Resource Estimation Research Articles

Addressing Geological Challenges in Mineral Resource Estimation: A Comparative Study of Deep Learning and Traditional Techniques

Spatial prediction of orebody characteristics can often be challenging given the commonly complex geological structure of mineral deposits. For example, a high nugget effect can strongly impact variogram modelling. Geological complexity can be caused by the presence of structural geological discontinuities combined with numerous lithotypes, which may lead to underperformance of grade estimation with traditional kriging. Deep learning algorithms can be a practical alternative in addressing these issues since, in the neural network, calculation of experimental variograms is not necessary and nonlinearity can be captured globally by learning the underlying interrelationships present in the dataset. Five different methods are used to estimate an unsampled 2D dataset. The methods include the machine learning techniques Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network; the conventional geostatistical methods Simple Kriging (SK) and Nearest Neighbourhood (NN); and a deep learning technique, Convolutional Neural Network (CNN). A comparison of geologic features such as discontinuities, faults, and domain boundaries present in the results from the different methods shows that the CNN technique leads in terms of capturing the inherent geological characteristics of given data and possesses high potential to outperform other techniques for various datasets. The CNN model learns from training images and captures important features of each training image based on thousands of calculations and analyses and has good ability to define the borders of domains and to construct its discontinuities.

Spectral Library of European Pegmatites, Pegmatite Minerals and Pegmatite Host-Rocks – the GREENPEG project database

Abstract. The New Exploration Tools for European Pegmatite Green-Tech Resources (GREENPEG) spectral database contains the spectral signature, obtained through reflectance spectroscopy studies, of European pegmatites and minerals as well as their host rocks. Samples include pegmatites of the Nb–Y–F (NYF) and Li–Cs–Ta (LCT) chemical types and host rocks from pegmatite locations in Austria, Ireland, Norway, Portugal, and Spain. The database contains the reflectance spectra (raw and continuum-removed spectra), sample photographs, and main absorption features automatically extracted by a self-proposed Python routine. Whenever possible, spectral mineralogy was interpreted based on the continuum-removed spectra. A detailed description of the database, its content and structure, the measuring instrument, and interoperability with geographic information system (GIS) tools is available in this database report. Moreover, examples of how the data can be used and interpreted are also provided. The advantages and added value of the presented dataset reside in its European scale, with representative samples from pegmatites with distinct genesis, mineralogy, structure, and host rocks that can be used as a reference for pegmatite exploration at a global scale via, for example, satellite image processing. The reported spectral mineral assemblages can also be of interest when considering resource estimation or ore processing. Thus, it is expected that this open dataset, available on the Zenodo platform https://doi.org/10.5281/zenodo.6518318 (Cardoso-Fernandes et al., 2022b), will be a reference for distinct types of users, ranging from academia to industry.

First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning

Fengyun-4A (FY-4A), which is the latest-generation Chinese geostationary meteorological satellite, measures solar reflection and thermal emission with high temporal, spatial, and spectral resolutions. It is expected to be highly beneficial for solar resource assessment and forecasting in China. This study is the first to estimate, using FY-4A and a random forest model, the global horizontal irradiance (GHI) at a 4-km–15-min spatio-temporal resolution over China, as a means to arrive at a solar photovoltaic (PV) resource map. In terms of GHI estimates, the root mean square error and mean bias error between hourly measured and retrieved values are 147.02 (35.2%), −5.64 W/m2 (−1.4%), respectively, whereas the values of daily estimates are 29.20 (18.0%), −2.97 W/m2 (−1.3%). The retrieval accuracy is found much better for instances with solar zenith angles smaller than 60°. Relatively larger errors are found at locations in the Sichuan Basin and northeastern China, which can be attributed to bright surfaces and/or strong cloud transients. With the retrieved irradiance, PV resource is derived through a physical model chain. The annual mean PV resource map suggests that, over most of the west areas, the annual mean effective irradiance exceeds 1700 kWh/m2, with the highest value found in Tibet (around 2000 kWh/m2 per annum). Eastern China has an annual effective irradiance of only 1300–1500 kWh/m2. The region with poorest solar resource is the Sichuan Basin (less than 1100 kWh/m2 per annum).

