Introduction

Unconventional Rare-earth elements & Critical minerals (URC) [Yesenchak et al., 2022] are crucial to a growing number of industries worldwide [Balaram, 2019]. Critical Minerals (CM) are minerals used in manufacturing which are essential to economic and national security while being vulnerable to supply disruption through any number of external factors [Yesenchak et al., 2022]. Unconventional CM resources contrast with conventional CM resources in that they are sourced from geologic or byproduct hosts distinctly separate from the mechanisms which establish conventional CM deposits; such unconventional sources include in situ geologic deposits and byproducts of industrial extraction [Yesenchak et al., 2022].

The extraction and recovery of conventional CM is a complex process traditionally involving strip mining, which is both expensive and environmentally destructive [Balaram, 2019]. Recent research has revealed that coaliferous sediments may act as unconventional CM sources containing REE in significant concentrations [Seredin and Dai, 2012]; determining the likelihood and location of these resources in sedimentary basins, however, is both complex and challenging. To address this, a new method of evaluating the potential occurrence of URC resources using a series of validated heuristics has been developed [Creason et al., 2023].

The URC Resource Assessment Tool is used for executing the URC method step 3, the calculation of the potential enrichment score, on extant datasets generated from steps 1 & 2 of the URC method. For description of how to use this tool, see the Usage section.

Statement of need

The URC Resource Assessment Method applies the data analysis methods outlined in Creason et al. [2023], the tool’s companion paper. This tool is a complete application written in Python and built on top of several open-source libraries. No other Python packages are known to contain the combination of geospatial information systems (GIS) and fuzzy logic support required to directly implement the method defined in Creason et al. [2023]. The intended users for this tool are geologists and geospatial scientists who are looking to better understand the mode and spatial distribution of potential URC resource occurrences in sedimentary basins.

There are several ways that the URC Resource Assessment Method can be configured to run, but fundamentally the tool takes in a collection of spatial domains which fall under the Lithological, Structural, and Secondary Alteration categories defined by the Subsurface Trend Analysis (STA) method [Rose et al., 2020]. These domains are combined, clipped to a researcher-defined boundary, and grided to cells of a research-specified dimension.

From this point, a Data Availability (DA) and / or a Data Supporting (DS) analysis can be undertaken by the tool; both analyses will operate on a vector-based spatial dataset describing the target formation, following the labelling scheme specified in the supplementary material in Creason et al. [2023]. These data are rasterized according to the grid specification of the aforementioned domains with each cell tagged with the appropriate set of indices. In the case of a DA analysis, each pixel in the rasterized data is evaluated by applying Equation (1) as described in Creason et al. [2023], producing a DA score for each cell that is unique to each geologic resource type. For the DS analysis, the Spatial Implicit Multivariate Probability Assessment (SIMPA) method [Wingo et al., 2019] is applied using a series of predefined Fuzzy Logic statements, encapsulating the logic of Equations (2), (3), and (4) in Creason et al. [2023].

The URC Resource Assessment Method can be run either using the standalone GUI, or as a command-line tool. The former configuration is useful for a guided approach to executing the URC mineral-related analyses and previewing the results in the tool itself, whereas the latter is useful for integration of the tool into a workflow as part of a batch process. Regardless of how it is run, the results of the requested analyses are written to GeoTIFF files, which can be imported into most GIS analysis tools. Optionally, when run from the GUI the results of an analysis can be previewed within the tool itself.