Resource Estimation Research Articles

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

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.

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.

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.

Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning.

Global demand for lithium, the primary component of lithium-ion batteries, greatly exceeds known supplies, and this imbalance is expected to increase as the world transitions away from fossil fuel energy sources. High concentrations of lithium in brines have been observed in the Smackover Formation in southern Arkansas (>400 milligrams per liter). We used published and newly collected brine lithium concentration data to train a random forest machine-learning model using geologic, geochemical, and temperature explanatory variables and create a map of predicted lithium concentrations in Smackover Formation brines across southern Arkansas. Using these predicted lithium maps with reservoir parameters and geologic information, we calculated that there are 5.1 to 19 million tons of lithium in Smackover Formation brines in southern Arkansas, which represents 35 to 136% of the current US lithium resource estimate. Based on these calculations, in 2022, 5000 tons of dissolved lithium were brought to the surface within brines as waste streams of the oil, gas, and bromine industries.

Calculation Modeling of Adsorbed and Bulk-Phase Oil Resources Based on Molecular Dynamics Simulations

Summary Nanopores prevalent in shale reservoirs significantly impact shale oil occurrence characteristics due to the strong intermolecular forces between crude oil molecules and the pore walls. Unlike bulk-phase oil, which is more readily recoverable with current technologies, the behavior of oil within these small-scale environments presents unique challenges. This study utilizes molecular dynamics simulations (MDSs) to investigate the characteristics of shale oil in slit nanopores, with the goal of refining a model that estimates the quantities of both bulk and adsorbed oil in shale reservoirs. We constructed models for three types of nanopores—organic graphene, illite, and quartz—using n-hexane (n-C6H14) as a proxy for shale oil. Our analysis reveals that mineral composition significantly influences fluid adsorption capacity, ranked as graphene > illite > quartz. Unlike prior research, we found that the critical flow pore diameter, which dictates the transition from adsorbed to free-flowing oil, cannot be simplistically equated to the combined thickness of adsorption layers. Specifically, in graphene pores with a diameter of 3.8 nm, the fluid mass density at the pore center still exhibits adsorption layer characteristics, forming up to nine layers. Building on these insights, we revised the shale reservoir resource estimation model to account for adsorption variances across different pore types. Our findings highlight the significant role of adsorbed oil in nanopores within shale reservoirs. Data from the Gulong shale oil block in the Daqing oil field indicate that adsorbed oil constitutes 37.15% of geological reserves, while bulk-phase oil accounts for the remaining 62.85%. This research provides essential data for accurately calculating shale oil reserves in nanopores, which are crucial for the effective exploitation of shale oil reservoirs.

Impact of Competent Persons' judgements in Mineral Resources classification

Uncertainty with regard to estimated grades and tonnages of a mineral deposit demands risk assessment in order to build investor confidence and attract the interest of other stakeholders in the success of a project. Uncertainties associated with Mineral Resource estimates can lead to unreliable production schedules and unpredictable cash flows. However, the techniques used in the mining industry to determine these uncertainties are inconsistent, because the important decisions taken in the process are solely dependent on the responsible Competent Person (CP), without limitations. This leads to disparities between different CPs' results, using data-sets from the same drill-holes. The various standard codes for public disclosure provide guidelines and recommendations for the classification of Mineral Resources and Reserves but do not provide details on, for example, the amount of geological and geostatistical information needed to qualify for each category of Resources and Reserves. The parameters used to generate the classification categories are subjectively assumed by the responsible CP. In this paper we investigate the impacts of different CPs' judgements on resource classification, using the same data-sets. The results underpin the need for the mining industry to develop a uniform Mineral Resources and Reserves classification framework that can minimize or avoid significant discrepancies that lead to potential misleading public disclosures.