From simple metrics to cervid forage: Improving predictions of ericaceous shrub biomass

Common understory vegetation species such as the ericaceous shrubs bilberry (Vaccinium myrtillus), cowberry (V. vitis-idaea) and heather (Calluna vulgaris), are key forage plant species for moose and other large herbivores, as well as fulfilling many additional ecosystem functions and services. Here we developed models to predict above-ground biomass of these ericaceous species in coniferous forests, using data on their percentage cover, height, and different stand characteristics. We also built models to understand how the aforementioned variables affect the proportion of the shrubs commonly utilized as forage by large herbivores. We found that the percentage cover of shrubs was the most important explanatory variable when predicting above-ground biomass, explaining 51%, 47% and 71% of the variation (marginal R2) in bilberry, cowberry and heather biomass, respectively. By adding ramet height to the model with percentage cover, the variation explained increased to 77% for bilberry, 75% for cowberry and 87% for heather. The best outcome for candidate models was obtained by adding stand site index and spruce basal area to the model, improving the variation explained in bilberry to 83%, to 81% for cowberry, and 91% for heather. When modelling the proportion of the shrubs commonly utilized as forage by large herbivores, stand site index and spruce basal area often played important roles. Some of the best fitting models for forage biomass explained 51% of the variation in bilberry, 59% in cowberry and 30% in heather. Site location did not have a major role in improving the variability explained in either type of model, which indicated the applicability of the models regardless of study location. Our models therefore have a high potential to be implemented in forestry decision support systems. Their inclusion should provide better large-scale estimations of forage resources, aiding forest management, and thereby taking an important step forward to determine the ecosystem carrying capacity of large herbivores.

Mobilised, Isolated and Hybrid Forms of Scientific Activity

The paper discusses different versions of the definition of mobilised, isolated and hybrid scientific activity. The paper traces the evolution of mobilization projects during the Soviet epoch. The case of Gordon Research Conferences as an instrument of intensification of scientific exchanges in the isolated format is considered. Examples of contemporary successful and unsuccessful mobilization projects are presented. The success of a project depends on a correct estimation of necessary resources, on the degree of concentration of research potential, as well as on the task formulation. Re-engineering, integration and adaptation of existing solutions are preferred as feasible tasks for mobilization projects.

Assessment of the residual life of turbine runners with operational defectiveness

The residual life of the runners of hydraulic turbines in the presence of operational defects is estimated. The main problems of the operation of hydraulic turbines associated with technological defects and exhaustion of the standard resource are described. The main requirements for initial data to be used in estimation of the residual resource and the requirements for predicting the residual resource of runners based on the results of surveys and analysis of their technical condition are specified. We have classified and briefly described the applied approaches and techniques used in estimation of the residual resource. The main damaging factors affecting the residual life of the runners are revealed: deformation aging of the metal, cavitation, corrosion and fatigue damage to the elements of runners. The most characteristic defects are divided into three groups: zones of cavitation erosion; corrosion-fatigue cracks; and weld defects. Particular attention is paid to corrosion-fatigue cracks identified using flaw detection. The mechanism of crack formation and the most probable location of the cracks in the runner are shown. Statistical data on the number of cracks at the onset of the runner operation and at the time of shutdown maintenance are presented. The main statistical parameters of the sample and the parameters of crack size distributions including the distribution law are determined. The distribution law is exponential for the crack length parameter; whereas for the crack opening width it is log-normal. The revealed multidirectional cracks are located at the surface, subsurface or inner layer of the metal. They arise from operational defects (ulcers, craters, undercuts or delamination) and grow during operation of the turbine units. We also present the design schemes of elements with cracks used for quantification of resources according to the criteria of fracture mechanics. The results of calculations for static and dynamic crack resistance are presented as the dependence of stress intensity factors on the crack size. The levels of the total accumulated damage to the runners, the values of the residual life at the stage of crack nucleation and development were determined for 11 hydraulic units in the «start-stop» and «working» cycles. The main conclusion is that the total operating time of the hydraulic turbine runners significantly exceeds the standard operating life, while the residual resource is insufficient for a further period of long-term operation.

