Resource information analysis
| Numbering Code |
U-ENG23 33187 LJ77 U-ENG23 33187 LJ58 U-ENG23 33187 LJ10 |
Year/Term | 2022 ・ First semester | |
|---|---|---|---|---|
| Number of Credits | 2 | Course Type | Lecture | |
| Target Year | Target Student | |||
| Language | Japanese | Day/Period | Mon.4 | |
| Instructor name |
KOIKE KATSUAKI (Graduate School of Engineering Professor) HAYASHI TAMETO (Graduate School of Engineering Professor) KASHIWAYA KOUKI (Graduate School of Engineering Associate Professor) TAKEKAWA JUNICHI (Graduate School of Engineering Associate Professor) ISHITSUKA KAZUYA (Graduate School of Engineering Assistant Professor) |
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| Outline and Purpose of the Course | In the process of geological survey and exploration related to mineral and energy resources, various information, such as lithofacies and minerals, rock physical properties and chemical composition, mechanical properties, and so forth are obtained in large quantities. Lectures will be given on modeling the spatial distribution of resources from this information and accurately evaluating ore reserves. In addition, the information analysis method necessary for designing and planning resource development by land mining, underground digging, and underwater drilling will be covered. Additionally, the geological properties, such as chemical component concentration and groundwater level in the fluid, and the response from underground regarding the input electromagnetic waves in the electromagnetic wave survey change with time. Lectures will be given on analysis methods for such data that change according to time and space, and understand the application to underground structure and the Earth’s crust environment evaluation. The contents are composed of four items: geological information analysis, time series data analysis, spatio-temporal data analysis, and integrated analysis of mechanical data. The purpose of the class is to understand the basics of these analytical methods and to acquire knowledge that can be applied to the field of resource engineering. | |||
| Course Goals | Learning the basics of the geological map creation method required for resource evaluation and the spatial distribution estimation method of geological data, the rock geochemical analysis method and mineral analysis method, the time series data analysis method, and the dynamic data analysis method for resource development. Additionally, being able to understand how they can be applied to the field of resource engineering. | |||
| Schedule and Contents |
Geological information analysis (5 times): Lectures will be given on the quality distribution model by geostatistics, the calculation method of ore reserves, the evaluation method of resource existence by data integration using Bayesian statistics, and the geological map creation and interpretation method of geological structure as a basis for resource distribution modeling. In addition, in order to clarify chemical anomalies of rock-forming ore deposits, lectures will be given on the geochemical data analysis method of rocks and Earth crust fluids, the chemical composition analysis method, and the crystal structure of minerals. Time series data analysis (2 times): Lectures will be given on autoregressive and multivariate regression models, which are representative analysis methods, in order to find inherent regularity from time series data and to enable future prediction. Spatio-temporal data analysis (2 times): Lectures will be given on principal component analysis and independent component analysis as unsupervised classification methods of spatio-temporal data. In addition, lectures will be given on analysis methods of spatio-temporal data using geostatistics, and will deepen understanding of how to model and visualize geological and environmental data that varies according to time and space. Integrated analytics of mechanical data (3 times): Lectures will be given on mechanical problems related to the development of underground resources and undersea resources, analysis methods of mechanical data and physical property data, the integration method of core data and logging data, the evaluation method of wide stress fields, and a world stress map, to utilize dynamic data to safely and efficiently develop mineral and energy resources. Additionally, many practical examples will be covered. Application of Artificial Neural Network (ANN) (2 times): Lectures will be given on the fundamentals of Machine Learning including ANN with practical applications to mineral and energy resource assessment and exploration. Feedback (1 time): Supplementary explanation of the items of insufficient understanding regarding the content of the above lectures |
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| Evaluation Methods and Policy | Class attendance and the results of reports will be evaluated together. | |||
| Course Requirements | It is assumed that students have taken the third year courses of Geological Engineering and Rock Engineering, and the second year course of Basic Mathematics of Geological Engineering | |||
| Study outside of Class (preparation and review) | Although preparation is not particularly necessary, students should spend enough time preparing the reports as a review and deepening their understanding. | |||
| Textbooks | Textbooks/References | Others; prints will be distributed as appropriate. | ||
| Related URL | ||||
