Geochemical relations among elements in stream sediment samples from Siojan Prospecting Area, Iran using geostatistical methods

Stream sediment samples play an important role in identifying potential areas of metallic and non-metallic mineralization in mineral exploration studies. The relationship of geochemical elements with each other shows how the elements are distributed in the area. Also, by identifying related elements, sampling and targeted chemical analysis can be used in the next stages of exploration. The purpose of this study is to investigate the elements related to the copper element in the Siojan prospecting area, which is located in South-Khorasan province and 30 km northwest of Birjand city of Iran. In Siojan area, 120 stream sediment samples of a 60 square kilometer area were collected to detect geochemical anomalies and were consequently analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for 45 elements. Preliminary geological studies showed that the studied area has copper mineralization potential, and therefore, copper was selected as the target element in this study. Copper trace elements were identified in the area and the results were used to identify copper mineralized anomalies. For the elemental analysis data, methods of Principal Component Analysis (PCA), Factor Analysis (FA), Hierarchical Cluster Analysis (HCA) and K-Means Clustering were performed to identify the relevant elements and relationships among them. Statistical analysis of the concentration of geochemical elements in the region revealed that copper and cobalt elements were identified as two elements of the same family in terms of geochemical genetics. The average value for copper and cobalt elements in the analyzed samples was 27.2 ppm and 15.5 ppm, respectively. Finally, the relationship between copper and cobalt elements was modeled as an equation using the K-Means Clustering algorithm

Machine learning aplicado na caracterização da assinatura petrofísica, espectral e geoquímica dos depósitos auríferos da Serra de Jacobina, Cráton São Francisco

