and/or is utilized to enhance oil or gas recovery from the formation. CCUS operations conducted on a commercial scale could play an important role in combating anthropogenic climate change. Field tests for carbon storage and utilization operations support the objective of scaling up by demonstrating the storage potential of target reservoirs, the profit potential from enhanced recovery, and the safety of all field operations. Field tests are monitored intensively in order to understand reservoir behavior in response to CO₂ injection and to evaluate progress toward project objectives. An ongoing small-scale carbon storage and utilization test in southwest Virginia is testing the potential for CO₂ storage and enhanced gas recovery from a depleted coalbed methane reservoir. The carbon storage and enhanced coalbed methane (CO₂-ECBM) test will store up to 20,000 tons of carbon dioxide in a coal reservoir composed of approximately 20 individual seams. The test involves two phases of CO₂ injection operations. Phase I was conducted from July 2, 2015, to April 15, 2016, and injected a total of 10,601 tons of CO₂. After a reservoir soaking period of seven months, Phase II is scheduled to begin in Fall 2016. The design of the monitoring program for the test considered several site-specific factors, including a unique reservoir geometry, challenging surface terrain, and simultaneous coalbed methane production activities which complicate the ability to attribute signals to sources. A multi-scale approach to the monitoring design incorporated technologies deployed over different, overlapping spatial and temporal scales. The work presented here describes the development of the monitoring program, including design considerations and rationales for selected technologies, and presents monitoring results and interpretations from Phase I of the test.