Journal articles on the topic 'Geological model selection'
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Liu, Yan. "Research of Application of FCE to Project Supervision." Applied Mechanics and Materials 347-350 (August 2013): 214–19. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.214.
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Construct the fuzzy comprehensive evaluation model on geological exploration project supervision index system, and be able to provide reference for geological and mineral exploration relating departments to make decisions. The comprehensive evaluation model is proven to be effective by analyzing some index selection solutions. Apply fuzzy comprehensive evaluation (FCE) method to the selection of geological exploration project supervision indexes, analyze the weights of the various factors which affect the indexes first, then perform fuzzy comprehensive evaluating on the indexes to be determined, and finally classify the evaluated objects and select corresponding indexes according to evaluation results. The method resolves the issues of strong subjectivity and results lacking of quantization which are caused by selecting indexes by simple analogy, and has higher scientificity and practical values.
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Lin, Lai Kuang, Yi Min Xia, Fei He, Qing Song Mao, and Kui Zhang. "Geological Adaptive Cutterhead Selection for EPB Shield Based on BP Neural Network." Applied Mechanics and Materials 607 (July 2014): 118–23. http://dx.doi.org/10.4028/www.scientific.net/amm.607.118.
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In view of complex and fuzziness of geological adaptive cutterhead selection for earth pressure balance (EPB) shield, a cutterhead selection method based on BP neural network is put forward. Considering the structure characteristics of EPB shield cutterhead, typical cutterhead types are classified and summarized based on cutterhead topology structure and number of spokes. After analyzing the determinants of cutterhead selection, one-to-many mapping relation between cutterhead type and geological parameters is put forward, and then core geologic parameters related to cutterhead selection are concluded. The feasibility of using neural network method to choose the cutterhead type is analyzed, and a BP neural network training model for cutterhead selection is set up and tested in testing sample data. The result shows that the selected cutterhead and the construction cutterhead are basically consistent. The feasibility of this method is proved and it can be theoretical basis for the cutterhead structure design which will improve scientific of cutterhead selection.
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Gunnink, J. L., D. Maljers, S. F. van Gessel, A. Menkovic, and H. J. Hummelman. "Digital Geological Model (DGM): a 3D raster model of the subsurface of the Netherlands." Netherlands Journal of Geosciences - Geologie en Mijnbouw 92, no. 1 (April 2013): 33–46. http://dx.doi.org/10.1017/s0016774600000263.
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AbstractA 3D geological raster model has been constructed of the onshore of the Netherlands. The model displays geological units for the upper 500 m in 3D in an internally consistent way. The units are based on the lithostratigraphical classification of the Netherlands. This classification is used to interpret a selection of boreholes from the national subsurface database. Additional geological information regarding faults, the areal extent of each unit and conceptual genetic models have been combined in an automated workflow to interpolate the basal surfaces of each unit on 100 × 100 metre (x,y dimensions) raster cells. The combination of all interpolated basal surfaces results in a 3D Digital Geological Model (DGM) of the subsurface. A measure of uncertainty of each of these surfaces is also given. The automated workflow ensures an easily updatable subsurface model. The outputs are available for end users through www.dinoloket.nl.
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Zhang, An Gang, Zi Fei Fan, and Heng Song. "Evaluation Model of Profile Control Layers' Selection Based on Fuzzy Cluster Analysis." Advanced Materials Research 734-737 (August 2013): 1374–80. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.1374.
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The selection of profile control layers is based on the decision made by single factor at present. Fuzzy cluster analysis based on transitive closure is introduced to establish an evaluation model of profile control layers' selection. Firstly, combining the static geological data and dynamic development data, the evaluation indexes which affect the selection of profile control layers are screened to form the evaluation index system; secondly, according to fuzzy cluster analysis theory, the evaluation index matrix related to profile control layers' selection is dynamically clustered, and the profile control layers are selected on the basis of each category's characteristics of geological and development; finally, the results of profile control layers' selection are examined by the comparison of water injection profile. In addition, the evaluation model is put into practice in a testing well group, and the application shows that the profile control layers selected by the model is reasonable and reliable.
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Bazargan, Hamid, and Mike Christie. "Bayesian model selection for complex geological structures using polynomial chaos proxy." Computational Geosciences 21, no. 3 (March 1, 2017): 533–51. http://dx.doi.org/10.1007/s10596-017-9629-0.
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Abbassi, Bahman, Li-Zhen Cheng, Michel Jébrak, and Daniel Lemire. "3D Geophysical Predictive Modeling by Spectral Feature Subset Selection in Mineral Exploration." Minerals 12, no. 10 (October 14, 2022): 1296. http://dx.doi.org/10.3390/min12101296.
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Several technical challenges are related to data collection, inverse modeling, model fusion, and integrated interpretations in the exploration of geophysics. A fundamental problem in integrated geophysical interpretation is the proper geological understanding of multiple inverted physical property images. Tackling this problem requires high-dimensional techniques for extracting geological information from modeled physical property images. In this study, we developed a 3D statistical tool to extract geological features from inverted physical property models based on a synergy between independent component analysis and continuous wavelet transform. An automated interpretation of multiple 3D geophysical images is also presented through a hybrid spectral feature subset selection (SFSS) algorithm based on a generalized supervised neural network algorithm to rebuild limited geological targets from 3D geophysical images. Our self-proposed algorithm is tested on an Au/Ag epithermal system in British Columbia (Canada), where layered volcano-sedimentary sequences, particularly felsic volcanic rocks, are associated with mineralization. Geophysical images of the epithermal system were obtained from 3D cooperative inversion of aeromagnetic, direct current resistivity, and induced polarization data sets. The recovered cooperative susceptibilities allowed locating a magnetite destructive zone associated with porphyritic intrusions and felsic volcanoes (Au host rocks). The practical implementation of the SFSS algorithm in the study area shows that the proposed spectral learning scheme can efficiently learn the lithotypes and Au grade patterns and makes predictions based on 3D physical property inputs. The SFSS also minimizes the number of extracted spectral features and tries to pick the best representative features for each target learning case. This approach allows interpreters to understand the relevant and irrelevant spectral features in addition to the 3D predictive models. Compared to conventional 3D interpolation methods, the 3D lithology and Au grade models recovered with SFSS add predictive value to the geological understanding of the deposit in places without access to prior geological and borehole information.
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Scheer, Dirk, Wilfried Konrad, Holger Class, Alexander Kissinger, Stefan Knopf, and Vera Noack. "Regional-scale brine migration along vertical pathways due to CO<sub>2</sub> injection – Part 1: The participatory modeling approach." Hydrology and Earth System Sciences 21, no. 6 (June 9, 2017): 2739–50. http://dx.doi.org/10.5194/hess-21-2739-2017.
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Abstract. Saltwater intrusion into potential drinking water aquifers due to the injection of CO2 into deep saline aquifers is one of the potential hazards associated with the geological storage of CO2. Thus, in a site selection process, models for predicting the fate of the displaced brine are required, for example, for a risk assessment or the optimization of pressure management concepts. From the very beginning, this research on brine migration aimed at involving expert and stakeholder knowledge and assessment in simulating the impacts of injecting CO2 into deep saline aquifers by means of a participatory modeling process. The involvement exercise made use of two approaches. First, guideline-based interviews were carried out, aiming at eliciting expert and stakeholder knowledge and assessments of geological structures and mechanisms affecting CO2-induced brine migration. Second, a stakeholder workshop including the World Café format yielded evaluations and judgments of the numerical modeling approach, scenario selection, and preliminary simulation results. The participatory modeling approach gained several results covering brine migration in general, the geological model sketch, scenario development, and the review of the preliminary simulation results. These results were included in revised versions of both the geological model and the numerical model, helping to improve the analysis of regional-scale brine migration along vertical pathways due to CO2 injection.