Towards quantifying uncertainties in geological models for mineral resource estimation through outside-in deposit-scale structural geological analysis

Mineral Resource downgrades can be prevented by integrating better structural geological interpretations into project evaluations. Geological modelling for Mineral Resource estimation is presented as a systematic process of structural geological analysis at the deposit-scale using drill-sampled grade data. The initial step in this process is to use Maximum Intensity Projection (MIP) rendering to interpret axial symmetry and its orientation from the grade interval mid-points at the deposit-scale, thus reducing the modelling process’s degrees of freedom to only three—estimating the prolateness of the grade or lithological distribution. This approach contrasts with the brute-force methods for assessing modelling uncertainty because the axial direction is fixed to the down-plunge orientation, thus eliminating from the outset, structurally implausible geometric configurations and unrealistic geological scenarios common in brute-force methods. Adopting this new approach of creating geological models means moving away from conventional mining industry methods that prioritise operational considerations in constructing resource estimates but fail to explain the geometric complexities of mineral deposits. We use experiments and a practical case study of a historical gold mine in Western Australia to highlight the significance of analysing the structural control of a gold deposit at the deposit-scale using raw grade data. Our research is based on anecdotal evidence suggesting that erroneous geological modelling leads to unforeseen Mineral Resource downgrades. However, we cannot confirm this hypothesis because there have been few, if any, independent public inquiries into Mineral Resource downgrades. To establish the underlying reasons for significant resource downgrades with certainty, impartial investigations (modelled on accident investigations in the aviation industry) are imperative.

Projected future European power sector water usage across power scenarios and corresponding trends in water availability

The current transition toward added renewables into the power mix is essential to mitigate climate change effects, but the energy transition has environmental impacts outside the scope of greenhouse gas emissions that also need attention. One such impact is the water-energy dependency nexus, where water dependencies are also seen for non-fossil technologies such as concentrated solar power (CSP), bioenergy and hydropower and mitigation technologies such as carbon capture and storage (CCS). In this light, the selection of power production technologies can potentially affect long-term water resource renewability and dry summer conditions, causing, e.g., power plant shutdowns. In this study, we employ an established and validated scheme of water consumption and withdrawal rates across energy conversion technologies at the European scale to project corresponding water usage rates towards 2050 for EU30 countries. We further use the entire range of global- and regional climate model ensembles for low-, medium- and high-emission scenarios to project trends and robustness estimates of freshwater resources and availability at the distributed level for corresponding countries and years towards 2100. The results show a high sensitivity of water usage rates to the implementation of energy technologies such as CSP and CCS, as well as the decommissioning rates of fossil technologies and some scenarios generally show unaltered or even vastly increasing water consumption and withdrawal rates. Further, the assumptions on using CCS technologies, an evolving field, show a high impact. The assessment of hydro-climatic projections showed some degree of overlaps between decreasing water availabilities and increasing power sector water usage, especially for one power production scenario with a high share of CCS implementation. Further, a vast climate model spread in water availability was seen for both yearly means and summer minima, emphasising the need to include extremes in water management, and the water availability was highly dependent on the emission scenario in some regions.

Numerical Simulations of Carbon Dioxide Storage Efficiency in Heterogeneous Reservoir Models

