Date of Award


Document Type

Open Access Master's Report

Degree Name

Master of Science in Geophysics (MS)

Administrative Home Department

Department of Geological and Mining Engineering and Sciences

Advisor 1


Committee Member 1


Committee Member 2



Rare earth elements (REEs) have gained significant global importance due to their critical role in supporting the transition towards reduced carbon emissions through industrial applications. REEs serve as essential raw materials for various critical components in modern infrastructure, defense systems, and technological advancements. Geochemical and geophysical data are pivotal in assessing the potential of REEs. Geochemical data provide direct insights into the elemental composition of rocks and soils, offering valuable information on the potential presence and dispersion of REEs. However, the complex geological processes that influence the distribution of REEs often exhibit intricate spatial patterns that may not be fully captured by geochemical data alone. Geophysical data, such as gravity and magnetic data, offer indirect but complementary insights into subsurface geological structures and mineral potential. The integration of geochemical, gravity, and magnetic data can aid in identifying exploration targets with increased confidence levels. While each data source individually provides valuable information, their combination allows for the identification of areas where multiple anomalies coincide, indicating a higher likelihood of mineralization. This approach helps reduce exploration uncertainties by prioritizing targets that exhibit consistent characteristics across various datasets, thereby enhancing the chances of discovering economically viable REE reserves.

This study aims to investigate the geochemical anomalies of REEs in Central Upper Michigan by employing geostatistics and fractal analysis to integrate geochemical, gravity, and magnetic data to quantify and map REEs anomalies. Both the heavy and light REEs (HREEs and LREEs) were mapped, integrating with gravity and magnetic data using a multivariate geostatistical method called cokriging. Cokriging utilized the spatial correlation and cross-correlation among these data types to provide more insightful predictions compared to solely relying on the geochemical dataset. Fractal modeling, which has proven to be a powerful tool in geological mapping for anomalous deposits, was utilized in this study. By leveraging the fractal characteristics of mineral deposit dispersion and the related geochemical trends, this approach was able to identify potential exploration zones. The concentration-Area (C-A) log-log plots of the HREEs and LREEs were generated, and their thresholds were subsequently identified using the segmented linear method. The fundamental premise of C-A fractal modeling is based on the observation that mineralization processes frequently result in patterns of element concentrations that exhibit fractal characteristics. These patterns can be analyzed to distinguish between the baseline (typical levels found in the earth's crust) and anomalies (elevated concentrations indicative of mineral deposits). Results from this study clearly show the anomaly distributions of both the HREEs and LREEs across the study area. Combining geochemical information with additional datasets results in a more thorough comprehension of subsurface circumstances, which is essential for precise anomaly mapping. The collaboration of these datasets enables a strong analysis, ultimately leading to a more dependable identification of possible mineral deposits and geological characteristics.