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Amaya, M., N. Linde, and E. Laloy. "Adaptive sequential Monte Carlo for posterior inference and model selection among complex geological priors." Geophysical Journal International 226, no. 2 (April 26, 2021): 1220–38. http://dx.doi.org/10.1093/gji/ggab170.
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SUMMARY Bayesian model selection enables comparison and ranking of conceptual subsurface models described by spatial prior models, according to the support provided by available geophysical data. Deep generative neural networks can efficiently encode such complex spatial priors, thereby, allowing for a strong model dimensionality reduction that comes at the price of enhanced non-linearity. In this setting, we explore a recent adaptive sequential Monte Carlo (ASMC) approach that builds on annealed importance sampling (AIS); a method that provides both the posterior probability density function (PDF) and the evidence (a central quantity for Bayesian model selection) through a particle approximation. Both techniques are well suited to parallel computation and rely on importance sampling over a sequence of intermediate distributions, linking the prior and the posterior PDF. Each subsequent distribution is approximated by updating the particle weights and states, compared with the previous approximation, using a small pre-defined number of Markov chain Monte Carlo (MCMC) proposal steps. Compared with AIS, the ASMC method adaptively tunes the tempering between neighboring distributions and performs resampling of particles when the variance of the particle weights becomes too large. We evaluate ASMC using two different conceptual models and associated synthetic cross-hole ground penetrating radar tomography data. For the most challenging test case, we find that the ASMC method is faster and more reliable in locating the posterior PDF than state-of-the-art adaptive MCMC. The evidence estimates are found to be robust with respect to the choice of ASMC algorithmic variables and much less sensitive to the model proposal type than MCMC. The variance of the evidence estimates are best estimated by replication of ASMC runs, while approximations based on single runs provide comparable estimates when using a sufficient number of proposal steps in approximating each intermediate distribution.
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Semen, Korchak. "Obtaining initial data for forecast engineering geological model construction." Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal 5 (October 20, 2022): 66–76. http://dx.doi.org/10.21440/0536-1028-2022-5-66-76.
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Objective and relevance. Technologies in mining and geology are intensely developing making the present research particularly relevant. The research considers the problems of obtaining reliable and high-quality data on the degree and nature of rock mass fracturing in a solid mineral deposit. These are fundamental data required to construct a forecast engineering geological model of a deposit. The engineering geological model is usually based on a structural geological model with the addition of various geomechanical parameters used in generally accepted international classifications for rock stability analysis, in particular, RMR (Bieniawski), MRMR (Laubscher), and Q-system (Barton). Methods of research. The authors describe the procedure for obtaining classification geomechanical parameters of a rock mass according to the data from oriented and non-oriented core drilling of special engineering geological (geomechanical) boreholes. Research results. The main factors and conditions for reliable data acquisition are given. Among the most important ones are: economic feasibility and validity of geomechanical boreholes drilling as well as their number; features of the geological and structural composition of a deposit as a condition for borehole location selection; proper organization of the drilling process aimed at obtaining high-quality core material; characteristics of geomechanical description of the core, depending on the rating system of rock mass classification applied. Scope of results. By way of example, deposits with different geological and structural conditions are listed where the described procedure was applied. High-quality and reliable information about the structural and tectonic features of the rock mass allows to create and fill the engineering geological model with data and solve the problems of stability analysis for almost any area of the future mine working.
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Eliawa, Ali i. "Using Geological and Topographic Maps in Site Selection of Solid Waste Disposal." Al-Mukhtar Journal of Sciences 37, no. 1 (March 31, 2022): 29–40. http://dx.doi.org/10.54172/mjsc.v37i1.446.
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Currently, land shortage for solid waste in most urban areas is a significant and growing potential problem. Although some efforts are made to decrease and recover the waste, landfill is still the most common process for waste disposal. Site selection of solid waste dumping in urban areas is a serious subject because of its huge effect on the economy, ecology, and environmental health. Consequently, several criteria must be created because of the difficulty of the parameters to select the process for combination in social, environmental and technical parameters. In this research, the most appropriate sites for locating dumping garbage are determined using the Geographical Information System (GIS) by implementing both methods Boolean logic model and Index overlay model. Based on several objectives, a provided spatial data set consisting of several maps in the form of layers, such as land use, geological distribution, landslides, etc., were used in the modeling process to choose the best site to dump the garbage of Chinchina city that used as a case in this application. The findings show that the Boolean logic model identified only two areas that met the criteria, whereas the Index overlay model identified three important classes through weight; unsuitable, moderate and suitable regions for construction waste disposal.
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Sunuwar, S. C. "Geological mapping in the Nepal Himalaya: importance and challenges for underground structures." Journal of Nepal Geological Society 51 (December 31, 2016): 89–95. http://dx.doi.org/10.3126/jngs.v51i0.24096.
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Geological mapping is very important technique to predict geological condition for underground structures. It helps to construct geological model for site selection and designing of any underground structures. Geological uncertainty is directly proportional to the accuracy of geological mapping. More accurate geological mapping resulted fewer uncertainties. Precise delineation of faults, shear/weak zones and water bearing zones is important part of the geological mapping to predict uncertainties. Geological mapping to predict geological condition for underground structures is a challenge in the tectonically active Nepal Himalaya due to thrusting, faulting, folding and reverse metamorphism nature of rocks with difficult terrain and high overburden. The mapping for underground structures is mostly focus on rock mass properties, faults, weak/shear zones, fractured zone, joints, folds, weathering depth and ground water bearing zones. This paper highlights importance of geological mapping and challenges for underground structures with case studies of uncertainties faced due to poor geological mapping.
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Koteras, Aleksandra, Jarosław Chećko, Tomasz Urych, Małgorzata Magdziarczyk, and Adam Smolinski. "An Assessment of the Formations and Structures Suitable for Safe CO2 Geological Storage in the Upper Silesia Coal Basin in Poland in the Context of the Regulation Relating to the CCS." Energies 13, no. 1 (January 1, 2020): 195. http://dx.doi.org/10.3390/en13010195.
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The paper presents an analysis of the possible location of geological formations suitable for CO2 storage in the Upper Silesia Coal Basin, Poland. The range of the reservoir has been determined on the basis of an analysis of basic geological parameters, which determine the selection criteria for sites suitable for CO2 storage. A dynamic modelling of the CO2 distribution in the aquifer is presented. Based on the constructed model of migration, reactivity, and geochemical transport of CO2 in geological structures, it is possible to identify potential migration routes and escape sites of CO2 on the surface. The analysis of the technical and geological possibilities of CO2 storage was carried out according to the regulations of the complex Polish geological law, specifically in terms of sequestration possibilities in geological formations.
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Lv, Wen Yu, and Zhi Hui Zhang. "Application of Thick Coal Seam Mining Method Prediction Model Based on Artificial Neural Network." Advanced Materials Research 962-965 (June 2014): 242–46. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.242.
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Because of thick coal seam mining method selection is not only affected by coal seam geological conditions, but also limited by workers, and not fully utilization of experts` experience, the effect of tradition coal mining method selection methods are not ideal. The thick coal seam mining method prediction model based on artificial neural network (TCSMMPM-ANN) was established through the analysis of thick coal seam mining by using Levenberg – Marquardt (L-M) improved algorithm to train network, the simulation results of network test show that this model can provide a new research idea for thick coal seam mining method optimal selection and face economic and technical index prediction, it will have a broad prospect in thick coal mining.
