URC Assessment Method

Latest Release: 1.0.1

Logic for running Unconventional Rare-Earth & Critical Minerals (URC) analyses on structured data, wrapped into a tool.

The tool documentation can be built using Sphinx with the content in the user_doc folder. Alternately, a compiled version of the documentation is mirrored on ReadTheDocs.io here.

The license and contribution instructions are also included as part of this distribution.

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Statement of Need

The URC Resource Assessment Method applies the data analysis methods outlined in Creason et al. 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. 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. 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. 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., 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 is applied using a series of predefined Fuzzy Logic statements, encapsulating the logic of Equations (2), (3), and (4) in Creason et. al.

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.

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Installation

Installation will require downloading/cloning this respository.

Ensure that all the packages in the Package Dependencies section of this document are installed in the environment under which the URC Assessment Method is to be run. Provided this requirement is met, the code in in this distribution can be run as-is by following the instructions in the How To Run section.

If challenges are encountered when attempting to download and install the required packages, see the Installation Tips and Troubleshooting section, below.

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How To Run

The tool can be launched by running the urc_assessment_method.py script. Running with no arguments will launch the Graphical User Interface (GUI). Launching with command line arguments will run the tool in batch execution mode. To see the arguments for the command line interface (CLI), launch with urc_assessment_method.py <task> -h, where <task> is one of the labels provided in the next section.

An Example Tutorial can be found in the user documentation, or at the ReadtheDocs.io mirror.

Available Processes Tasks

• Create Grid: Generate index files for the various domain types which is scaled to a specific grid cell size. This task can be invoked using the CLI by specifying the create_grid task.
• Calculate PE Score: Utilize the outputs from the Create Grid task and apply to a specified, structured dataset to calculate the PE Score for various mechanisms. This task can be invoked from the CLI by specifying the pe_score task.

Running Unit Tests

Unit Tests exist for the core utility functions within the urclib package, namely common_utils.py and urc_common.py. These tests can be run using pytest, namely by opening a terminal in the root directory of the repository, and running:

pytest

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Package Dependencies

The code in this repository targets Python version 3.7 or greater.

The following packages are necessary for running the URC tool; optional dependencies are marked with an asterisk (*):

• NumPy: Any recent version should do, but tested against 1.20.3.
• pandas: Tested with 1.2.5.
• GDAL: Tested with version 3.3.0, and should work with more recent versions.
• PyQt5 *: Tested against 5.15.2, but any 5.15.x version should work. Only required if running urc_assessment_method.py with the GUI active. Based on the Qt library.
• PyOpenGL *: Only required if urc_assessment_method.py is run with GUI active. Version 3.1.6.
• PyOpenGL-accelerate *: strictly optional; improves performance of OpenGL operations invoked by PyOpenGL; version should match with PyOpenGL: 3.1.6.
• PyGLM *: Only required if urc_assessment_method.py is run with GUI active. Version 2.2.0.

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Installation Tips and Troubleshooting

Virtual Environments

It is recommended to run this software under a freshly created virtual environment. To this end, a pair of ad hoc requirements files are provided, one for using Python's standard venv package, and one for virtual environments using Anaconda.

To build an environment using venv:

python3 -mvenv urc_env
source urc_env/bin/activate

python -mpip install -r requirements.txt

To build an environment using conda:

conda env create -n URC --file environment.yml

Notes on installing GDAL

GDAL can be tricky to install. For Windows, it is recommended using Anaconda with the following install command:

conda install -c conda-forge gdal

For Linux it is recommended to use pip with the following steps:

1. Install the GDAL developer and binary files using the package manager and packages appropriate for your Linux distribution. For Ubuntu, this would look something like:

   sudo apt update
   sudo apt install libgdal-dev gdal-data

2. Install the GDAL python bindings using pip, ensuring that the bindings version matches the previously installed libraries. This can be done through the clever use of gdal-config:

   pip install gdal==$(gdal-config --version)

Further information on installing GDAL and working within Python can be found in the Binaries and Python Bindings sections of the official GDAL website.