Tese (doutorado) — Universidade de Brasília, Instituto de Geociências, Programa de Pós-Graduação em Geologia, 2022.Neste trabalho foram adquiridas variáveis categóricas e numéricas relacionadas a propriedades físicas das rochas, tais como densidade, susceptibilidade magnética, condutividade elétrica, concentração de radioelementos, reflectância entre outras propriedades químicas. As análises químicas de rocha foram obtidas através de medidas in situ de fluorescência de raios-X portátil (pXRF). Adicionalmente, foram analisadas descrições petrográficas e análises de química mineral quantitativas e semiquantitativas em amostras chave para a compreensão do sistema mineral. Ao todo, foram processadas 1950 análises de pXRF, 2484 medidas de espectrorradiometria, 7490 medidas de susceptibilidade magnética, 5720 medidas de condutividade elétrica, 598 medidas de densidade, 541 análises de química mineral (ablação de laser de espectrômetro de massa, LA-ICP-MS) e 304 medidas de radioelementos, além de 20 análises petrográficas por microscópio óptico e 5 análises por microscópio eletrônico. Utilizamos abordagens supervisionadas para fazer previsões e fornecer informações sobre as mineralizações auríferas em rochas do Grupo Jacobina, Cráton do São Francisco, usando os parâmetros petrofísicos e litogeoquímicos em escala de amostra. Um modelo de aprendizado de máquina baseado no algoritmo Random Forests foi aplicado para prever a mineralização em amostras de testemunho de sondagem. As acurácias médias foram de 0,87 para treinamento de validação cruzada, 0,91 para os dados de teste e 0,86 para previsão de todas as amostras. O resultado permitiu estimar a importância das variáveis de entrada para a predição e essas estimativas foram validadas por uma interpretação petrográfica de microscopia óptica e eletrônica de varredura, que foram realizadas para esclarecer a relação entre minerais de diferentes estágios com a mineralização do ouro. Paralelamente, utilizamos abordagens nãosupervisionadas para extrair informações sobre a estruturação das amostras nos dados de LAICP-MS e de reflectância espectral. Usamos métodos de Agrupamento Aglomerativo (Hierarchical Clustering) para avaliar os padrões de elementos traços de acordo com o tipo de pirita (detrítica ou epigenéticas) e níveis estratigráficos. Em seguida, implementamos a técnica Uniform Manifold Approximation and Projection (UMAP) para reduzir a dimensionalidade avaliada para uma projeção bidimensional buscando inspecionar a estrutura interna dos dados. Elementos como Cu, Zn, Ag, Sb, Te, Au, Pb e Bi são mobilizados durante a alteração mineral e foram cristalizados em minerais recém-formados, como calcopirita, pirrotita e esfalerita, que estão espacialmente associados à pirita epigenética e ouro. O padrão das piritas do Grupo Jacobina parece não variar ao longo da estratigrafia, o que sugere uma manutenção da fonte de sedimento ao longo da história de sedimentação ou um posterior reequilíbrio químico. Relativo às análises de reflectância espectral, aplicamos o algoritmo de Self-Organizing Maps (SOM) para segmentar dados em vários agrupamentos baseados na matriz de distância das unidades e, em seguida, usamos a projeção UMAP para compactar a estrutura de dados para um gráfico bidimensional, mantendo os principais padrões de dados e comparando com os espectros de minerais conhecidos descritos nos metaconglomerados. Assim, estimamos a composição mineral com base na distância de cada medição dos minerais conhecidos e validamos essa inferência usando dados geoquímicos. Os resultados da inferência mineral corresponderam ao esperado pela análise geoquímica, validando a estimativa da composição mineral das amostras. Baseado nestes resultados, separamos as assinaturas das propriedades físicas e químicas nas zonas mineralizada, proximal e estéril e indicamos critérios que podem ser utilizados para a prospecção de ouro em zonas de paleoplacer modificado, como presença de calcopirita, esfalerita e outros sulfetos na matriz, além de pirita, teores de cromo, potássio e enxofre, susceptibilidade magnética, densidade e a presença de argilominerais.This thesis aims to characterize the signature of gold mineralization of the Serra do Córrego Formation, the basal unit of the Jacobina Group, using multisource data (petrophysics, spectroradiometrics, geochemistry, and mineral chemistry) through data integration and pattern verification using machine learning. Categorical and numerical variables related to the physical properties of rocks were acquired, such as density, magnetic susceptibility, electrical conductivity, the concentration of radioelements, and reflectance, among other chemical properties. Rock chemical analyzes were obtained by in situ portable X-ray fluorescence (pXRF) measurements. Petrographic descriptions and quantitative and semi-quantitative mineral chemistry analyses were also considered in samples for understanding the mineral system. Altogether, 1950 pXRF analyses, 2484 spectroradiometric measurements, 7490 magnetic susceptibility measurements, 5720 electrical conductivity measurements, 598 density measurements, 541 mineral chemistry analyses (mass spectrometer laser ablation, LA-ICPMS), and 304 measurements of radio elements, in addition to 20 petrographic analyzes by optical microscope and 5 analyzes by electron microscope. We use supervised approaches to make predictions and provide information on gold mineralizations in rocks of the Jacobina Group, São Francisco Craton, using sample-scale petrophysical and lithogeochemical parameters. A machine learning model based on the Random Forests algorithm was applied to predict mineralization in drill core samples. Average accuracies were 0.87 for cross-validation training, 0.91 for testing, and 0.86 for all-sample prediction. The result allowed us to estimate the importance of the input variables for the prediction. These estimates were validated by a petrographic interpretation of optical and scanning electron microscopy, which were performed to understand better the relationship between minerals of different stages of gold mineralization. In parallel, we used unsupervised approaches to extract information about sample structuring from LA-ICP-MS and spectral reflectance data. We used Hierarchical Clustering methods to evaluate trace element patterns according to pyrite type (detrital or epigenetic) and stratigraphic levels. Then, we implemented the Uniform Manifold Approximation and Projection (UMAP) technique to reduce the evaluated dimensionality to a two-dimensional projection, seeking to inspect the internal structure of the data. Elements such as Cu, Zn, Ag, Sb, Te, Au, Pb, and Bi are mobilized during mineral alteration and crystallized into newly formed minerals such as chalcopyrite, pyrrhotite, and sphalerite, which are spatially associated with epigenetic pyrite and gold. The multivariate pattern of the pyrites of the Jacobina Group does not seem to vary along with the stratigraphy, which suggests maintenance of the sediment source throughout the sedimentation history or a subsequent chemical rebalancing. Concerning spectral reflectance analyses, we apply the Self-Organizing Maps (SOM) algorithm to segment data into various groupings based on the best unit machine distance matrix. We then use the UMAP algorithm to compress the data structure into a two-dimensional graph, maintaining the main data patterns and comparing them with the spectra of known minerals described in the metaconglomerates. Thus, we estimate the mineral composition based on the distance of each measurement from known minerals and validate this inference using geochemical data. The results of the lithogeochemistry validate the estimate of the mineral composition of the samples. Based on all presented results, we separated the signatures of the physical and chemical properties in the mineralized, proximal and sterile zones. We indicated criteria that can be used for prospecting for gold in modified paleoplacer zones, such as chalcopyrite, sphalerite, and other sulfides in the matrix and pyrite, besides Cr, K, and S contents, magnetic susceptibility, density, and the presence of clay minerals