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Yang, Feng, Xudong Hu, Zhenyao Xia, Lei Cui, and Qi Yang. "Susceptibility Evaluation of Debris Flow Disaster in Plateau Hydropower Cascade Development Reservoir Area." Nature Environment and Pollution Technology 22, no. 1 (March 2, 2023): 229–36. http://dx.doi.org/10.46488/nept.2023.v22i01.021.
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The Rumei Hydropower Station is a typical cascade hydropower development project in a plateau area. The dam site is located in an area with complex topography, lithology, and geological structure. Geological disasters are developed in the area, mainly debris flow. Thus, taking the dam site and the surrounding areas as key evaluation objects, the engineering geological characteristics, geological environment characteristics, and the susceptibility and risk of geological disasters that may be caused are predicted and evaluated. The main methods used in this assessment are the binary logistic regression model and expert evaluation. The results show that the susceptibility to geological disasters is small and medium. The results of this study could provide a scientific basis for the rationality of the general layout and site selection of the project construction in the plateau water elevator level development reservoir area.
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Pakyuz-Charrier, Evren, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mark Jessell. "Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization." Solid Earth 9, no. 2 (April 6, 2018): 385–402. http://dx.doi.org/10.5194/se-9-385-2018.
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Abstract. Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.
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Shang, Xiaofei, Huawei Zhao, Shengxiang Long, and Taizhong Duan. "A Workflow for Integrated Geological Modeling for Shale Gas Reservoirs: A Case Study of the Fuling Shale Gas Reservoir in the Sichuan Basin, China." Geofluids 2021 (August 25, 2021): 1–22. http://dx.doi.org/10.1155/2021/6504831.
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Shale gas reservoir evaluation and production optimization both require geological models. However, currently, shale gas modeling remains relatively conventional and does not reflect the unique characteristics of shale gas reservoirs. Based on a case study of the Fuling shale gas reservoir in China, an integrated geological modeling workflow for shale gas reservoirs is proposed to facilitate its popularization and application and well improved quality and comparability. This workflow involves four types of models: a structure-stratigraphic model, reservoir (matrix) parameter model, natural fracture (NF) model, and hydraulic fracture (HF) model. The modeling strategies used for the four types of models vary due to the uniqueness of shale gas reservoirs. A horizontal-well lithofacies sublayer calibration-based method is employed to build the structure-stratigraphic model. The key to building the reservoir parameter model lies in the joint characterization of shale gas “sweet spots.” The NF models are built at various scales using various methods. Based on the NF models, the HF models are built by extended simulation and microseismic inversion. In the entire workflow, various types of models are built in a certain sequence and mutually constrain one another. In addition, the workflow contains and effectively integrates multisource data. Moreover, the workflow involves multiple model integration processes, which is the key to model quality. The selection and optimization of modeling methods, the innovation and development of modeling algorithms, and the evaluation techniques for model uncertainty are areas where breakthroughs may be possible in the geological modeling of shale gas reservoirs. The workflow allows the complex process of geological modeling of shale gas reservoirs to be more systematic. It is of great significance for a dynamic analysis of reservoir development, from individual wells to the entire gas field, and for optimizing both development schemes and production systems.
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Peters, Jared L., Felix Butschek, Ross O'Connell, Valerie Cummins, Jimmy Murphy, and Andrew J. Wheeler. "Geological seabed stability model for informing Irish offshore renewable energy opportunities." Advances in Geosciences 54 (October 12, 2020): 55–65. http://dx.doi.org/10.5194/adgeo-54-55-2020.
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Abstract. Climate change has driven the European Union to propose a reduction in carbon emissions by increasing renewable energy production. Although Ireland is rich in renewable energy, especially offshore wind resources, it is failing to reduce its annual carbon emissions. This study endeavours to improve Ireland's marine spatial planning abilities and offshore renewable energy developments by harmonising and customising a unique geological dataset for incorporation into geospatial assessments of Ireland's continental shelf. A dataset of 1858 points, including 17 new seabed samples collected at strategic sites for this study, is created and used to build a series of geospatial outputs. Data are interpolated with empirical Bayesian kriging to use variogram analyses for probabilistically interpolating coded geological values. The interpolation results are validated through leave-one-out cross-validation and combined with bespoke models of bathymetry and seabed slope using map algebra. The final model reveals areas of relative probable seabed stability based on geological and geomorphological characteristics and is shown to comport with known conditions in several locations. Results suggest that the methods and results presented here could provide useful information to future planning activities and initial site selection assessments.
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Fu, Xuesong, Quanmei Gong, Yaojie Wu, Yu Zhao, and Hui Li. "Prediction of EPB Shield Tunneling Advance Rate in Mixed Ground Condition Using Optimized BPNN Model." Applied Sciences 12, no. 11 (May 28, 2022): 5485. http://dx.doi.org/10.3390/app12115485.
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Tunneling in mixed ground often results in severe torque fluctuations and a low advance rate. Therefore, choosing a reasonable set of parameters for accurate advance rate prediction is paramount to reduce cutter wear and improve tunneling efficiency. However, since the geological parameters in mixed ground conditions are diverse and uncertain, the prediction of the advance rate (AR) of EPB shield tunneling is significantly more difficult than that in homogeneous ground (i.e., full-face hard-rock ground). In addition, the operating parameters of the EPB shield tunneling can be subjective and suboptimal, and each of them has some intricate influence on AR. In this paper, an optimized back-propagation neural network by genetic algorithm (BPNN-GA) was proposed for reasonable operating parameter selection and accurate AR prediction, and four typical machine learning methods were used for comparison. Five processing strategies with different input parameters were also proposed and compared to determine the optimum selection of geological parameters in mixed ground conditions. The proposed models with strategies were adopted in the case study of the Nanjing Metro Line S6 project, and a total of 1188 rings of datasets were used for this study. The results showed that the proposed modified BPNN with the genetic algorithm could be effectively implemented for the AR prediction. It concluded that Strategy B—i.e., using the composite ratio and the geological parameters of each layer as input—was the best strategy in mixed ground conditions for advance rate prediction. Hence, a high correlation between measured and predicted AR was observed in this study with a correlation coefficient (R2) of 0.920.
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Yang, Hou-Cheng, Guanyu Hu, and Ming-Hui Chen. "Bayesian Variable Selection for Pareto Regression Models with Latent Multivariate Log Gamma Process with Applications to Earthquake Magnitudes." Geosciences 9, no. 4 (April 12, 2019): 169. http://dx.doi.org/10.3390/geosciences9040169.
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Generalized linear models are routinely used in many environment statistics problems such as earthquake magnitudes prediction. Hu et al. proposed Pareto regression with spatial random effects for earthquake magnitudes. In this paper, we propose Bayesian spatial variable selection for Pareto regression based on Bradley et al. and Hu et al. to tackle variable selection issue in generalized linear regression models with spatial random effects. A Bayesian hierarchical latent multivariate log gamma model framework is applied to account for spatial random effects to capture spatial dependence. We use two Bayesian model assessment criteria for variable selection including Conditional Predictive Ordinate (CPO) and Deviance Information Criterion (DIC). Furthermore, we show that these two Bayesian criteria have analytic connections with conditional AIC under the linear mixed model setting. We examine empirical performance of the proposed method via a simulation study and further demonstrate the applicability of the proposed method in an analysis of the earthquake data obtained from the United States Geological Survey (USGS).
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Juang, C. Hsein, Wenping Gong, James R. Martin, and Qiushi Chen. "Model selection in geological and geotechnical engineering in the face of uncertainty - Does a complex model always outperform a simple model?" Engineering Geology 242 (August 2018): 184–96. http://dx.doi.org/10.1016/j.enggeo.2018.05.022.
