Resource Estimation Research Articles

Some basic improvements in mineral resource estimation and reporting

ABSTRACT Bulk density miscalculations and subsequent reporting can produce resource estimation errors and unexpected financial outcomes. Density and bulk density vary throughout a mineral deposit, especially in porous deposits and deposits containing variable amounts of high-density minerals (e.g., barite). Resource estimators must correlate bulk density with core recovery and apply quality assurance/quality control analysis to bulk density data. Core recovery is an important factor in resource estimation and reporting. Block tonnages (ore and waste) are estimated from volumes using a dry bulk density value. Ore dilution is a volumetric effect integral to ore reserve estimation that also has the potential to affect the economics of mining a deposit. Both the bulk density and dilution problems could be overcome by reporting a mineral resource estimate in grade per unit volume.

Глубинное строение и металлогенический потенциал южного фланга Кувалорогского интрузивного массива (Камчатка)

The dееp structure of the southern flank of the Kuvalorog intrusion located in the Kvinum-Kuvalorog nickeliferous zone of the Sredinny Ridge (Kamchatka) was investigated. A volumetric model of rock density distribution up to a depth of 5 km below sea level was constructed. Massive blocks with density characteristic of basitic rocks of the Dukuk nickel-bearing plutonic complex were identified. The blocks are located on two levels and are chambers of a single magmatic system. The upper level corresponds to the exposed part of the intrusion. The lower level consists of separate blocks located at a depth of 0 to 2 km. The blocks of both levels are connected by complexly shaped lintels. The volume of hidden magmatic chambers of the lower level was determined. The average Ni, Cu, Co contents in the rocks of the intrusion and the volume of the chambers determine their metallogenic potential. The average Ni, Cu, Co contents in the rocks of the nickel–bearing complex exceed the crustal clarks by 1.5–3 times. The massive block in the area of the watershed of the rivers Кихчик and Pravaya Kikhchinskaya has the largest resources (million tons): Ni — 1.71, Cu — 0.80, Co — 0.51. The obtained estimates of the metallogenic resource of the lower level chamber are the minimum possible ones.

Comparisons of Montane Snow Water Equivalent Projections: Calculating Total Snow Mass in Regions with Projection Agreement and Divergence in the Western US

Abstract Montane snowpack is a vital source of water in the Western United States. Here, we use a large-ensemble approach to evaluate the agreement across 124 snow water equivalent (SWE) projections with statistically downscaled forcing between end-of-century (2076 – 2095) and early 21st century (2106 – 2035) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and mid-spring snowmelt (15 May) in five western US domains. Using 1) the percent change to end-of-century SWE across different ensembles of snow projections, and 2) the shift between early 21st century and end-of-century SWE distributions for each snow projection, we identified relationships between projections that were consistent across each domain. In low to mid-elevations, end-of-century SWE decreases were 48% and larger on 15 April. These regions had projected changes to SWE that were both high-confidence and in relative agreement across projections. Despite this, the majority of 15 April SWE volume existed in higher elevations where the magnitude and direction (positive or negative) of SWE changes were most uncertain. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify 1) the highest-confidence changes to future snow water resources, and 2) the locations and periods where and when improvements to snow projections would most benefit estimates of future snow water resources.

The cost of solving linear differential equations on a quantum computer: fast-forwarding to explicit resource counts

How well can quantum computers simulate classical dynamical systems? There is increasing effort in developing quantum algorithms to efficiently simulate dynamics beyond Hamiltonian simulation, but so far exact resource estimates are not known. In this work, we provide two significant contributions. First, we give the first non-asymptotic computation of the cost of encoding the solution to general linear ordinary differential equations into quantum states – either the solution at a final time, or an encoding of the whole history within a time interval. Second, we show that the stability properties of a large class of classical dynamics allow their fast-forwarding, making their quantum simulation much more time-efficient. From this point of view, quantum Hamiltonian dynamics is a boundary case that does not allow this form of stability-induced fast-forwarding. In particular, we find that the history state can always be output with complexity O(T1/2) for any stable linear system. We present a range of asymptotic improvements over state-of-the-art in various regimes. We illustrate our results with a family of dynamics including linearized collisional plasma problems, coupled, damped, forced harmonic oscillators and dissipative nonlinear problems. In this case the scaling is quadratically improved, and leads to significant reductions in the query counts after inclusion of all relevant constant prefactors.