Shale Mechanics in Deep Drilling: Enhancing Stability and Efficiency Through AI and Hydraulic Fracturing

Shale mechanics is a critical field in the drilling process, especially during deep drilling and the development of unconventional resources, where shale formations present various challenges. Given the diverse and multi-scale nature of shale, mechanical characteristics such as brittleness, fracture toughness, and stress behavior must be effectively controlled to ensure wellbore stability, optimize fracture treatments, and enhance drilling efficiency. Key areas of interest in this review include wellbore stability, hydraulic fracturing methods, and the application of innovative technologies, such as artificial intelligence (AI) and machine learning (ML). The current literature was extensively reviewed, with an emphasis on major papers addressing various aspects of shale mechanics. The appropriate selection of fluid and mud weight has been identified as critical for maintaining wellbore stability. Despite advances in hydraulic fracturing technology that have improved production efficiency, environmental concerns - particularly regarding water usage and chemical management - remain significant. Furthermore, the integration of AI and ML has enhanced production forecasts and resource estimation, though challenges related to data quality and availability persist. A review of the current state of knowledge in shale mechanics indicates that further progress will require improved geomechanical models, advanced monitoring tools, and environmentally sustainable strategies.

Weight Assignment Method and Application of Key Parameters in Shale Gas Resource Evaluation

The evaluation methods for shale gas resources and the key parameters involved are diverse. The main research object of this article is the key parameters in shale gas resource evaluation, and this study aims to quantitatively evaluate the required key parameters for calculation, thereby improving the credibility of shale gas resource evaluation results. This article mainly analyzes the weight analysis of the key parameters used in the resource evaluation process, focusing on the analysis and determination of key parameters controlling the generation, enrichment, and preservation of shale gas. The key parameters are graded and evaluated layer by layer, and the common key parameters that participate in the determination are extracted. Based on the above analysis, a weight conversion model is proposed to quantitatively evaluate the actual importance of key parameters in resource evaluation. By combining statistical research, the partial correlation coefficients in the correlation coefficients are applied to estimate the resource quantities in the exploration field in order to quantitatively evaluate the credibility of the resource evaluation results. This article focuses on the weight assignment of key parameters for calculating the resource quantity of the Jiaoshiba shale gas field in the Fuling area of Chongqing and obtains different weight results for different parameters. Using this method to assign key parameter weights to the target block can provide reliable parameter support for shale gas resource evaluation and provide strong support for shale gas resource estimation.

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.

A Quantitative Particle-Based Approach for the Geometallurgical Assessment of Tailings Deposits

Tailings generated during ore processing may host significant residual contents of valuable commodities, including critical metals. The particle properties of the tailings, such as mineralogy, particle size, and the surface liberation of ore minerals, strongly control processing behaviour. This study explores a novel combination of methods for incorporating particle data, derived from automated mineralogy, into geometallurgical models of tailings deposits to better understand their reprocessing potential and the economic feasibility of re-mining. This was achieved through binning of different particle types, geostatistical modelling of particle bin frequencies, and bootstrap resampling to reconstruct particle populations. The spatial distributions of processing-relevant particle properties throughout the tailings deposit were predicted with corresponding uncertainties. There are clear systematic trends in the spatial distributions of different particle types, resulting from the sedimentary-style deposition of the tailings. For instance, the tailings nearer the dam walls comprise coarser, silicate-rich particles, while fine-grained and well-liberated sulphide mineral particles are more abundant in the centre of the tailings deposit. As a result, robust models could be developed for the spatial distributions of particle size and mineralogy, which strongly control the sorting of particles during deposition, and other related properties, such as sulphide mineral grain sizes. Finally, a bulk sulphide flotation process was simulated and acid mine drainage potential estimated using the interpolated particle data. Around 58% of the sulphide minerals present could be recoverable by flotation, with the recoverable sulphide portion decreasing towards the centre of the TSF due to the fine-grained nature of the sulphide minerals. The acid mine drainage potential of the tailings is estimated to be moderate to high, indicating that the carbonate minerals present are not sufficient to neutralise the high acid-generating potential of the sulphide minerals. Overall, this study demonstrates how particle-based geometallurgical models can be developed and utilised for practical applications, with the aim of improving the accuracy of resource and reserve estimations of tailings deposits and the sustainable and responsible management of anthropogenic resources. The methodology proposed here can be easily transferred to other tailings deposits.

Decoupling and predicting natural gas deviation factor using machine learning methods

Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.