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Liu, Shuai, Jieyong Zhu, Dehu Yang, and Bo Ma. "Comparative Study of Geological Hazard Evaluation Systems Using Grid Units and Slope Units under Different Rainfall Conditions." Sustainability 14, no. 23 (December 2, 2022): 16153. http://dx.doi.org/10.3390/su142316153.
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The selection of evaluation units in geological hazard evaluation systems is crucial for the evaluation results. In an evaluation system, relevant geological evaluation factors are selected and the study area is divided into multiple regular or irregular independent units, such as grids, slopes, and basins. Each evaluation unit, which includes evaluation factor attributes and hazard point distribution data, is placed as an independent individual in a corresponding evaluation model for use in a calculation, and finally a risk index for the entire study area is obtained. In order to compare the influence of the selection of grid units or slope units—two units frequently used in geological hazard evaluation studies—on the accuracy of evaluation results, this paper takes Yuanyang County, Yunnan Province, China, as a case study area. The area was divided into 7851 slope units by the catchment basin method and 12,985,257 grid units by means of an optimal grid unit algorithm. Nine evaluation factors for geological hazards were selected, including elevation, slope, aspect, curvature, land-use type, distance from a fault, distance from a river, engineering geological rock group, and landform type. In order to ensure the objective comparison of evaluation results for geological hazard susceptibility with respect to grid units and slope units, the weighted information model combining the subjective weighting AHP (analytic hierarchy process) and the objective statistical ICM (information content model) were used to evaluate susceptibility with both units. Geological risk evaluation results for collapses and landslides under heavy rain (25–50 mm), rainstorm (50–100 mm), heavy rainstorm (150–250 mm), and extraordinary rainstorm (>250 mm) conditions were obtained. The results showed that the zoning results produced under the slope unit system were better than those produced under the grid unit system in terms of the distribution relationship between hazard points and hazard levels. In addition, ROC (receiver operating characteristic) curves were used to test the results of susceptibility and risk assessments. The AUC (area under the curve) values of the slope unit system were higher than those of the grid unit system. Finally, the evaluation results obtained with slope units were more reasonable and accurate. Compared with the results from an actual geological hazard susceptibility and risk survey, the evaluation results for collapse and landslide geological hazards under the slope unit system were highly consistent with the actual survey results.
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Papafotiou, Alexandros, Chao Li, Dominik Zbinden, Mohamed Hayek, Michael J. Hannon, and Paul Marschall. "Site Selection for a Deep Geological Repository in Switzerland: The Role of Performance Assessment Modeling." Energies 15, no. 17 (August 23, 2022): 6121. http://dx.doi.org/10.3390/en15176121.
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In the development of deep geological repositories (DGRs), performance assessment modeling is used to evaluate the integrity and performance of the engineered and geological barriers for thousands or millions of years of evolution of the disposal system. To evaluate the suitability of a site for a DGR, geoscientific data from dedicated site investigation programs are integrated into site-specific assessments. This paper presents the development and implementation of a modeling workflow aimed at comparing three potential siting areas for a DGR in Switzerland from the viewpoint of long-term safety and technical feasibility. The workflow follows the guidelines of the national regulator addressing safety relevant criteria such as the barrier efficiency of the host rock and its mechanical and chemical integrity in response to repository-induced influences and the long-term stability of the repository site over geological scales. In the regulatory requirements, the role of parametric, conceptual, and scenario uncertainty has been identified as an issue of special importance in the site selection process. The assessment approach comprises a portfolio of numerical models for the simulation of solute, gas and heat transport in the repository nearfield. The modeling was performed with deterministic as well as probabilistic variants integrated in an indicator-based approach that allows the consistent comparison of the candidate sites using quantitative dimensionless performance indices. The model-based assessment of the sites allows a traceable, transparent, and verifiable implementation of the site selection process.
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Giraud, Jérémie, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay. "Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code." Geoscientific Model Development 14, no. 11 (November 2, 2021): 6681–709. http://dx.doi.org/10.5194/gmd-14-6681-2021.
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Abstract. The quantitative integration of geophysical measurements with data and information from other disciplines is becoming increasingly important in answering the challenges of undercover imaging and of the modelling of complex areas. We propose a review of the different techniques for the utilisation of structural, petrophysical, and geological information in single physics and joint inversion as implemented in the Tomofast-x open-source inversion platform. We detail the range of constraints that can be applied to the inversion of potential field data. The inversion examples we show illustrate a selection of scenarios using a realistic synthetic data set inspired by real-world geological measurements and petrophysical data from the Hamersley region (Western Australia). Using Tomofast-x's flexibility, we investigate inversions combining the utilisation of petrophysical, structural, and/or geological constraints while illustrating the utilisation of the L-curve principle to determine regularisation weights. Our results suggest that the utilisation of geological information to derive disjoint interval bound constraints is the most effective method to recover the true model. It is followed by model smoothness and smallness conditioned by geological uncertainty and cross-gradient minimisation.
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Liu, Zhi Min, Xi Gao Liu, Jin Tao Zhang, Wei Jie Zhang, and Miao Wu. "Observation Frequency Selection Based on Dynamic and Electric Field Excitation Method for Advanced Detection in Coal Mine Roadway." Applied Mechanics and Materials 675-677 (October 2014): 1301–7. http://dx.doi.org/10.4028/www.scientific.net/amm.675-677.1301.
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The dynamic and electric field excitation method is a new advanced detection method. In this method, the observation frequency and of emission electrode must be reasonably selected according to the actual geological conditions of coal mine roadway to obtain the apparent frequency with adequate Induced Polarization (IP) information of geological anomalies and improve the measurement accuracy of the instrument. This paper completed the simulation of apparent frequency varying with the observation frequency and Cole-Cole model parameters with MATLAB software. The results show that the key to selecting the observation frequency mainly depends on the charge and discharge time constant .Then the paper analyzed the effect of the measured apparent frequency on the measurement accuracy. According to time constant values of geological conditions in coal mine roadway, considering the efficiency and electromagnetic coupling effects on the measurement, it is best to select the observation frequency within a frequency range of 0.05~0.1Hz and the frequency ratio coefficient of 13 or 15. Now apparent IP effect is obtained.
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Chen, Kui, Zheng Zheng, and Qian Zhang. "Residual Analysis in Inverse Identification of Total Thrust on Shield Tunneling Machine." Applied Mechanics and Materials 455 (November 2013): 557–60. http://dx.doi.org/10.4028/www.scientific.net/amm.455.557.
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Total thrust on shield tunneling machine is the most important mechanical quantity, while its also the core parameter which can reflect the geological adaptability of the shield. Mechanical model of total thrust is established by applying the mechanical analysis firstly. Multiple regression method is applied to indentify undetermined coefficients of mechanical model. Based on the on-site data acquired from Rd. TieDong to Rd. ZhangXingZhuang of Tianjin No.3 subway project, regularity and compositions of model residual are discussed and inverse identification model of total thrust is further proposed. The comparison between identification results and the on-site data verifies the feasibility of inverse identification model. Analysis results indicate that inverse identification model can dynamically reflect the relationship among total thrust, operating parameters and geological parameters. This work can offer helpful references for thrust selection and real-time control.
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Karablin, Mikhail, Sergei Prostov, and Nikolai Smirnov. "Assessing the impact made by groundwater processes and undermining on coal pit wall stability." Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal, no. 1 (February 17, 2021): 36–44. http://dx.doi.org/10.21440/0536-1028-2021-1-36-44.
