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Center for Advanced Subsurface Earth Resource Models (CASERM)

Colorado School of Mines

Virginia Tech - Virginia Polytechnic Institute and State University

Last Reviewed: 09/10/2018

Center Mission and Rationale

The CASERM is a collaboration among industry, government agencies, and universities with the purpose of:

  • developing fundamental knowledge that transforms the way geoscience data is used to locate and characterize subsurface earth resources and, thus, to enhance exploration success, decrease prospect development time, and reduce overall spending;
  • disseminating this knowledge to Center members; and
  • addressing the critical industry need for trained and prepared employees by educating future researchers, engineers, and scientists.

Research program

Machine Learning in Resource Modeling and Mine Planning

Machine learning (ML) has the potential to revolutionize mining by advancing the technology of lofting in resource modeling and estimation. This approach can tap into the capabilities of ML algorithms to integrate big data, uncover hidden relations, and make predictions. Improved accuracy in predicting orebody shapes and quantifying uncertainties translates into monetary value through increased reserves or avoiding wasted mining operation based on false predictions. The aims of the project are to develop adaptive learning lofting methods using supervised and self-supervised ML algorithms. The method will adaptively integrate multiple geoscientific data with sparse drill hole intersections to construct 3D orebody shapes and quantify the spatial uncertainty of the shapes.

Distal signatures and vectors of hydrothermal systems in carbonates

Many hydrothermal deposits can be hosted in carbonate-rich wall rocks, including porphyry deposits (e.g., Grasberg, Indonesia; Cerro Corona, Peru), and skarns (e.g., Antamina and Uchucchacua, Peru), or have carbonates in the surrounding stratigraphy (e.g., Candelaria, Chile). Beyond the orebodies or massive replacement bodies the hydrothermal footprint continues in the carbonates, by spent fluids and producing weak signals, particularly along faults. The objective of this research is to define the distal signatures of mineralizing fluids in carbonates, including mineralogical and textural signatures, geochemical signals in whole rock, vein coatings and carbonate minerals, C-O isotopes, and fluorescence and cathodoluminescence features of carbonate minerals. Vectors in these parameters towards ores will also be identified. Seemingly similar carbonates are well known to have very different alteration intensity under similar conditions; the project will also develop methods to quantitatively assess carbonate reactivity.

Seismic and radar high-resolution 3D mapping of fractures, geologic structure, and petrology beyond the mined volume

We propose testing and development of high-resolution, cost-effective, and timely geophysical imaging methods that can be applied within the mine. Targets are 1) mine safety and ground control on the daily to monthly scale, 2) mine planning on the monthly to annual scale and longer-term mine expansion by delineation of nearby deposits, and 3) temporal monitoring of permeability pathways for groundwater flow, such as for hydrothermal energy, wastewater or CO2 disposal, and in situ leach mining. Ground-penetrating radar and seismic reflection are the highest-resolution geophysical subsurface imaging methods and complement one another in spatial resolution and depth of imaging. Geophysical images can be integrated with geology by measuring the physical properties of mine rocks.

Increasing the value of hyperspectral data

Knowledge of both deposit mineralogy and the physical and mechanical properties of rock units is critical at many stages of project development from early exploration to mining and remediation. This project aims to use hyperspectral core scanning data to determine the quantitative mineralogy of drill core and to predict rock physical and mechanical properties. This project is a multi-year project that will involve the following main tasks: (1) identify diagnostic features in hyperspectral spectra to identify the mineralogy using traditional automated mineralogy data for assessment, and (2) find relationships between the mineralogy derived from hyperspectral core scanning and petrophysical properties (i.e., density, hardness, abrasivity). The quantitative mineralogical and rock physical data obtained could then be used to build a 3D block model informing the cost of mining (rock blasting and comminution behavior). The project will make use of machine learning (ML) techniques.


Colorado School of Mines

Dept of Geol & Geol Eng, Colorado School of Mines
1516 Illinois St

Golden, Colorado, 80401

United States

Virginia Tech - Virginia Polytechnic Institute and State University

Department of Mathematics Computational Modeling & Data Analytics
570 McBryde, 225 Stanger Street

Blacksburg, Virginia, 24061

United States