Applications of aerospace technology to petroleum exploration. Volume 2: Appendices

Participants in the investigation of problem areas in oil exploration are listed and the data acquisition methods used to determine categories to be studied are described. Specific aerospace techniques applicable to the tasks identified are explained and their costs evaluated

Uso de machine learning na mineração: revisão de literatura e aplicação do algoritmo random forest para otimização da recuperação mássica durante o beneficiamento de ferro

Mining is undergoing important changes made possible by the exponential technological development of the last decades. New environmental challenges and regulatory changes encourage the search for tools that enable more efficient and sustainable mining. In this context, the evolution of statistical and computational methods of artificial intelligence (AI) and machine learning (ML) are important tools that allow the massive analysis of high-resolution and large-scale data, allowing process improvement and increased productivity. An underexplored application is the use of AI to reduce losses during the beneficiation process. In iron mining, for example, it is estimated that losses in Brazilian plants can reach 20% and in Brazil 20 to 40% of the weight of the total ore mined is destined for tailings dams. Several factors are related to a lower-than-expected mass recovery, from variables associated with processing at the plant to those associated with the mineralogical composition of the site, however, establishing the contribution of each of these variables remains a challenge. Thus, the objective of this work is to review the literature on the concepts and applications of ML methods in mining and use data from a public bank to train and test a supervised ML algorithm in the discovery of possible factors involved in mass losses and recovery of iron. Iron was chosen due to its importance for Brazilian mining, especially in the state of Minas Gerais, and due to the availability of mineral databases containing this element. The Random Forest (RF) algorithm was selected and the analyzes were performed using codes implemented in Python language. The trained model had an overall accuracy of 74% in predicting variables associated with high or low iron recovery from 13 prediction variables. This approach proved to be a fast, cost-effective, and efficient which can provide several important information in the elaboration of hypotheses related to iron mining. Adjustments to the model can provide the highest expected accuracy of the RF, and the adjustment of high and low mass recovery parameters according to field or industry demands allow the application and adjustments of the model generated here in multiple research and industrial contexts.Trabalho de Conclusão de Curso (Graduação)A mineração passa por mudanças importantes possibilitadas pelo desenvolvimento tecnológico exponencial das últimas décadas. Novos desafios ambientais e mudanças regulatórias fomentam a busca de ferramentas que possibilitem uma mineração mais eficaz e sustentável. Nesse contexto, a evolução dos métodos estatísticos e computacionais de inteligência artificial (IA) e machine learning (ML) são ferramentas importantes que permitem a análise massiva de dados de alta resolução e grande escala, permitindo aprimoramento de processos e aumento da produtividade. Uma aplicação pouco explorada é o uso de IA na redução de perdas durante o processo de beneficiamento. Na mineração de ferro, por exemplo, estima-se que as perdas nas usinas possam chegar a 20% e no Brasil de 20 a 40% do peso do total do minério lavrado é destinado para barragens de rejeito. Vários fatores estão relacionados a uma recuperação mássica inferior ao esperado, desde variáveis associadas ao beneficiamento na usina às associadas a composição mineralógica do sítio; no entanto, estabelecer a contribuição de cada uma dessas variáveis permanece um desafio. Assim, o objetivo deste trabalho é fazer uma revisão de literatura dos conceitos e aplicações dos métodos de ML na mineração e usar dados de um banco público para treino e teste de um algoritmo supervisionado de ML na descoberta de possíveis fatores envolvidos nas perdas e recuperação mássica de Ferro. O Ferro foi escolhido devido a sua importância para a mineração brasileira, especialmente no estado de Minas Gerais, e devido a disponibilidade de bancos de dados minerais contendo este elemento. Foi selecionado o algoritmo Random Forest (RF) e as análises foram realizadas através de códigos implementados em linguagem Python. O modelo treinado obteve acurácia geral de 74% na previsão de variáveis associadas à recuperação alta ou baixa de Ferro a partir de 13 variáveis de predição. Essa abordagem se mostrou uma forma rápida, sem custo e eficiente que pode fornecer várias informações importantes na elaboração de hipóteses relacionadas à mineração do Ferro. Ajustes no modelo podem conferir a maior acurácia esperada do RF e, o ajuste dos parâmetros de alta e baixa recuperação mássica de acordo com demandas de campo ou da indústria permitem a aplicação e ajustes do modelo aqui gerado em múltiplos contextos de pesquisa e industriais