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Introduction. The reliability of geomechanical prediction depends on the level of detail of databases covering geological structure, geometry and physical properties of the rock mass under investigation. In order to improve the accuracy of coal pit wall stability prediction, following the generalization of databases containing geological survey, groundwater monitoring, geophysical sounding and mine surveying, it is advisable to construct three-dimensional geological-geophysical models accounting for the main adverse factors, and thereafter search for the most hazardous section. Research aim is to predict wall stability according to the developed algorithm based on the threedimensional geological-geophysical model. Research methodology includes a search for the most hazardous rock mass site section by the ratio between shear and retaining forces within the established zones with anomalous physical characteristics. Results. By generalizing databases containing geological studies, groundwater monitoring, geophysical sounding by the method of electrical resistivity tomography, and mine surveying, a three-dimensional geological- geophysical model has been constructed of a wall loaded with “heap of dry rock atop of the hydraulic dump” man-made structure and undermined by underground works. The trial site stability has been predicted for the true state of mining. Comparative analysis of the obtained data has been carried out. Summary. The combination of natural and man-made factors, including hydrogeological conditions of the territory, seasonal and climatic behavior, tectonic faulting of the deposit and shear zones connected with undermining result in the development of a rather complex geological structure of the wall which includes local deconsolidated and waterlogged zones significantly reducing the stability of the pit slope. At the trial site of Kedrovsky pit due to spatial and temporal alternation of properties and state of rock within the landslide hazardous zone, the variation range of the factor of safety in six typical sections amounts n = 1.06–2.39. For that reason the objective prediction of slope stability in similar conditions (in addition to geological survey and hydrogeological observations data analysis) should include geophysical monitoring of anomalous zones origination and development, hereupon creation of a treedimensional geological-physical model, and the automated calculation of the factor of safety including repeated selection of the most hazardous section.
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Lv, Yan, and Laijun Lu. "Geological Mineral Energy and Classification Based on Machine Learning." Wireless Communications and Mobile Computing 2021 (November 26, 2021): 1–7. http://dx.doi.org/10.1155/2021/2788161.
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In order to mine geological mineral energy and study on geological mineral energy classification, a method based on a wireless sensor was proposed. Of logistic regression, artificial neural networks, random forests, and main wireless sensor algorithms of support vector machine (SVM) with the model in the application of the energy mineral resource prediction practice effects are reviewed and discuss the practical application in the process of sample selection, the wrong points existing in the cost, the uncertainty evaluation, and performance evaluation of the model using wireless sensor algorithm, random forest of the probability distribution of mineralization in the study area is calculated, and five prospecting potential areas are delineated. The results show that the ratio of ore-bearing unit and non-ore-bearing unit is 1 : 1, and the best random forest training model is obtained. 70% of the training sample set was randomly selected as the training set, and the remaining 30% was used as the test set to construct the random forest model. The training accuracy of the model is 96.7%, and the testing accuracy is 96.5%. Both model training accuracy and model testing accuracy are very high, which proves the accuracy of RF model construction and achieves satisfactory results. In this study, a wireless sensor is successfully applied to 3D mineral energy prediction, which makes a positive exploration for mineral resource prediction and evaluation in the future. Finally, the prediction of mineral resource energy based on a wireless sensor is an important trend of future development.
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Zhou, Changlin, Lang Zhou, Fei Liu, Weihua Chen, Qian Wang, Keliang Liang, Wenqiu Guo, and Liying Zhou. "A Novel Stacking Heterogeneous Ensemble Model with Hybrid Wrapper-Based Feature Selection for Reservoir Productivity Predictions." Complexity 2021 (January 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6675638.
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Acid fracturing is the most important stimulation method in the carbonate reservoir. Due to the high cost and high risk of acid fracturing, it is necessary to predict the reservoir productivity before acid fracturing, which can provide support to optimize the parameters of acid fracturing. However, the productivity of a single well is affected by various construction parameters and geological conditions. Overfitting can occur when performing productivity prediction tasks on the high-dimension, small-sized reservoir, and acid fracturing dataset. Therefore, this study developed a stacking heterogeneous ensemble model with a hybrid wrapper-based feature selection strategy to forecast reservoir productivity, resolve the overfitting problem, and improve productivity prediction. Compared to other baseline models, the proposed model was found to have the best predictive performances on the test set and effectively deal with the overfitting. The results proved that the hybrid wrapper-based feature selection strategy introduced in this study reduced data acquisition costs and improved model comprehensibility without reducing model performance.
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Zhou, Changlin, Lang Zhou, Fei Liu, Weihua Chen, Qian Wang, Keliang Liang, Wenqiu Guo, and Liying Zhou. "A Novel Stacking Heterogeneous Ensemble Model with Hybrid Wrapper-Based Feature Selection for Reservoir Productivity Predictions." Complexity 2021 (January 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6675638.
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Acid fracturing is the most important stimulation method in the carbonate reservoir. Due to the high cost and high risk of acid fracturing, it is necessary to predict the reservoir productivity before acid fracturing, which can provide support to optimize the parameters of acid fracturing. However, the productivity of a single well is affected by various construction parameters and geological conditions. Overfitting can occur when performing productivity prediction tasks on the high-dimension, small-sized reservoir, and acid fracturing dataset. Therefore, this study developed a stacking heterogeneous ensemble model with a hybrid wrapper-based feature selection strategy to forecast reservoir productivity, resolve the overfitting problem, and improve productivity prediction. Compared to other baseline models, the proposed model was found to have the best predictive performances on the test set and effectively deal with the overfitting. The results proved that the hybrid wrapper-based feature selection strategy introduced in this study reduced data acquisition costs and improved model comprehensibility without reducing model performance.
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Shrestha, Jagat Kumar. "Impact of Road Cuts in Slope Stability in Hilly Regions of Nepal." Journal of Advanced College of Engineering and Management 6 (July 6, 2021): 43–55. http://dx.doi.org/10.3126/jacem.v6i0.38289.
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This paper reviews the geological and engineering aspects of rural road construction in the hilly areas of Nepal. The general background in geological, climatic and geographical setting is briefly presented in reference to the five-zone Himalayan model for the Nepal Himalayas. Then, alignment selection of rural roads is discussed in the context of the five zone mountain model. The impact of road cross section design and construction on mountain slopes has been studied. The cut width is a key geometric design parameter that has a significant impact on slope stability and volume of excavation. The choice of cut width in cross-section is reviewed and appropriate cut width in cross-section is recommended in terrain slopes to minimize slope failures and volume of excavation.
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Wu, Yanhui, Wei Wang, Guowei Zhu, and Peng Wang. "Application of seismic multiattribute machine learning to determine coal strata thickness." Journal of Geophysics and Engineering 18, no. 6 (December 2021): 834–44. http://dx.doi.org/10.1093/jge/gxab054.
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Abstract The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas.
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Kopytov, Aleksandr, and Vladimir Pershin. "Development of the Digital Model for the Selection of Mine Working Support in Complex Mining and Geological Conditions." E3S Web of Conferences 134 (2019): 01009. http://dx.doi.org/10.1051/e3sconf/201913401009.
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The methodological basis for the development of a multi-factor digital model for selecting efficient and safe mine working support for given mining and geological conditions on the basis of the assessment of rock mass stability conditions depending on its fracturing and stress state when mining iron ore deposits of Gornaya Shoria is presented. The need to solve fundamentally new problems related to the application of various types of support systems is due to the changeover of mines to ore mining at great depths and classifying deposits as liable to rock-bumps. It is a very important factor, because a decrease in the stability of workings is associated with a high level of stresses in the deep levels being worked out, increased by bearing pressure, which ultimately leads to an increase in the cost of maintaining them. The developed multi-factor digital model “EvrazrudaKrep”, which is based on the results of the analysis of guidelines and instructions of the institutes such as All-Russian Research Institute of Mining Geomechanics and Survey, East Research Institute of Ore Mining, Mining Institute of Siberian Branch of Russian Academy of Science, T.F. Gorbachev Kuzbass State Technical University, as well as the mine working support experience in Russia and abroad, allows to quickly solve the problem of choosing a support and improving mining safety in complex mining and geological conditions.