Byproduct-to-Host Ratios for Assessing the Accessibility of Mineral Resources.

Mineral resources are essential for reaching net-zero ambitions by 2050. There is a rising diversity of metals in electricity generation and storage technologies, as well as for mobility technologies. However, little is known about the future supply of minor elements historically mined in low volumes such as indium, tellurium, germanium, or tantalum. Those minor elements are found in lower concentrations in the ores of major elements and therefore rarely form economic deposits on their own. Such elements are often produced as byproducts of a host (or "target commodity", which underpins the bulk of a mine's profitability) in ore, e.g., in porphyry ore, tellurium is a byproduct where copper is the host. As a result, the primary supply of those minor elements depends on the supply of the major elements. Such dependency has not been accounted for in scenarios of the mineral supply. To address this gap, we developed a methodology to harmonize scattered data of mineral resource estimates and to calculate the mass ratio between the byproduct and the host in ores and concentrates, called the byproduct-to-host (BtH) ratio. We collected crude ore tonnage and element grades, among other key data, from the state-of-the-art literature and publicly available mining company reports. Our data set covers 3422 deposits across 141 countries providing 22 275 BtH ratios. The future supply of minor elements can be derived by multiplying the primary production of host elements by the developed BtH ratios, noting the limitations of data representativity. The open-access nature of this work facilitates the enrichment and update of this data set in the coming years.

Geological Condition Characterization and Coal Resource Estimation at the “KJG” Pit of PT Bukit Asam Tbk, Muara Enim Regency, South Sumatra Province

Abstract Coal is a conventional energy source found in tropical countries (e.g., Indonesia) with varying distribution and thickness in the South Sumatra Basin. However, the thickness of the coal seams was not evenly distributed. This affected the estimation of coal resources in the mining area of PT Bukit Asam Tbk in Tanjung Enim, Muara Enim Regency. We used field observation methods and borehole data analysis to determine the geological conditions and estimate the coal resources. The geological condition seems to indicate a claystone unit with tuffaceous sandstone interbeds and a sandstone unit with claystone interbeds. These two units might host coal seams (i.e., A1, A2, B1, B2, and C), where the coal seam thickness may determine increased towards the south and west in the study area. The coal resource estimation based on the SNI 5015: 2019 classification, under moderate geological conditions, can result in a total of 26.034.991 tonnes of measured resources, 25.209.688 tonnes of indicated resources, and 11.189.554 tonnes of inferred resources. Therefore, to ensure efficient coal mining in accordance with the measured reserves, the stripmining method may be used by the company.

Recovery resources for college students: Leveraging web scraping to unveil current estimates.

Growing recognition of the importance of addressing substance use among emerging adults has led to a rapid expansion of recovery services on college campuses. However, existing estimates on collegiate recovery programs or communities (CRPs/Cs) and other services are outdated or lack rigor, leaving the extent of these resources unclear. This study aimed to fill this gap in our understanding by providing current estimates of recovery-related resources. Utilizing the Python web scraping library BeautifulSoup, we gathered a large sample of ".edu"-hosted webpages (N = 995) with references to recovery services (e.g., "collegiate recovery", "peer support"). Eligible webpages (n = 552) were screened by a team of 11 reviewers to extract information on these services. During extraction, we identified 270 institutions that advertised on-campus recovery services for students. Of these institutions, 176 advertised formal CRPs/Cs. A majority of CRPs/Cs (n = 164) advertised mutual aid meetings and sober/drug-free social activities (n = 138), while only 83 advertised drop-in centers. Relatively few historically Black colleges or universities (n = 5), Hispanic-serving institutions (n = 21), or native-serving institutions (n = 0) hosted recovery services. On-campus services to support recovery have greatly expanded since previous estimates, but gaps may exist in the services provided by these programs. By providing an updated estimate and examining service uniformity, this study can aid in future expansion and standardization efforts to support students in recovery. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Processing of GASKAP-Hi pilot survey data using a commercial supercomputer

Modern radio telescopes generate large amounts of data, with the next generation Very Large Array (ngVLA) and the Square Kilometre Array (SKA) expected to feed up to 292 GB of visibilities per second to the science data processor (SDP). However, the continued exponential growth in the power of the world’s largest supercomputers suggests that for the foreseeable future there will be sufficient capacity available to provide for astronomers’ needs in processing ‘science ready’ products from the new generation of telescopes, with commercial platforms becoming an option for overflow capacity. The purpose of the current work is to trial the use of commercial high performance computing (HPC) for a large scale processing task in astronomy, in this case processing data from the GASKAP-Hi pilot surveys. We delineate a four-step process which can be followed by other researchers wishing to port an existing workflow from a public facility to a commercial provider. We used the process to provide reference images for an ongoing upgrade to ASKAPSoft (the ASKAP SDP software), and to provide science images for the GASKAP collaboration, using the joint deconvolution capability of WSClean. We document the approach to optimising the pipeline to minimise cost and elapsed time at the commercial provider, and give a resource estimate for processing future full survey data. Finally we document advantages, disadvantages, and lessons learned from the project, which will aid other researchers aiming to use commercial supercomputing for radio astronomy imaging. We found the key advantage to be immediate access and high availability, and the main disadvantage to be the need for improved HPC knowledge to take best advantage of the facility.

Nurturing a New Industry Rooted in Geoscience: Stakeholder Insights on Minewater Thermal in Scotland

Heat decarbonisation is crucial for climate action and the transition to a sustainable society. Abandoned, flooded mines can be used to provide low-carbon heating and cooling for buildings or as thermal energy storage for district heating networks (“minewater thermal resources”). Due to the capital-intensive nature of the infrastructure required for minewater thermal, its use should be considered early in project development. Developers therefore need to be aware of the full range of low-carbon heating solutions to implement the most sustainable solutions. Through interviews with twelve key stakeholders in Scotland, this study aims to determine the level of awareness of this technology among stakeholders who require heat for their developments, and stakeholders who would be involved in the development or construction of such schemes. Our findings have implications for how the geoscience community could aid the development of a minewater thermal industry. Stakeholders perceived a range of advantages of minewater, including use as thermal storage and the co-location of minewater resources with heat demand. Perceived disadvantages included the high capital cost and pre-construction risks associated with determining the feasibility minewater resources. Building trust and confidence in minewater thermal technology was identified as a key factor for success. Issues relevant for low-carbon heat in general were also raised including, high retail cost of electricity, skills gaps and labour shortages. Geoscientists can identify prospective minewater resources and assess the risks associated with exploration, development and operation of that resource, contributing to building confidence and reducing up-front capital costs. Given the multidisciplinary nature of the heat decarbonisation challenge, geoscientists must be able to communicate clearly and transparently about the science underpinning resource estimates and risk mitigation measures. For minewater thermal projects to succeed, geoscientists must be equipped with skills, knowledge and understanding to embrace these wider roles in nurturing this nascent industry.

Understanding the impact of data gaps on long-term offshore wind resource estimates

Abstract. In the context of a wind farm project, the wind resource is assessed to predict the power output and the optimal positioning of wind turbines. This requires taking wind measurements on the site of interest and extrapolating these to the long term using so-called “measure, correlate, and predict” (MCP) methods. Sensor, power supply, and software failures are common phenomena. These disruptions cause gaps in the measured data, which can especially be long in offshore measurement campaigns due to harsh weather conditions causing system failures and preventing servicing and redeployment. The present study investigates the effect of measurement data gaps on long-term offshore wind estimates by analyzing the bias they introduce in the parameters commonly used for wind resource assessment. Furthermore, it aims to show how filling the gaps can mitigate their effect. To achieve this, we perform investigations for three offshore sites in Europe with 2 years of concurrent measurements. We use reanalysis data and various MCP methods to fill gaps in the measured data and extrapolate these data to the long term. Current standards demand high data availability (80 % or 90 %) for wind measurement campaigns, so we expect that the effect of missing data on the uncertainty in long-term extrapolations is of the same order of magnitude as other uncertainty components such as the measurement uncertainty or the inter-annual variability. Nevertheless, our results show that the effects of gaps are considerably smaller than the other uncertainty components. For instance, gaps of 180 d cause an average deviation of the long-term mean wind speed of less than 0.04 m s−1 and a 95th percentile deviation of less than 0.075 m s−1 for all tested sites. Due to the low impact of gaps, gap filling does not have the potential to significantly reduce the uncertainty in the long-term extrapolation.

Automated estimation of offshore polymetallic nodule abundance based on seafloor imagery using deep learning

The burgeoning demand for critical metals used in high-tech and green technology industries has turned attention toward the vast resources of polymetallic nodules on the ocean floor. Traditional methods for estimating the abundance of these nodules, such as direct sampling or acoustic imagery are time and labour-intensive or often insufficient for large-scale or accurate assessment. This paper advocates for the automatization of polymetallic nodules detection and abundance estimation using deep learning algorithms applied to seabed photographs.We propose UNET convolutional neural network framework specifically trained to process the unique features of seabed imagery, which can reliably detect and estimate the abundance of polymetallic nodules based on thousands of seabed photographs in significantly reduced time (below 10 h for 30 thousand photographs). Our approach addresses the challenges of data preparation, variable image quality, coverage-abundance transition model and sediments presence.We indicated the utilization of this approach can substantially increase the efficiency and accuracy of resource estimation, dramatically reducing the time and cost currently required for manual assessment. Furthermore, we discuss the potential of this method to be integrated into large-scale systems for sustainable exploitation of these undersea resources.

Adaptive integration of history variables in constrained mixture models for organ-scale growth and remodeling.

In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue patch for a parameter study and show the performance under different G&R scenarios. To confirm that adaptive strategies enable large organ-scale examples, we show simulations of different hypertension conditions with a real-world example of a biventricular heart discretized with a finite element mesh. In our example, adaptive integrations sped up simulations by a factor of three and reduced memory requirements to one-sixth. The reduction of the computational costs gets even more pronounced for simulations over longer periods. Adaptive integration of the history variables allows studying more finely resolved models and longer G&R periods while computational costs are drastically reduced and largely constant in time.

Machine learning perioperative applications in visceral surgery: a narrative review.

Artificial intelligence in surgery has seen an expansive rise in research and clinical implementation in recent years, with many of the models being driven by machine learning. In the preoperative setting, machine learning models have been utilized to guide indications for surgery, appropriate timing of operations, calculation of risks and prognostication, along with improving estimations of time and resources required for surgeries. Intraoperative applications that have been demonstrated are visual annotations of the surgical field, automated classification of surgical phases and prediction of intraoperative patient decompensation. Postoperative applications have been studied the most, with most efforts put towards prediction of postoperative complications, recurrence patterns of malignancy, enhanced surgical education and assessment of surgical skill. Challenges to implementation of these models in clinical practice include the need for more quantity and quality of standardized data to improve model performance, sufficient resources and infrastructure to train and use machine learning, along with addressing ethical and patient acceptance considerations.

Enhancing Mineral Exploration Programs Through Quantitative XRD: A Case Study from the Gumsberg Polymetallic Sulphide Deposits, Sweden

As challenges in precious and base metal exploration intensify due to the diminishing availability of high-grade ore deposits, rising demand, energy costs, and stricter regulations towards net-zero carbon activities, advanced techniques to enhance exploration efficiency are becoming increasingly critical. This study demonstrates the effectiveness of quantitative X-ray diffraction (QXRD) with Rietveld refinement, coupled with multivariate statistical analysis (including agglomerative hierarchical clustering, principal component analysis, and fuzzy analysis), in characterizing the complex mineralogy of strata-bound volcanic-associated limestone-skarn Zn-Pb-Ag-(Cu-Au)-type sulphide deposits (SVALS). Focusing on 113 coarse rejects from the Gumsberg project located in the Bergslagen mining district in central Sweden, the research identified five distinct mineralogical clusters corresponding to polymetallic base metal sulphide mineralization, its proximal alteration zones, and variably metamorphosed host rocks. The results reveal significant sulphide mineralization, ranging from disseminated to massive occurrences of sphalerite, pyrrhotite, pyrite, and galena, with trace amounts of secondary minerals like anglesite in certain samples indicating weathering processes. The study also identifies rare minerals such as armenite, often overlooked in traditional geological logging. These findings underscore the potential of QXRD to enhance resource estimation, optimize exploration strategies, and contribute to more efficient and sustainable mineral exploration programs. The accuracy of QXRD was cross-validated with geological logs and geochemical data, confirming its reliability as a mineralogical discrimination tool.

Prediction of the Resource of the Power Plant Hybrid Vehicle

This article substantiates the possibility of solving a difficult task to offer a reliable estimate of the residual resource of the power plant of a hybrid car, which is exposed during operation to the influence of a complex set of external factors. This task is solved by monitoring the time change of diagnostic parameters and controlling the load–speed mode of the hybrid power plant, as well as using physical and chemical processes that cause the degradation of the traction battery. Methodological principles for assessing the resource of a hybrid power plant of a car have been developed, which are based on the disclosure of cause-and-effect relationships between realized and nominal quality indicators, operating conditions, and indicators of technical condition. The choice of rational solutions for the operation of a hybrid car is given, and the external conditions are determined, under which the residual life of the hybrid power plant will change. The scientific result of the study is a theoretical generalization of the scientific provisions of forecasting the resource of the hybrid power plant of a car. This ensures the efficient use of hybrid vehicles and reduces maintenance costs.

Accurate optimal quantum error correction thresholds from coherent information

Quantum error correcting (QEC) codes protect quantum information from decoherence as long as error rates fall below critical error thresholds. In general, obtaining thresholds implies simulating the QEC procedure using, in general, suboptimal decoding strategies. In a few cases and for sufficiently simple noise models, optimal decoding of QEC codes can be framed as a phase transition in disordered classical spin models. In both situations, accurate estimation of thresholds demands intensive computational resources. Here we use the coherent information of the mixed state of noisy QEC codes to accurately estimate the associated optimal QEC thresholds already from small-distance codes at moderate computational cost. We show the effectiveness and versatility of our method by applying it first to the topological surface and color code under bit-flip and depolarizing noise. We then extend the coherent information based methodology to phenomenological and quantum circuit level noise settings. For all examples considered, we obtain highly accurate estimates of optimal error thresholds from small, low-distance instances of the codes, in close agreement with threshold values reported in the literature. Our findings establish the coherent information as a reliable competitive tool for the calculation of optimal thresholds of state-of-the-art QEC codes under realistic noise models. Published by the American Physical Society 2024

Mapping South Africa's canopy-forming kelp forests using low-cost, high-resolution Sentinel-2 imagery

Globally, kelp forests play a vital role in providing ecological, social and economic services. They contribute to the livelihoods and resilience of many coastal communities. To date, a gap exists in contemporary, comprehensive and consistent maps of South African kelp forests, since they were last mapped partially in 2006 by covering 900 km of South Africa's coastline. This study addresses this gap by producing a comprehensive remotely sensed high-resolution 2016 map of canopy forming coastal kelp forests. Using the 10m resolution Sentinel-2 A and B spectral bands 4 (RED) and 8 (NIR), an expert derived Normalized Difference Vegetation Index (NDVI) threshold was used to detect surface protruding kelp forests along 1300 km coastline. The results were validated with expert verification and using Google Earth, data from historic maps, remote sensing and biomass estimation of South African kelp resources. Our study demonstrates the aptitude of Sentinel-2 for detecting canopy-forming kelps at low cost along South Africa's west and south coast. We report the encountered drawbacks in mapping an expansive coastline with varied sea conditions including wave activity, tides and turbidity. Comprehensive ground validation, long-term monitoring sites and the use of time series datasets remain a gap towards improving the calibration of remote sensing methods and long-term monitoring of South African kelp forests. Our results provide a foundation for continuous monitoring of South African kelp forests as well as exploring other unmapped kelp/similar habitats to inform research and environmental management.

