The Center for Advanced Subsurface Earth Resource Models (CASERM) seeks to transform the way geoscience data is used in the exploration and mining industry sector. Research at CASERM is focused on challenges in the development of 3D models for subsurface mineral resources, particularly as these models integrate diverse geoscience data to inform decision-making and minimize geological risk, beginning with locating and mining subsurface resources and continuing through mine closure and environmental remediation. 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 CASERM members. Addressing the critical industry need for trained and prepared employees by educating future researchers, engineers, and scientists. Achieving this broad vision requires cross-disciplinary collaborations, including combining expertise in the traditional geoscience disciplines of mineralogy, geochemistry, petrology, economic geology, and geophysics with those in spatial statistics, inverse theory, numerical methods, computer science and high-performance computing, seismic imaging and inversion, tomographic imaging, and petrophysics.
Research Areas
CASERM conducts research in these topic areas: Development of novel instrumentation, analysis, and interpretation methods for enhanced characterization of rock properties; integration, scaling, and inversion of geological, petrophysical, and geophysical data types of dissimilar spatial resolution and distribution to identify and characterize subsurface resources; development of machine learning methods to predict subsurface properties, quantify uncertainty, and de-risk decision-making; and development of graphical and exploratory data analysis solutions and visualization tools for 3D subsurface features.
Research focuses on:
Integrating sequential simulation methods with visual ensemble analytics for applications in the mining sector
Mineral resource exploration results in large quantities of spatially referenced, multi-variate data sets. This project implements recent advances in Bayesian Visual Analytics (BaVA) to discover common attributes (geochemistry or mineral abundance) among features of interest within these multi-variate data sets. The BaVA framework combines the computational efficiency of machine learning with human cognition and expertise to rapidly identify spatial and parametric relationships that may remain hidden in traditional univariate or bivariate geostatistical analyses. Through collaborative case study analyses with CASERM member-partners, this project introduces BaVA methods for data visualization and analysis in the mining sector.
Distal signatures and vectors of hydrothermal systems in carbonates
Many hydrothermal deposits can be hosted in carbonate-rich wall rocks, including porphyry deposits and skarns, or have carbonates in the surrounding stratigraphy. Beyond the orebodies or massive replacement bodies the hydrothermal footprint continues in the carbonates, by spent fluids and producing weak signals, particularly along faults. This research defines the distal signatures of mineralizing fluids in carbonates, including mineralogical and textural signatures, geochemical signals in whole rock, vein coatings and carbonate minerals, stable isotopes, and fluorescence and cathodoluminescence features of carbonate minerals.
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. 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 multiyear project will involve identifying diagnostic features in hyperspectral spectra to identify the mineralogy using traditional automated mineralogy data for assessment; and finding relationships between the mineralogy derived from hyperspectral core scanning and petrophysical properties. The quantitative mineralogical and rock physical data obtained could then be used to build a 3D block model informing the cost of mining.
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.
Seismic and radar high-resolution 3D mapping of fractures, geologic structure, and petrology beyond the mined volume
CASERM is developing high-resolution and cost-effective geophysical imaging methods that can be applied within the mine. Targets are mine safety and ground control on the daily to monthly scale; mine planning on the monthly to annual scale and longer-term mine expansion; and temporal monitoring of permeability pathways for groundwater flow, such as for hydrothermal energy, wastewater or carbon dioxide disposal, and in situ leach mining, a mining process used to recover minerals through boreholes drilled into a deposit.
Facilities & Resources
Partner Organizations
Abbreviation |
CASERM
|
Country |
United States
|
Region |
Americas
|
Primary Language |
English
|
Evidence of Intl Collaboration? |
|
Industry engagement required? |
Associated Funding Agencies |
Contact Name |
Thomas Monecke
|
Contact Title |
Center Director
|
Contact E-Mail |
tmonecke@mines.edu
|
Website |
|
General E-mail |
|
Phone |
|
Address |
The Center for Advanced Subsurface Earth Resource Models (CASERM) seeks to transform the way geoscience data is used in the exploration and mining industry sector. Research at CASERM is focused on challenges in the development of 3D models for subsurface mineral resources, particularly as these models integrate diverse geoscience data to inform decision-making and minimize geological risk, beginning with locating and mining subsurface resources and continuing through mine closure and environmental remediation. 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 CASERM members. Addressing the critical industry need for trained and prepared employees by educating future researchers, engineers, and scientists. Achieving this broad vision requires cross-disciplinary collaborations, including combining expertise in the traditional geoscience disciplines of mineralogy, geochemistry, petrology, economic geology, and geophysics with those in spatial statistics, inverse theory, numerical methods, computer science and high-performance computing, seismic imaging and inversion, tomographic imaging, and petrophysics.
Abbreviation |
CASERM
|
Country |
United States
|
Region |
Americas
|
Primary Language |
English
|
Evidence of Intl Collaboration? |
|
Industry engagement required? |
Associated Funding Agencies |
Contact Name |
Thomas Monecke
|
Contact Title |
Center Director
|
Contact E-Mail |
tmonecke@mines.edu
|
Website |
|
General E-mail |
|
Phone |
|
Address |
Research Areas
CASERM conducts research in these topic areas: Development of novel instrumentation, analysis, and interpretation methods for enhanced characterization of rock properties; integration, scaling, and inversion of geological, petrophysical, and geophysical data types of dissimilar spatial resolution and distribution to identify and characterize subsurface resources; development of machine learning methods to predict subsurface properties, quantify uncertainty, and de-risk decision-making; and development of graphical and exploratory data analysis solutions and visualization tools for 3D subsurface features.
Research focuses on:
Integrating sequential simulation methods with visual ensemble analytics for applications in the mining sector
Mineral resource exploration results in large quantities of spatially referenced, multi-variate data sets. This project implements recent advances in Bayesian Visual Analytics (BaVA) to discover common attributes (geochemistry or mineral abundance) among features of interest within these multi-variate data sets. The BaVA framework combines the computational efficiency of machine learning with human cognition and expertise to rapidly identify spatial and parametric relationships that may remain hidden in traditional univariate or bivariate geostatistical analyses. Through collaborative case study analyses with CASERM member-partners, this project introduces BaVA methods for data visualization and analysis in the mining sector.
Distal signatures and vectors of hydrothermal systems in carbonates
Many hydrothermal deposits can be hosted in carbonate-rich wall rocks, including porphyry deposits and skarns, or have carbonates in the surrounding stratigraphy. Beyond the orebodies or massive replacement bodies the hydrothermal footprint continues in the carbonates, by spent fluids and producing weak signals, particularly along faults. This research defines the distal signatures of mineralizing fluids in carbonates, including mineralogical and textural signatures, geochemical signals in whole rock, vein coatings and carbonate minerals, stable isotopes, and fluorescence and cathodoluminescence features of carbonate minerals.
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. 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 multiyear project will involve identifying diagnostic features in hyperspectral spectra to identify the mineralogy using traditional automated mineralogy data for assessment; and finding relationships between the mineralogy derived from hyperspectral core scanning and petrophysical properties. The quantitative mineralogical and rock physical data obtained could then be used to build a 3D block model informing the cost of mining.
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.
Seismic and radar high-resolution 3D mapping of fractures, geologic structure, and petrology beyond the mined volume
CASERM is developing high-resolution and cost-effective geophysical imaging methods that can be applied within the mine. Targets are mine safety and ground control on the daily to monthly scale; mine planning on the monthly to annual scale and longer-term mine expansion; and temporal monitoring of permeability pathways for groundwater flow, such as for hydrothermal energy, wastewater or carbon dioxide disposal, and in situ leach mining, a mining process used to recover minerals through boreholes drilled into a deposit.