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Lei, Tianwang, Yao Lu, Chong Zhang, Jing Wang, and Qi Zhou. "Research on the Application of GIS Technology Combined with RBFNN-GA Algorithm in the Delineation of Geological Hazard Prone Areas." Computational Intelligence and Neuroscience 2021 (December 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/2677453.
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With the rapid development of the economy and society, geological disasters such as landslides, collapses, and mudslides have shown an intensifying trend, seriously endangering the safety of people’s lives and property, and affecting the sustainable development of the economy and society. Aiming at the problems of merging different data layers and determining the weighting of data stacking in the statistical analysis model based on GIS technology in the evaluation of the risk of geological disasters, this study proposes a logistic regression model combined with the RBFNN-GA algorithm, that is, the determination of the occurrence of geological disasters. The fusion coefficient (CF value) with the RBFNN-GA algorithm model, and with the help of SPSS statistical analysis software, solves the problem of factor selection, heterogeneous data merging, and weighting of each data layer in the risk assessment. In the experimental stage, this study adopts the method of geological hazard certainty coefficients to carry out the sensitivity analysis of the geological hazards in the study area. Using homogeneous grid division, the spatial quantitative evaluation of the risk of geological disasters is realized, and at the same time, the results of the spatial quantitative evaluation of the risk of geological disasters are tested according to the latest landslide points in the region. The existing classification mainly depends on the acquisition of land use/cover information or the processing method of the acquired information, but the existing information acquisition will be limited by time, space, and spectral resolution. The results show that the number of landslide points per unit area in the extremely unstable zone and the unstable zone is 0.0395 points/km2 and 0.0251 points/km2, respectively, which is much higher than 0.0038 points/km2 in the stable zone, indicating the evaluation results and actual landslide conditions.
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Kipczak, Piotr, Krzysztof Władzielczyk, and Rafał Dudek. "Modified calculation model for loads of bearing systems for tricone roller bits." New Trends in Production Engineering 2, no. 1 (October 1, 2019): 214–22. http://dx.doi.org/10.2478/ntpe-2019-0022.
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Abstract The article presents a modified calculation model of loads of cone bearing systems in tricone roller bits. The presented model includes the actual nature of the loads of a tricone roller bit and the effect of these loads on the construction of individual elements of the cone bearing systems. The presented calculation model was created in order to develop a computer program that allows the calculation and verification of structural parameters of cone bearing systems even at the stage of their initial design. It will allow an optimal selection of these parameters to geological properties of drilled rocks.
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Panischev, Oleg Yuryevich, Ekaterina Nikolaevna Ahmedshina, Dina Vladimirovna Kataseva, Igor Vyacheslavovich Anikin, Alexey Sergeevich Katasev, Amir Muratovich Akhmetvaleev, and Arslan Valerievich Nasybullin. "Neurofuzzy Model of Formation of Knowledge Bases for Selection of Geological and Technical Measures in Oil Fields." International Journal of Engineering Research and Technology 13, no. 11 (November 30, 2020): 3589. http://dx.doi.org/10.37624/ijert/13.11.2020.3589-3595.
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Barongo, J. O. "Selection of an appropriate model for the interpretation of time-domain airborne electromagnetic data for geological mapping." Exploration Geophysics 29, no. 1-2 (March 1998): 107–10. http://dx.doi.org/10.1071/eg998107.
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Lamont, Matthew, Troy Thompson, and Carlo Bevilacqua. "Drilling success as a result of probabilistic lithology and fluid prediction—a case study in the Carnarvon Basin, WA." APPEA Journal 48, no. 1 (2008): 31. http://dx.doi.org/10.1071/aj07004.
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The aim of quantitative interpretation (QI) is to predict lithology and fluid content away from the well bore. This process should make use of all available data, not well and seismic data in isolation. Geological insight contributes to the selection of meaningful seismic attributes and the derivation of valid inversion products. Uncertainty must be taken into account at all stages to permit risk assessment and foster confidence in the predictions. The use of the Bayesian framework enables prior knowledge, such as a geological model, to be incorporated into a probabilistic prediction, which captures uncertainty and quantifies risk. Nostradamus is a fluid and lithology prediction toolkit that forms part of a comprehensive QI workflow. It utilises a Bayesian classification scheme to make quantitative predictions based upon inverted seismic data and depth-dependent, stochastic rock physics models. The process generates lithology and fluid probability volumes. All available information is combined using geological knowledge to create a realistic pre-drill model. Separately, stochastically modelled multidimensional crossplots, which account for the uncertainty in the rock and fluid properties (based on petrophysical analyses of well data), are used to build probability density functions such as acoustic impedance (AI) vs Vp/Vs and LambdaRho vs MuRho. These are then compared to crossplots of equivalent inverted data to make predictions and quantitatively update the geological model. Individual probability volumes as well as a most-likely lithology and fluid volume are generated. This paper presents a case study in the Carnarvon Basin that successfully predicts fluids and lithologies away from well control in a way that effectively quantifies risk and reserves. Two of the three successful gas exploration wells were drilled close to dry holes.
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Moyano Nieto, Ismael Enrique, Renato Cordani, Lorena Paola Cárdenas Espinosa, Norma Marcela Lara Martínez, Oscar Eduardo Rojas Sarmiento, Manuel Fernando Puentes Torres, Diana Lorena Ospina Montes, Andrés Felipe Salamanca Saavedra, and Gloria Prieto Rincón. "Interpretation of geophysical anomalies for mineral resource potential evaluation in Colombia: Examples from the northern Andes and Amazonian regions." Boletín Geológico, no. 46 (June 30, 2020): 5–22. http://dx.doi.org/10.32685/0120-1425/boletingeo.46.2020.514.
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This paper focuses on presentation of the methodology used by geophysicists at the Servicio Geológico Colombiano (SGC) for the processing, anomaly selection and interpretation of airborne magnetometry and gamma spectrometry data. Three (3) selected magnetic anomalies from different geological settings (Andes Cordillera, San Lucas Range and Amazon region) are presented as examples. 3D magnetic vector inversion (MVI) modeling of each of the selected magnetic anomalies shows magnetic sources less than 100 m deep or exposed with sizes from 2.5 to 6 km. The magnetic data interpretation also allows the identification of linear features that could represent structural control for fluid migration and/or ore emplacement. Additionally, the integration of the geophysical data with other geoscientific information (geologic, metallogenic and geochemical data) leads to the proposition of an exploration model for each anomaly: intrusion-related/VMS deposits for the Andes, porphyry/intrusion-related/epithermal deposits for San Lucas and carbonatite/kimberlite for Amazonas. The methodology used and examples presented illustrate the potential of SGC airborne geophysical data for mineral resource evaluation and as input for the design of fieldwork for geological, geophysical, geochemical and metallogenic characterization of an area of interest.
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Sun, Qi, Jing Yang, Li Yan Sun, and Xiu Long Dong. "The Technology Limits for Stratified Injection of Polymer Flooding." Advanced Materials Research 1094 (March 2015): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amr.1094.433.