The U.S. Department of Energy’s National Energy Technology Laboratory (DOE-NETL) has been developing methods and tools (the online Carbon Dioxide Storage prospeCtive Resource Estimation Excel aNalysis (CO2-SCREEN) tool) to estimate carbon dioxide (CO2) storage potential in subsurface reservoirs. The CO2 storage efficiency terms are input in the tool to calculate storage potential in targeted reservoirs. In this effort, two CO2 storage efficiency terms were evaluated: volumetric displacement ( E V ) and microscopic displacement ( E d ). The first term deals with efficiency of CO2 propagation into an accessible reservoir volume, while the second term evaluates effectiveness of native fluid displacement with CO2. The interpreted well logs and core sample measurements were applied to create the heterogeneous reservoir models including geostatistical realizations of porosity and intrinsic permeability fields. Supercritical CO2 was injected over the course of 30 years into brine-saturated reservoir models for clastics, limestone, and dolomite lithologies and deltaic fluvial, aeolian, shallow marine, and reef depositional environments by means of varying reservoir parameters and injection scenarios. The reservoir models providing vertically heterogeneous petrophysical properties and designated as “layered reservoir models” (with homogeneous parameters along each layer of the model) were not determined to be a transition between the homogeneous and heterogeneous models in respect to storage efficiency. Another finding shows that high-efficiency factors do not necessarily mean increased CO2 storage; they rather indicate that the available volume and pore space are more fully utilized. The CO2 storage efficiency factors were evaluated dynamically at the select time points using P 10 ‐ P 50 ‐ P 90 percentiles. The results of this study show that the P 10 ‐ P 90 distribution for volumetric efficiency is wider when compared to the microscopic efficiency. It was found that where dominant buoyancy forces drive the plume to the top of a target formation, the volumetric efficiency is low. Tighter sandstone and carbonate formations show prevalence of capillary forces and better utilization of reservoir volume.

Availability of palliative specialty care for adults in community oncology: Results from the 2022 NCORP landscape assessment.

e24125 Background: Palliative care promotes timely symptom management, and care planning and improves quality of life in oncology patients. About 60% of community oncology practices sampled in 2015 had outpatient palliative care. This study updates estimates of palliative care resources in community oncology. Methods: Questions from the 2022 Landscape Assessment of National Cancer Institute Community Oncology Research Program (NCORP) (funding provided by 2UG1CA189824) assessed the availability of specialist palliative care providers and ancillary care services, including utilization of advanced practice providers (APPs). Descriptive statistics describe availability of providers and services. Multivariable logistic regression was used to identify practice characteristics associated with palliative care specialist availability in 2022. Differences in rates of palliative care specialists, outpatient palliative care, APPs and patient portal use between 2015 and 2022 assessment results were assessed using Chi-Square tests, assuming independent time snapshots of general population characteristics. Results: Most community oncology practices sampled in 2015 had palliative care specialists (76%) and 62% of practices reported having outpatient palliative care. Of the 259 non-pediatric exclusive NCORP practice groups included in the 2022 analyses (Table 1), significantly more had palliative care specialists (86%; p = 0.003) and offered palliative care in the outpatient setting (76%; p = 0.001). Most practices utilized APPs (92%) and had a patient portal (95%), while only 41% had a survivorship clinic. Multivariable analyses demonstrated practices with more adult oncology providers (OR = 1.03, p = 0.039) and those with a designated survivorship clinic (OR = 3.16, p = 0.001), were significantly more likely to have specialty palliative care in the outpatient setting. Conclusions: More practices reported availability of palliative care specialists and outpatient services in community oncology practices in 2022 compared to 2015, with more oncology providers being associated with increased odds of having outpatient specialty palliative care in 2022. Smaller community oncology practices with fewer providers may need support to expand their capacity to provide palliative care. [Table: see text]

A fuzzy logic based explicit declustering technique

During the spatial estimation of geoscience resource variables, the quantity or quality of minerals and hydrocarbons can be represented by a broad range of properties, including geochemical, geotechnical, or other physical measures. Preferential sampling within the region of interest causes biased global parameters due to clustered sampling patterns. Unbiased sample distribution is essential for conducting conditional simulations to model uncertainty of spatially distributed attributes, e.g. geochemical content of metal or porosity. Therefore, declustering procedures are applied during resource estimation to estimate an unbiased statistical distribution of the measured variables. Traditional techniques such as cell declustering do not consider grade clustering, i.e., the similarity of measured variables within a spatially clustered neighbourhood. This paper presents a declustering technique that explicitly accounts for spatial clustering and the similarity of measured samples' attributes within these spatially clustered samples. In the proposed method, samples were first classified explicitly into spatial and geochemical (grade) clusters using the Fuzzy c-means algorithm. Declustering weights were derived using the Mamdani based Fuzzy Inference System using various T-norm operations. The technique was applied to the publicly available GSLib and Walker Lake datasets. It was shown that the proposed scheme produced more accurate results than those obtained with the traditional declustering technique.

