Application of Gaofen-5 Hyperspectral Data in Uranium Exploration: A Case Study of Weijing in Inner Mongolia, China

Gaofen-5 (GF-5) satellite is the world's first full-spectrum hyperspectral satellite to achieve comprehensive observations of the atmosphere and land. The Advanced Hyperspectral Imager (AHSI) carried by GF-5 can acquire 330-chanel imagery covering 390 - 2500 nm. However, the application of GF-5 AHSI imagery in uranium exploration is currently unknown. In this paper, the AHSI imagery was used for prospecting uranium mineralization in the Weijing, Inner Mongolia, China. The matched filter (MF) and threshold segmentation were used for mapping goethite, Al-high, Al-medium and Al-poor sericite. And the principal component analysis (PCA) and gray-level co-occurrence matrix (GLCM) were used to extract the texture information of the study area. Subsequently, combined with geological information, the relationship between alteration information, texture complexity and uranium mineralization was discussed, and it was pointed out that goethite, Al-medium, Al-poor sericite and texture complexity in this area can be used as indicators of uranium mineralization. Finally, three prospects were delineated, which will guide the follow-up uranium exploration in this area and promote the application of GF-5 AHSI data.

Uranium Exploration, Deposit and Resources: The Key of Nuclear Power Plant Development Program in Indonesia

Uranium deposit in Indonesia was found in almost all Indonesian Archipelago, mainly in Kalimantan, Sulawesi, Sumatera, Papua, Bangka Belitung and Riau islands. Uranium exploration activities started in the 1960s to recent, conducted in many exploration stages. The exploration in prospects area are completed with drilling activities to delineate the mineralization zone and continued to resources estimation. In Kalan Area, the research had been completed with underground/tunneling mining. The uranium resources are classified into discovered or undiscovered based on exploration stages, and conventional or unconventional based on sources of primary/secondary/by-product mineral production. The resources are calculated from Kalan Area and its surroundings (Kalimantan) with addition of Mamuju Area (West Sulawesi) and Sibolga Area (North Sumatera). Uranium identified resource in Indonesia is 13,503 tU while the undiscovered is 62,330 tU. Meanwhile, categorized by uranium source, the conventional and unconventional resources are 48,388 tU and 27,445 tU respectively. The uranium resources categories should be increased and completed with feasibility study to increase the resources to reserve classification. The exploration, deposit, and resources are the key to ensure the readiness of developing nuclear power plants in Indonesia, where one of them is Experimental Power Reactor (EPR) or Reaktor Daya Eksperimental (RDE) with domestic uranium fuel.

Uranium and Vanadium Resources of Utah: An Update in the Era of Critical Minerals and Carbon Neutrality

Utah is the second largest vanadium producing state and the third largest uranium producing state in the United States. Carnotite, a primary ore mineral for both vanadium and uranium, was first discovered and used by Native Americans as a source of pigment in the Colorado Plateau hysiographic province of eastern Utah. Radioactive deposits have been ommercially mined in Utah since about 1900, starting with radium, followed by vanadium, and thenuranium. In 1952, the discovery of the Mi Vida mine in Utah’s Lisbon Valley mining district in San Juan County kicked off a uranium exploration rush across the Colorado Plateau. As a result, the United States dominated the global uranium market from the early 1950s to late 1970s. In the modern mining era, Utah is an important contributor to the domestic uranium and vanadium markets with the only operating conventional uranium-vanadium mill in the country, multiple uranium-vanadium mines on standby, and active uranium-vanadium exploration. Overall, Utah has produced an estimated 122 million lbs U3O8 and 136 million lbs V2O5 since 1904. Most of this production has been from the sandstone-hosted deposits of the Paradox Basin, with minor production from volcanogenic deposits and as byproducts from other operations across the state

Geological, Geochemical, and Radiometric Study of Sandstone-Type Uranium Deposit Exploration in Menukung Area, West Borneo

BATAN has been carried out uranium exploration in West Borneo since 1969. So far, the exploration is focused on metamorphite-type uranium deposits in Kalan Area. The previous study concluded that mineralized uranium is originated from Sepauk Tonalite consisted of felsic-intermediate igneous rocks, and is hosted in medium-grade foliated and non-foliated metamorphic rocks of Pinoh Metamorphite. As uranium exploration develops, the International Atomic Energy Agency (IAEA) introduces the sandstone-type uranium mineralization concept that offers a more cost-effective mining process. The Melawi Basin becomes an attractive probable location for sandstone-type uranium deposit exploration since it is situated downstream of Schwaner Mountain's Sepauk Tonalite. The sandstone-dominated Tebidah Formation of Melawi Basin can be the host rock for sandstone-type uranium deposit if there is a reduction zone to trap the mobile uranium in the groundwater. The geological mapping, geochemical sampling, and radiometric survey were conducted in Menukung Area to prove the hypothesis. It is located in the eastern part of the Tebidah Formation, which contains abundant carbonaceous mudstones associated with coal seams. Mobile uranium content analysis showed the anomaly of 36–60 ppm at the central of Tebidah Formation at the study area, while radiometric data denoted the anomaly of 6.5–11.3 ppm eU. At those locations, coal and carbonaceous sandstone were observed. Those data indicate the presence of a reductive environment that gives the advantage to uranium trapping. It can be concluded that there is a possibility of the occurrence of sandstone-type uranium mineralization in the Menukung Area.

Computer modeling of electromagnetic data for mineral exploration: Application to uranium exploration in the Athabasca Basin

Electromagnetic (EM) methods are important geophysical tools for mineral exploration. Forward and inverse computer modeling are commonly used to interpret EM data. Real-life geology can be complex, and our computer modeling tools need to faithfully represent subsurface features to achieve accurate data interpretation. Traditional rectilinear meshes are less flexible and have difficulty conforming to the complex geometries of realistic geologic models, resulting in large numbers of mesh cells. In contrast, unstructured grids can represent complex geologic structures efficiently and accurately. However, building realistic geologic models and discretizing these models with unstructured grids suitable for EM modeling can be difficult and requires significant effort and specialized computer software tools. Therefore, it is important to develop workflows that can be used to facilitate model building and mesh generation. We have developed a procedure that can be used to build arbitrarily complex geologic models with topography using unstructured grids and a finite-volume time-domain code to calculate EM responses. We present an example of a trial-and-error modeling approach applied to a real data set collected at a uranium exploration project in the Athabasca Basin in Canada. The uranium mineralization is closely related to graphitic fault conductors in the basement. The deep burial depth and small thickness of the graphitic fault conductors demand accurate data interpretation results to guide subsequent drill testing. Our trial-and-error modeling approach builds initial realistic geologic models based on known geology and downhole data and creates initial geoelectrical models based on physical property measurements. Then, the initial model is iteratively refined based on the match between modeled and real data. We show that the modeling method can obtain 3D geoelectrical models that conform to known geology while achieving a good match between modeled and real data. The method can also provide guidance of where future drill holes should be directed.