Block encodings of discrete subgroups on a quantum computer

We introduce a block encoding method for mapping discrete subgroups to qubits on a quantum computer. This method is applicable to general discrete groups, including crystal-like subgroups such as BI of SU(2) and V of SU(3). We detail the construction of primitive gates—the inversion gate, the group multiplication gate, the trace gate, and the group Fourier gate—utilizing this encoding method for BT and for the first time BI group. We also provide resource estimations to extract the gluon viscosity. The inversion gates for BT and BI are benchmarked on the quantum computer with estimated fidelities of 40−4+5% and 4−3+5%, respectively. Published by the American Physical Society 2024

Clay resource estimation using a geostatistical approach in Tam Binh district, Vinh Long province, Viet Nam

ABSTRACT Clay is an essential resource for improving the brick and ceramic industries. However, the demand for clay needs to grow. Estimating industrial mineral reserves requires an optimal and unbiased statistical tool. Geographical information systems and geostatistics are two promising tools that could be used to predict clay resources. The study objective was to apply these two tools to compare the performance of the methods for spatial clay resource prediction, considering clay thickness and spatial distribution. The demonstration site is in Tam Binh district, Vinh Long Province, Viet Nam. The results show that the Gaussian variation model at a 600 × 400 m distance is good for sampling and extrapolating clay thickness. This is based on 240 geolocated samples with different densities. Projected clay reserves match measurements, and the spatial statistical method is more accessible and valuable than the traditional method. The interpolated method’s total selected soil borehole number was optimized for clay resource estimation, and geostatistics can help manage and plan clay resources and save money. Future research should examine various spatial interpolation techniques and approaches in management, land use, and topography to improve the accuracy of calculations.

Enhancing Thin Coal Seam Detection in Eastern Indian Coalfields Using ICWT-Decon-Based Seismic Attributes and Acoustic Impedance Inversion

A high-resolution seismic survey (HRSS) is often used in coal exploration to bridge the data gap between two consecutive boreholes and avoid ambiguity in geological interpretation. The application of high-resolution seismic surveys in the Indian context is challenging as the delineation of thin non-coal layers within the coal layer requires a very high seismic data resolution. However, conventional seismic processing techniques fail to resolve thin coal/non-coal layers and faults, which is crucial for the precise estimation of coal resources and mine economics. To address these issues, we applied the inverse continuous wavelet transform deconvolution (ICWT-Decon) technique to post-stack depth-migrated seismic sections. We examined the feasibility of the ICWT-Decon technique in both a synthetic post-stack depth-migrated model and 2D/3D seismic data from the North Karanpura and Talcher Coalfields in Eastern India. The results offered enhanced seismic sections, attributes (similarity and sweetness), and acoustic inversion that aided in the precise positioning of faults and the delineation of a thin non-coal layer of 4.68 m within a 16.7 m coal seam at an approximate depth of 450 m to 550 m. This helped in the refinement of the resource estimation from 74.96 MT before applying ICWT-Decon to 55.92 MT afterward. Overall, the results of the study showed enhancements in the seismic data resolution, the better output of seismic attributes, and acoustic inversion, which could enable more precise lithological and structural interpretation.

Dynamic primary resources, not just wild prey availability, underpin lion depredation of livestock in a savanna ecosystem.

Because it can lead to retaliatory killing, livestock depredation by large carnivores is among the foremost threats to carnivore conservation, and it severely impacts human well-being worldwide. Ongoing climate change can amplify these human-wildlife conflicts, but such issues are largely unexplored, though are becoming increasingly recognized. Here, we assessed how the availability of primary resources and wild prey interact to shape large carnivore selection for livestock rather than wild prey (i.e., via prey switching or apparent competition). Specifically, we combined remotely sensed estimates of primary resources (i.e., water availability and primary productivity), wild prey movement, and 7 years (2015-2021) of reports for livestock depredation by African lions (Panthera leo) in the Makgadikgadi Pans ecosystem, Botswana. Although livestock depredation did not vary between wet versus dry seasons, analyses at finer temporal scales revealed higher incidences of livestock depredation when primary production, water availability, and wild prey availability were lower, though the effects of wild prey availability were mediated by water availability. Increased precipitation also amplified livestock depredation events despite having no influence on wild prey availability. Our results suggest that livestock depredation is influenced by the diverse responses of livestock, wild prey, and lions to primary resource availability, a driver that is largely overlooked or oversimplified in studies of human-carnivore conflict. Our findings provide insight into tailoring potential conflict mitigation strategies to fine-scale changes in resource conditions to efficiently reduce conflict and support human livelihoods.