First-quantized eigensolver for ground and excited states of electrons under a uniform magnetic field

First-quantized eigensolver (FQE) is a recently proposed quantum computation framework for obtaining the ground state of an interacting electronic system based on probabilistic imaginary-time evolution. Here, we propose a method for introducing a uniform magnetic field to the FQE calculation. Our resource estimation demonstrates that the additional circuit responsible for the magnetic field can be implemented with a linear depth in terms of the number of qubits assigned to each electron. Hence, introduction of the magnetic field has no impact on the leading order of the entire computational cost. The proposed method is validated by numerical simulations of the ground and excited states employing filtration circuits for the energy eigenstates. We also provide a generic construction of the derivative circuits together with measurement-based formulae. As a special case of them, we can obtain the electric-current density in an electronic system to gain insights into the microscopic origin of the magnetic response.

Petrophysical properties and volume estimation of hydrocarbon resources in x field, onshore niger delta: A reservoir characterization study

A reservoir characterization study was conducted on three wells located in X Field, situated in the Onshore region of the Niger Delta. A suite of conventional digital well logs was utilized to identify hydrocarbon-bearing reservoirs, determine reservoir petrophysical parameters, and infer the depositional environment. The study delineated four hydrocarbon-bearing reservoirs, labeled A, B, C, and D, with porosity estimates ranging from 25% to 27%, and permeability values varying from 1863.22md to 2759.78md. These results suggest that the reservoirs have good storage capacity and permit free flow of fluids, consistent with prior research in the Niger Delta. The water saturation values, ranging from 43% to 70% for Well X and 53% to 94% for Well Y, indicate the presence of significant hydrocarbon in reservoir C, while Well Z did not contain any hydrocarbon. The estimation of oil and gas resources indicated that Well X contains 1.11 X 105 barrels/acre of oil and 5.16 X 107 cubic feet/acre of gas, while Well Y contains 4.43 X 106 cubic feet of gas. The analysis of the volume of shale (0.15-0.19) revealed that the reservoirs range from slightly shaly sand to shaly sand. Based on the log motifs, the study suggests that the reservoirs are mainly fluvial channel deposits, and the rapid alternation of thin beds of sand and shale indicates deposits of delta progradation and river floodplain deposits.

Multivariate Statistical Analysis of Coal in Dahor Formation, Borneo Island, Indonesia: A Comparative Study Utilizing Principal Component Analysis (PCA)

This study focuses on the multivariate statistical analysis of coal samples from the Dahor Formation in Borneo Island, Indonesia, utilizing Principal Component Analysis (PCA). The objective is to discern the differences in coal samples based on their geological history and chemical properties. The Indonesian coal industry heavily relies on domestic consumption, with plans to increase coal usage for domestic purposes. Coal classification systems play a vital role in evaluating coal quality and determining its economic value. The geological background of the study area in Borneo Island is discussed, emphasizing relevant formations and sedimentation processes. The application of PCA in multivariate statistical analysis is explained, along with the generation of a correlation matrix to explore relationships between variables. The results reveal the presence of four distinct quadrants in the processed coal data, indicating the influence of volatile matter, total moisture, ash content, and fixed carbon. Further analysis demonstrates relatively insignificant differences in the proximate analysis parameters, except for ash content, which indicates the presence of impurity minerals. The concentration of volatile matter, ash, and moisture impacts the fixed carbon content, while volatile matter influences the combustion process. These findings provide valuable insights into coal quality and utilization. The utilization of PCA and multivariate analysis enhances understanding of coal characteristics and facilitates decision-making in coal-related industries. The study concludes with a call for further investigations and analyses to enhance our understanding of coal deposits, improve resource estimation, and develop more sustainable and efficient coal-based processes. Continued research in this field will contribute to advancements in coal science and the development of strategies for responsible coal utilization.