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

C and SM lunar orbital science study, volume 2 Final report

Experiment descriptions and cost estimates for CSM lunar orbital science stud

APPLICATION OF GEOPHYSICAL AND GEOCHEMICAL METHODS FOR SOIL CHARACTERISATION IN SUSTAINABLE PRECISION AGRICULTURE IN SELECTED FARMS

All soils have potential for high yield for specific crops. Nigerian soils have potential for medium to high yield, but poor farming practices including the misuse of chemical fertilizers result in a number of constraints such as soil salinity, degradation and declining fertility, which militate against high crop yields. Nigeria, currently battling with food insecurity because population growth is not commensurate with agricultural production. Thus, there is need for urgent intervention in the agricultural sector. The aim of this study was to integrate geophysical and geochemical methods for sustainable precision agriculture in two farm sites of Covenant University and Landmark University, Nigeria. In this study, electrical resistivity, geochemical and satellite imagery methods were used for soil characterisation in farm sites at Covenant University, Ota, Southwest and Landmark University, Omu-Aran, North-central Nigeria between June, 2018 and January, 2019. The electrical resistivity data were processed using RES2DINV and Win-Resist software. Geochemical analysis of soil samples from the sites was conducted using ICP-MS in ACME laboratory, Canada. Monthly MERRA satellite data was used to determine the soil temperature and soil moisture content while soil salinity was estimated from Landsat-8 satellite imagery. The study showed that electrical resistivity of the topsoil in Covenant University farm ranged from 120 -500 Ωm, while that of Landmark University farm ranged from 345-527 Ωm. The soil types delineated at the Covenant University farm were clayey sand and lateritic clay; sand/lateritic gravelly sand was delineated at Landmark University farm. Potentially toxic elements were detected in the soil samples of both sites; arsenic (As), chromium (Cr), lead (Pb) and copper (Cu) exceeded FAO/WHO recommended standard limits in Covenant University farm. The pollution indices of Co, Cr, Ni, Pb and Mn in the Covenant University farm were within low to high contamination, while As was within medium to high contamination. In Landmark University farm, the pollution indices of Pb, Cu, Zn, Co and Cd ranged from low to medium, while As has pollution index within low to high contamination. Results showed elevated concentrations of As in all samples. Ca-Mg, P-Mg, Fe-Al, Ca-K, Mg-K and Na-K paired nutrients were positively correlated at 5% level of significance in both farmlands, indicating similar increase in both farmlands. Also, the geospatial maps revealed zones of high and low accumulation of essential macro nutrients within the farmlands. Landmark University farmland indicated higher soil salinity than Covenant University farm land. Soil temperature (ST) data at Covenant University farm ranged from 296 - 314 K, while ST at Landmark University farm ranged from 289 - 317 K. Soil moisture content data for both farms ranged from 23 – 113 3 3 mmand 10 - 110 3 3 mmin Covenant and Landmark University farms, respectively. The sandy gravelly soil of Landmark University farm is suitable for the planting of root and tuber crops such as carrot, yam, potatoes, turmeric and beets. Cabbage, leafy vegetables and lemon grass can be grown successfully in Covenant University farm. The ecological risk assessment of toxic metals, showed that arsenic may present a moderate to very high biological risk to both plants and animals that feed on the soil of both farm lands. The site-specific information of the farm sites has been provided. This study provides database that can serve as useful guide in soil management decision making for better yiel

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer