Project Info
*ADAPTIVE LEARN I NG AND QUANTIFICATION ALGORITHMS FOR ADVANCES IN GEOLOGICAL EXPLORATION
Hua Wang
huawang@mines.edu
This project will develop innovative machine learning-based approaches for predicting prospective areas of exploration on regional scales and for exploration targeting at district scales. The project aims to (1) implement and develop new machine learning approaches for integrating large-scale exploration data including geology,
geochemistry, and geophysics to identify highly prospective areas for both green field and brown field exploration; (2) devise computational and statistical algorithms to predict and identify high-potential target areas (along with estimated uncertainty associated with this prediction) at the prospecting stage to allow designing an optimal drilling program; and (3) investigate the use of machine learning and spatial statistical techniques in automated interpolation and interpretation of drilling data for generating 3D ore body shapes in resource quantification.
More Information:
Grand Challenge: Provide energy from fusion
Colin T Barnett and Peter M Williams, BW Mining, 2008, Using Geochemistry and Neural Networks to Map Geology under Glacial Cover, Geoscience B.C,.
Barnett, C. T., and P.M. Williams, 2006, Mineral exploration using modern data mining techniquesNormal access, First Break, 24,
David, M., 2012, Geostatistical ore reserve estimate, Elsevier Jacob, J. And C. Prins, 2016, Construction of an expert-opinion-based virtual orebody for a diamondiferous linear beach deposit, J. of Southern African Institute of Mining and Metallurgy, 116,
C. Xu and P.A. Dowd, 2001, Orebody modelling by optimal surface reconstruction, Transactions of the Institution of Mining and Metallurgy Section B-Applied Earth Science, 2001; 110(2):110-120
Malcolm H. HerbertChristopher B. JonesDouglas S. Tudhope, 1995, Three-dimensional reconstruction of geoscientific objects from serial sections, The Visual Computer 11, 343-359
Primary Contacts:
Dr Hua Wang: huawang@mines.edu
Student Preparation
Qualifications
Students are expected to take CSCI 261,262 before taking this project. It
would be good if the student have already taken CSCI 303,358,404,470, but this is
not required.
TIME COMMITMENT (HRS/WK)
6 hours per week
SKILLS/TECHNIQUES GAINED
1. The students will learn the skills to perform data processing and management.
2. The students will be involved my research team to perform research on machine learning and data mining.
3. The students will be involved into scientific paper writing for the results from this project.
4. The students will have chance to work together with our collaborators in industry.
5. The students will gain the fundamental knowledge on mineral exploration, as well as how to use machine learning, as well as computational algorithms, to deal with problems in geophysics analysis. In a word, after the training in this project by successfully completing the assigned research tasks, the student is expected to be ready for pursing a graduate degree in the area of machine learning, data mining, or artificial intelligence, or a broader area of computer science.
MENTORING PLAN
1. One orientation meeting is planned at the beginning of the project, in which the undergraduate students will be introduced to the research team. The project and research culture of the faculty’s research team will be introduced to the undergraduate students in the meeting.
2. Technical seminars within the research team are planned, once per week. In every meeting, the undergraduate students will present a research paper relevant to the project and lead discussions on it with the faculty and the graduate students in the research team.
3. Professional development sessions within the research team are planned, once per week. In every meeting, the faculty or the graduate students in the research team will examine the progress of the project and the recent research results, exchange the ideas with the undergraduate students, and help them develop research skills, including algorithm development, experimental design, scientific results evaluation, paper writing, and so on.
4. A poster session will be conducted at the end of the project in which the results of this project will be presented to the research teams of the faculty, the Computer Science Department, and the collaborators of the faculty.
PREFERRED STUDENT STATUS
Sophomore
Junior
Senior