VALUATION OF NATURAL RESOURCES IN THE COASTAL ZONE FOR RECREATION FIELDS

In a coastal zone of a seas recreation resources are various. In general, they distinguished by 3 blocks, according to features of atmosphere, hydrosphere and lithosphere as a parts of geographical sphere. At the same time, the exponents and qualities were separated, and the scales were elaborated for every of them. The resources estimations were made according to medicine-biological qualities of the coastal zone for next exponents: beach, cliff, submarine slope, water, sediment etc.

Geothermal Resources of European Continent: A Regional Assessment

A regional assessment of geothermal resources of the continental region of Europe is attempted in the present work. It is based on results of observational data on heat transfer by conduction as well as hydrothermal and magmatic processes in the upper crust and makes use of geothermal data reported at the website of IHFC and also estimates based on available information on occurrences thermal springs and volcanic events. The resource assessment has been carried out for 8109 sites distributed in 39 countries. These datasets were reevaluated and spatially gridded using kigrid interpolation to construction of regional distribution maps of geothermal resources and interpreted on the basis of available information on tectonic setting and geological characteristics. The estimates for volcanic areas are based on a modified method of magmatic heat budget (MHB). has been employed in deriving estimates of geothermal resources in areas of recent volcanic activity. According to results obtained the total resource base (RB) at 6km on depth is estimated to be 8.653.306 ± 7 GJ. The mean resource base per unit area (RBUA) is ~ 985 GJ. The prominent features in geothermal maps are the presence of localities with significantly high values of resource base greater than 2000 GJ in countries such as Russia, Italy, Iceland, and west Norway. In addition, vertical distributions of temperatures were calculated in for depths reaching down to 6 km. The results obtained indicate potential availability of high temperature resources in vast regions of the European continent.

Floating Photovoltaic Systems Coupled with Pumped Hydroplants under Day-Ahead Electricity Market Conditions: Parametric Analysis

The intermittent nature of the solar resource together with the fluctuating energy demand of the day-ahead electricity market requires the use of efficient long-term energy storage systems. The pumped hydroelectric storage (PHS) power plant has demonstrated its technical and commercial viability as a large-scale energy storage technology. The objective of this paper is to analyse the parameters that influence the mode of operation in conjunction with a floating photovoltaic (FPV) power plant under day-ahead electricity market conditions. This work proposes the analysis of two parameters: the size of the FPV power plant and the total process efficiency of the PHS power plant. Five FPV plant sizes are analysed: 50% (S1), 100% (S2), 150% (S3), 350% (S4) and 450% (S5) of the PHS plant. The values of the total process efficiency parameter analysed are as follows: 0.77 for old PHS plants, and 0.85 for more modern plants. The number of daily operating hours of the PHS plant is 4 h. These 4 h of operation correspond to the highest prices on the electricity market. The framework of the study is the Iberian electricity market and the Alto Rabagão dam (Portugal). Different operating scenarios are considered to identify the optimal size of the FPV power plant. Based on the measured data on climatic conditions, an algorithm is designed to estimate the energy production for different sizes of FPV plants. If the total process efficiency is 0.85, the joint operation of both plants with FPV plant sizes S2 and S3 yields a slightly higher economic benefit than the independent mode of operation. If the total process efficiency is 0.77, there is always a higher economic benefit in the independent operation mode, irrespective of the size of the FPV plant. However, the uncertainty of the solar resource estimation can lead to a higher economic benefit in the joint operation mode. Increasing the number of operating hours of the PHS plant above 4 h per day decreases the economic benefit of the joint operation mode, regardless of the total process efficiency parameter and the size of the FPV plant. As the number of operating hours increases, the economic benefit decreases. The results obtained reveal that the coupling of floating photovoltaic systems with pumped hydroelectric storage power plants is a cost-effective and reliable alternative to provide sustainable energy supply security under electricity market conditions. In summary, the purpose of this work is to facilitate decision making on the mode of operation of both power plants under electricity market conditions. The case studies allow to find the optimal answer to the following practical questions: What size does the FPV power plant have to be in order for both plants to be better adapted to the electricity market? What is the appropriate mode of operation of both plants? What is the economic benefit of changing the turbine pump of the PHS power plant? Finally, how does the installation of the FPV power plant affect the water volume of the upper reservoir of the PHS plant? Knowledge of these questions will facilitate the design of FPV power plants and the joint operation of both plants.

