Current methods for locating new sources of uranium are often time-consuming and costly, requiring governments and mining companies to fly aircrafts over large swaths of land in remote areas. But with Earth observation data being more accessible and computers' improved processing power, utilizing satellites to detect features in faraway places has become a much more viable option at a low cost.
Reda El-Arafy, a trained geologist and now an assistant nuclear geology and remote sensing assistant professor with Egypt’s Nuclear Materials Authority earned her Ph.D. at UAB School of Engineering. Using sensor systems on Earth-observation satellites, Sarah Parcak and Scott Brande found a more cost-effective way to identify potential uranium exploration sites in southwest Sinai.
ESTABLISHING GROUND TRUTH
El-Arafy first selected 30+ areas in southwestern Sinai in which uranium-rich deposits were suspected. A critical component of the study was to collect geological samples throughout the area for which advanced satellite imagery was available. Multi-spectral sensors can capture data in several spectral bands, with some recording up to 10 different bands. Hyperspectral sensors, however, record data in much narrower spectral bands, allowing us to focus our attention on very small specific parts of the electromagnetic spectrum. This is why El-Arafy chose to study data from the Landsat-8, ASTER, and HYPERION satellites, as minerals of interest in this study are highly recognizable in the short-wave infrared and the thermal-infrared spectral bands detected by multi-spectral and hyper-spectral sensors onboard.
As part of El-Arafy's research, the Egyptian Nuclear Material Authority provided additional ground truth interpretation information by sharing data from a gamma-ray spectrometer they flew over the study area. This new data set was overlaid on satellite images to help identify anomalies or variations in satellite sensor data and optimize algorithms for postprocessing.
LEARNING TO KNOW YOUR SAMPLES
Once the samples and remote sensor data were collected, El-Arafy divided his time between his two mentors, working alternately with Parcak - best known for her work discovering new archeological dig sites using satellites – learning the intricacies of Earth observation data post-processing and in the chemistry lab with Brande – a paleontologist and chemistry professor – to help analyze the composition of rocks El-Arafy personally collected in the study area.
There is a lack of data from this area - southwestern Sinai - and these were the first samples collected from some sites, so the team was unsure about the detailed geochemistry.
IDENTIFYING SATELLITE-VISIBLE TELL-TALE SIGNS FOR URANIUM DEPOSITS
El-Arafy’s chemical analysis revealed that secondary uranium mineralization in southwestern Sinai was specifically associated with zones containing iron oxide and clay minerals. Satellite sensors cannot directly detect uranium, but their associated minerals have distinct spectral signatures that can be identified.
As a final and most important step in the research, El-Arafy developed algorithms based on shortwave and thermal infrared signatures of iron oxides and clay minerals for proxy indicators of uranium deposits. By using these algorithms, the team was able to quickly identify unexplored areas with similar compositions in the study area, allowing them to find new potential targets more quickly and efficiently.
As a result of using data from advanced satellite sensors, El-Arafy developed an accurate system for identifying new uranium exploration targets in the southwestern Sinai desert over the course of four years.
The left image is a true colour composite, the center image is a false colour composite, and the right image is a false colour composite.
WHAT THE FUTURE OF RESOURCE EXPLORATION WILL LOOK LIKE
El-Arafy's studies are just one example of projects in the realm of resource exploration through Earth observation data. Until recently, a lack of standardization and unified access points for remote sensing made comprehensive analysis from imagery difficult. With the advancement of automated systems, expanding amounts of info increasingly have practical uses for many diversified endeavours such as smart cities, precision agriculture, or investment appraisal.