Estimasi Sumberdaya Bauksit Menggunakan Metode Ordinary Kriging Pada PT. Wahana Petra Sejahtera Kabupaten Bengkayang Provinsi Kalimantan Barat

PT. Wahana Petra Sejahtera, which operates in the bauxite mining sector, requires resource calculations as a basis for carrying out its mining activities. This research aims to determine the levels and distribution of Al2O3, SiO2, and Fe2O3 and estimate bauxite resources using the ordinary kriging method. This research uses secondary data in coordinates, elevation, test pit depth, and slope data. Data processing uses software in database creation, statistical analysis, block and composite modeling, variogram analysis, resource estimation, and model validation. The calculation results for the Jirak block show that the Al2O3 waste content is 1,488,000 tons, low-grade 4,532,000 tons, medium-grade 1,824,000 tons, and high-grade 32,000 tons. For SiO2 the average content is 13.54% and the average Fe2O3 content is 13.50%. Meanwhile, in the Sindu block the Al2O3 waste content is 64,000 tons, medium-grade 1,188,000 tons, and high-grade 3,056,000 tons. Meanwhile, the average SiO2 content is 9.13% and the average Fe2O3 content is 12.06%. The total resources in the Jirak block are 7,696,000 tons with the amount meeting the COG of 800 tons. Meanwhile, in the Sindu block, the total resources are 5,008,000 tons with an amount that meets the COG of 1,280,000 tons.

Improving horizon computation algorithm with quasirandom sequences

The topographic horizon can be used to estimate shading for applications requiring an accurate solar resource estimate, including the effects of the local terrain. With high-resolution topography data, horizon estimation significantly contributes to the computation time needed for irradiance simulations. Approximate horizon algorithms have been proposed previously, sampling topography in a finite number of directions. This paper extends this idea by presenting a sampling approach based on the quasirandom sequences. The sampling strategy is optimized with 300,000 km2 of topography data from Europe and the USA. We demonstrate that our algorithm is several orders of magnitude faster than the precise horizon calculation and significantly improves the previous approximate algorithms. In particular, at the ground level with a topography resolution of 1 m/pixel, the proposed method achieved the same accuracy as the old approximate method four times faster. The horizon is one of the key methods in geographical information science and a part of many modeling tools in the built environment. Improvements in computation time benefit all applications that require irradiance modeling, e.g., the yield of vehicle- and building-integrated photovoltaic or temperature modeling of buildings.

Predictive control technique for solar photovoltaic power forecasting

An accurate estimation of photovoltaic (PV) power production is crucial for organising and regulating solar PV power plants. The suitable prediction is often affected by the variable nature of solar resources, system location and some internal/external disturbances, such as system effectiveness, climatic factors, etc. This paper develops a novel strategy for applying a predictive control technique to PV power forecasting applications in a smart grid environment. The strategy develops the model predictive control (MPC) under demand response (DR) and some data-driven methods. It has been found that it is challenging to model an MPC for solar power forecasting regardless of its robustness and ability to handle constraints and disturbance. Thus, an optimal quadratic performance index-based MPC scheme is formulated to model a forecasting method for a PV power prediction. This strategy is then compared with some machine learning approaches. The developed strategies solve the problem of accurately estimating the direct current (DC) power yielded from the PV plant in a real-world implementation. The study also considers external disturbances to evaluate the significance of the developed methods for a suitable forecast. Therefore, this study optimally demonstrates that an accurate solar PV DC power prediction can relatively be estimated with an appropriate strategy, such as MPC and MLs, considering the system disturbances. This study also offers promising results for intelligent and real-time energy resource estimation that assist in developing the solar power sector.