A machine learning based Distributed Congestion Control Protocol for multi-hop wireless networks

The application areas of multi-hop wireless networks are expected to experience sustained growth in the next years. This growth will be further supported by the current possibility of providing low-cost communication capabilities to any device. One of the main issues to consider with this type of networks is congestion control, that is, avoiding an excessive volume of data traffic that could lead to a loss of performance. In this work, a distributed congestion control mechanism is proposed for generic multi-hop networks. Different categories of data traffic are taken into account, each of them with different quality of service requirements. The mechanism is based on machine learning techniques, specifically, the CatBoost algorithm that uses gradient boosting on decision trees. The obtained decision trees are used to predict whether the packets to be transmitted over the network will reach their destination on time or not. This prediction will be made based on the network load state, which will be quantified by means of two parameters: the utilization factor of the different transmission channels, and the occupancy of the buffers of the network nodes. To make the values of these parameters available to all nodes in the network, an appropriate dissemination protocol has also been designed. Besides, a method to assign different transmission priorities to each traffic category, based on the estimation of the network resources required at any time, has also been included. The complete system has been implemented and evaluated through simulations, which show the correct functionality and the improvements obtained in terms of packet delivery ratio, network transit time, and traffic differentiation.

Application of Artificial Neural Network for the Prediction of Copper Ore Grade

Precise prediction of ore grade is essential in feasibility studies, mine planning, open-pit and underground optimization, and ore grade control. Conventional methods, such as geometric and geostatistical methods, are the most popular techniques for mineral resource estimation but fail to capture the complexity of orebodies. Due to this limitation, grades are incorrectly estimated, leading to inaccurate mine plans and costly financial decisions. Here, we propose an ore grade prediction method using an artificial neural network (ANN). We collected 14,294 datasets from the Jaguar mine in Western Australia. The proposed model was developed by incorporating lithology, alteration, eastings, northwards, altitude, dip, and azimuth to predict the grade, and the performance evaluation metrics were measured based on the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), correlation coefficient, R, and coefficient of determination (R2). The proposed ANN model outperformed classic machine learning methods with R2, R, MAE, MSE, and RMSE of 0.584, 0.765, 0.0018, 0.0016, and 0.041, respectively. The Shapley technique was used to evaluate the feature importance of the input variables for the grade prediction. Lithology demonstrated the highest influence on ore prediction, whereas eastings had the least impact on output. The proposed approach is promising for ore model prediction.

Modeling and Estimation of Nickel Laterite Resources Using Geocomputing Methods at North Konawa, Southeast Sulawesi

Administratively, the research area is located in the Waturambaha area, North Konawe, Southeast Sulawesi province. The method used in determining the distribution and estimation of Ni laterite measurable resources is one of the geocomputational methods, Inverse Distance Wieght (IDW). From the interpolation of 82 drill points, it was concluded that the distribution of Ni levels is quite high (COG ≥ 1.2%) discriminated dominantly in the central to northern part and some points in the northwest of the study area, while low-grade Ni (<1.2%) is distributed in the northeastern to southeastern part of the study area. Based on distributed ore modeling in the central to northern parts of the study area on limonite layers with a thickness of 2 – 4 m while in saprolite layers the thickness ranges from 6 – 12 m. Waste in the limonite layer with a thickness of 5-10 m while in saprolite 4-7 m. Modeling the distribution of Ni by the IDW method in the study area obtained a measured resource volume of Ni of 1,196,450 m³ which was then multiplied by the density of each layer (limonite and saprolite) and obtained a tonnage of ni measured resources of 1,854,500 M/T with 1.39% ni average.