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At present the development of Daqing Oilfield has entered the water pick-up period, and the polymer separate injection technology for the injection well is urgent needed. However, the difficulty of selecting well and lever for the separate injection of the injection well is relatively large due to the complexity of the Class II reservoir of geological conditions. So for The limits of technology of the geological features, the limits of technology injection of stratified polymer injection for the Class II reservoir provides a scientific basis for the development of oil fields.In this paper, taking Daqing Oilfield Sabei Development Zone as example, establish the mathematical model of polymer flooding. Determine the well and layer selection principles of layered polymer injection wells in the ClassIIreservoir timing. According to the current development situation, give the decrease in water content and the improvement value in recovery under a given measure.Through this paper, we have got production effect of layered polymer injection in the Class II reservoir of Sabei area and given quantified layered polymer injection technology limits.
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Landa, Jiří, and Milan Hokr. "Contaminant Transport from a Deep Geological Repository: Lumped Parameters Derived from a 3D Hydrogeological Model." Energies 15, no. 18 (September 9, 2022): 6602. http://dx.doi.org/10.3390/en15186602.
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A model of contaminant transport from a repository to the biosphere is one of the major needs in the safety assessment of the geological disposal of spent nuclear fuel. This work deals with the development of a procedure that obtained characteristic data from the transport path by postprocessing the results of the 3D flow and transport models, according to the repository concept for the Czech Republic. Postprocessing was used to map the entire transport pathway, which included the smallest tracer flows; therefore, it is called the “integral method”. The results are the characteristics of the storage system, such as: transport path length, flow time, total dilution, groundwater flow, longitudinal dispersivity, porosity, etc. These acquired characteristics can be used directly in safety analyses or to narrow the selection of candidate sites. Furthermore, these parameters were used to set up a model with lumped parameters (in this case, created in the GoldSim SW environment). Even only one “Pipe” component, after being properly set up, shows almost identical results to the entire 3D model. Based on the results of the 3D model, it is possible to set up a lumped parameter model that accurately simulates the transport path and can perform further calculations of a larger number of contaminants in repeated runs, e.g., with stochastic input data, which would be very laborious (or not possible at all) with the 3D model.
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Tan, Zhongsheng, Zonglin Li, Zhenliang Zhou, Haixiang Lai, Yifeng Jiao, Fengyuan Li, and Liming Wang. "Research on an Evaluation Method for the Adaptability of TBM Tunnelling." Applied Sciences 12, no. 9 (April 30, 2022): 4590. http://dx.doi.org/10.3390/app12094590.
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When a TBM carries out tunnelling in complex strata, there is often low tunnelling efficiency and an increase in tunnelling costs due to the improper selection of tunnelling parameters, the wrong estimation of geological conditions, or adverse geology, so it is necessary to evaluate the tunnelling adaptability of TBM construction. In this paper, based on hydraulic engineering in Xinjiang, 11 evaluation indexes of TBM tunnelling adaptability are determined by comprehensively considering the influence of tunnelling parameters, geological conditions, and adverse geological factors on TBM tunnelling adaptability. After that, the membership function of each evaluation index is determined by referring to the existing research results and fuzzy mathematics method, and the weight of each evaluation index is determined and adjusted by the analytic hierarchy process (AHP)–entropy weight (EW) method. Finally, the adaptability evaluation method and evaluation model of TBM tunnelling are put forward. The TBM tunnelling adaptability evaluation model proposed in this paper is verified by relying on the actual situation of three interval tunnels in the project, and good effects are obtained. This study can provide a reference for the evaluation of TBM tunnelling adaptability in similar strata.
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Dautov, A. M. "The role of seismic exploration and its impact on geology and field development." Kazakhstan journal for oil & gas industry 2, no. 4 (December 15, 2020): 26–35. http://dx.doi.org/10.54859/kjogi95588.
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This paper presents a brief study of the presence and study of seismic material to study the geological structure of deposits and the natural state of hydrocarbon reserves within the Caspian, Ustyurt-Bachinsky and Mangyshlak basin. The research is based on the concept of studying hydrocarbon deposits by type by foreign companies, based on the structural and dynamic interpretation of seismic material on the example of Kazakhstan fields. Characteristic indicators of the concept are the selection of sedimentation conditions and the beginning of forecasting their distribution in areas where there is no well data, for the selection of sedimentation bodies, the presence of accumulation zones, as well as modern migration channels. The importance of studying seismic material with the help of dynamic interpretation of oil and gas fields in the Caspian basin was clearly demonstrated by earlier analyses of seismic material. Calculations, proofs, and experimental studies made it possible, based on the created conceptual model of the structure of hydrocarbon deposits, to justify the prospects for oil and gas potential and the feasibility of conducting geological exploration within these areas.
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Lim, Seojin, Changhyup Park, Jaejun Kim, and Ilsik Jang. "Integrated Data Assimilation and Distance-Based Model Selection with Ensemble Kalman Filter for Characterization of Uncertain Geological Scenarios." Natural Resources Research 29, no. 2 (May 9, 2019): 1063–85. http://dx.doi.org/10.1007/s11053-019-09489-2.
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GRYSHCHUK, Pavlo. "INVERSION OF GRAVITY DATA USING A GENETIC ALGORITHM." Ukrainian Geologist, no. 1-2(44-45) (June 30, 2021): 71–77. http://dx.doi.org/10.53087/ug.2021.1-2(44-45).238889.
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The genetic algorithm for the selection of gravitational sources is considered. In the basis of the approach, the principle of selection of genes from fathers and mutations was laid down, which was adapted to form geological structures. For a two-dimensional grid model, the designation of apparent density in the blocks is selected by a choice of models of two (parental) variants, in which gravitational initial and calculated anomalies coincide better. In the quality of the object function is used a middle gradient norm of gravity fields. Generation of new models for effective density in blocks is released randomly. Theoretical models were built up for one body with one and two values of apparent densities. The theoretical sections with four layers were considered. The fitting of the model was carried out under the condition that the value of the effective density was known or a certain range was set. Each block was rectangular in shape with a square section in the plane of the gravity data profile and a limited lateral elongation. Comparison of the output and calculated anomalies of the gravitational acceleration was carried out using the average norm and the percentage error. The absence of jumps in the objective function graph ensured that an accurate model was determined. The correct geometry of a body with a homogeneous apparent density was determined at a fixed value of the effective density for four layers. The model with two values of density had some errors in determining the geometry of the bodies. The genetic algorithm, based on an evolutionary approach to certain physical parameters of blocks, performs the fitting of a gravity model rather quickly and effectively. The main factors affecting the accuracy of geometry are apparent density data. The implemented approach allows one to estimate the cross section by the grid distribution of the effective density. The development is applied for a two-dimensional interpretation of the gravity anomaly over an oil and gas field. The resulting interpretation of the shape of the anticlinal structure is consistent with geological data.
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Yan, Xiao Ming, Zi Long Zhou, and Xi Bing Li. "Three-Dimensional Visual Modeling Technology and Application of Open Pit Mining Boundary." Advanced Materials Research 524-527 (May 2012): 790–93. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.790.
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With mining depth and state-wide expansion in open pit mining, it is necessary to bulid a three-dimensional visual model of open pit mining boundary, which can be used in the analysis of pit slope stability, engineering decisions, geological analysis and production planning. In this paper, a three-dimensionla visual model reflecting the complex formation load and terrain conditions was built by collecting original open pit design and geological data. With this model, stability analysis of open pit can be obtained and theoretical basis for selection of design can be provided. In the specific prcess of modelling, the original information in the existing CAD mining topographic maps were be used fully and the mine topographic maps was imported into Surpac mining software. Surface digital terrain model can be obtained elevation assignmented by corrction processing of CAD linears and vector processing of measring point data. On this basis, by using MIDAS software and considering the requirements of the scope of computational space, a three-dimensional model can be obtained through Boolean cut operations. With this model, the real surface shape of open pit mining boundary can be reflected.
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Zimnyukov, Vladimir, Marina Zborovskaya, Vasiliy Fartukov, and Anton Zaitsev. "Experimental justification of the stability of the floating unit." E3S Web of Conferences 264 (2021): 01054. http://dx.doi.org/10.1051/e3sconf/202126401054.
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One of the main tasks arising when installing a floating hydroelectric power unit on a foundation without preliminary excavation is a thorough justification of the shear stability and bearing capacity of the "floating hydroelectric power unit - foundation" system on a complex geological massif. Failure to take into account these factors can lead to serious consequences during the landing of the structure in the target and further operation. It should be emphasized that this problem still includes a number of difficulties and does not always allow obtaining exact solutions in a volumetric setting. Based on the selection of a wide range of model materials, bases of various capacities were modelled for four models. In this case, the shear real characteristics of alluvial soils and their change after reinforcing cementation were taken into account. The studies were carried out on 4 models under static loads with bringing them to destruction. The models reproduced the real geological conditions at the base of the block, simulated deformation, and shear characteristics. Indicator diagrams of displacements, damage patterns, and generalized safety factors for bearing capacity were obtained. Model tests have shown that reinforcing cementation reduces not only the values of horizontal and vertical displacements of structures but also leads to a significant increase in the safety factor.
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Nikraftar, Z., S. Rajabi-Kiasari, and S. T. Seydi. "GENETIC ALGORITHM BASED FEATURE SELECTION FOR LANDSLIDE SUSCEPTIBILITY MAPPING IN NORTHERN IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 821–25. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-821-2019.
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Abstract. Recognizing where landslides are most likely to occur is crucial for land use planning and decision-making especially in the mountainous areas. A significant portion of northern Iran (NI) is prone to landslides due to its climatology, geological and topographical characteristics. The main objective of this study is to produce landslide susceptibility maps in NI applying three machine learning algorithms such as K-nearest neighbors (KNN), Support Vector Machines (SVM) and Random Forest (RF). Out of the total number of 1334 landslides identified in the study area, 894 (≈67%) locations were used for the landslide susceptibility maps, while the remaining 440 (≈33%) cases were utilized for the model validation. 21 landslide triggering factors including topographical, hydrological, lithological and Land cover types were extracted from the spatial database using SAGA (System for Automated Geoscientific Analyses), ArcGIS software and satellite images. Furthermore, a genetic algorithm was employed to select the most important informative features. Then, landslide susceptibility was analyzed by assessing the environmental feasibility of influential factors. The obtained results indicate that the RF model with the overall accuracy (OA) of 90.01% depicted a better performance than SVM (OA = 81.06%) and KNN (OA = 83.05%) models. The produced susceptibility maps can be productively practical for upcoming land use planning in NI.
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Malvić, Tomislav, Marija Bošnjak, Josipa Velić, Jasenka Sremac, Josip Ivšinović, Maria Alzira Pimenta Dinis, and Uroš Barudžija. "Recent Advances in Geomathematics in Croatia: Examples from Subsurface Geological Mapping and Biostatistics." Geosciences 10, no. 5 (May 15, 2020): 188. http://dx.doi.org/10.3390/geosciences10050188.
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Geomathematics is extremely important in geosciences, particularly in the geology. The key for any geomathematical analysis is the definition of a typical model to be applied for further prognosis, either through deterministic or stochastic approaches. The selection of the appropriate procedure is presented in this paper. Two different geomathematical subfield datasets were used in subsurface geological mapping and palaeontology and different biostatistics applications, representing important geomathematical subfields in the Croatian geology. The different subsurface interpolation methods tested, validated and recommended for application were used to obtain the best possible outcome in reservoir modelling, in the cases with small datasets. Cross-validation may be chosen as the main selection criteria, applied to the Croatian part of the Pannonian Basin System (CPBS). Recent advances in biostatistics applied in palaeontology and case studies from Croatia are also presented, where biometric studies are of significant importance in fossil biota. Data, methods and problems in geosciences are vast subjects, and address a wide spectrum of fundamental science. Because geology includes subsurface and surface geology, and very different datasets regarding variable and number of data, we have chosen here two representative case study groups with original samples from Northern Croatia. Subsurface mapping has been presented on limited petrophysical datasets from the Northern Croatian, Miocene, hydrocarbon reservoirs. Biostatistics have been presented on very different samples, allowing us to achieve paleoenvironmental reconstructions of the size of relevant fossils, such as dinosaurs or other species and their paleoenvironments. All examples highlight examples of the valuable application of geomathematical tools in geology. The results, cautiously validated and correlated with other, non-numerical (indicator, categorical) geological knowledge, are of enormous assistance in creating better geological models.
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GORDON, RICHARD, and JOHN E. TYSON. "FATAL SEXUALLY TRANSMITTED DISEASES (FSTDs), SUCH AS AIDS, SELECT FOR THE EVOLUTION OF MONOGAMY AND PROVIDE A MODEL FOR BACKGROUND EXTINCTION." Journal of Biological Systems 01, no. 04 (December 1993): 425–50. http://dx.doi.org/10.1142/s0218339093000252.
Full textAbstract:
Monogamous behavior may have a genetic component, in which case it can be subject to natural selection. The mechanism of selection may be the reduced fertility that correlates with STDs. AIDS is a fatal STD (FSTD) capable (without intervention) of severely reducing the human population. We show how, for any mating species, multiple FSTDs (statistical “runs”) occurring within the characteristic time for population recovery, drive that species exponentially towards extinction. Runs of FSTDs occur on geological time scales, providing a general, quantitative model for background extinction rates, and a simpler alternative to the Red Queen hypothesis. It has been difficult to get people to change their risk behavior even when they have full knowledge of how HIV is spread. If we come to understand monogamy (and perhaps polygamy) as heritable traits, then new approaches for slowing the HIV epidemic become apparent.
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Tewari, Saurabh, Umakant Dhar Dwivedi, and Susham Biswas. "Intelligent Drilling of Oil and Gas Wells Using Response Surface Methodology and Artificial Bee Colony." Sustainability 13, no. 4 (February 4, 2021): 1664. http://dx.doi.org/10.3390/su13041664.
Full textAbstract:
The oil and gas industry plays a vital role in meeting the ever-growing energy demand of the human race needed for its sustainable existence. Newer unconventional wells are drilled for the extraction of hydrocarbons that requires advanced innovations to encounter the challenges associated with the drilling operations. The type of drill bits utilized in any drilling operation has an economical influence on the overall drilling operation. The selection of suitable drill bits is a challenging task for driller while planning for new wells. Usually, when it comes to deciding the drill bit type, generally, the data of previously drilled wells present in similar geological formation are analyzed manually, making it subjective, erroneous, and time consuming. Therefore, the main objective of this study was to propose an automatic data-driven bit type selection method for drilling the target formation based on the Optimum Penetration Rate (ROP). Response Surface Methodology (RSM) and Artificial Bee Colony (ABC) have been utilized to develop a new data-driven modeling approach for the selection of optimum bit type. Data from three nearby Norwegian wells have been utilized for the testing of the proposed approach. RSM has been implemented to generate the objective function for ROP due to its strong data-fitting characteristic, while ABC has been utilized to locate the global optimal value of ROP. The proposed model has been generated with a 95% confidence level and compared with the existing model of Artificial Neural Network and Genetic Algorithm. The proposed approach can also be applied over any other geological field to automate the drill bit selection, which can minimize human error and drilling cost. The United Nations Development Programme also promotes innovations that are economical for industrial sectors and human sustainability.