Vote for your favorite mineral in #MinCup26! - Quartz vs. Asagiite
It's a classic vs a newbie as the most common named mineral in the crust Quartz is up against an exceedingly-rare sea foam-green mineral discovered just a few years ago Asagiite.
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Quartz from
Rajapalot, Ylitornio, Lapland, Finland


Locality type:Project
Classification
Species:Quartz
Formula:SiO2
Confirmation
Validity:Believed Valid
Data
Mineral Data:Click here to view Quartz data
Locality Data:Click here to view Rajapalot, Ylitornio, Lapland, Finland
Data Identifiers
Mindat Occurrence Record ID:1526669
Long-form Identifier:1:3:1526669:1
GUID (UUID V4):0
Localities for Quartz in this Region
Palokas, Rajapalot, Ylitornio, Lapland, Finland
Nearest other occurrences of Quartz
22.4km (13.9 miles) ⓘKivilompolo, Ylitornio, Lapland, Finland
50.3km (31.3 miles) ⓘKivimaa, Tervola, Lapland, Finland
52.4km (32.6 miles) ⓘRompas, Ylitornio, Lapland, Finland
55.6km (34.5 miles) ⓘPalokas, Rajapalot, Ylitornio, Lapland, Finland
63.8km (39.6 miles) ⓘSören, Töre, Kalix, Norrbotten County, Sweden
64.2km (39.9 miles) ⓘPukinselkä, Tervola, Lapland, Finland
66.4km (41.3 miles) ⓘLaurila gold occurrence, Keminmaa, Lapland, Finland
69.1km (43.0 miles) ⓘVinsa, Rovaniemi, Lapland, Finland
73.0km (45.4 miles) ⓘGräddmanshällan Västra, Kalix, Norrbotten County, Sweden
77.9km (48.4 miles) ⓘPetäjävaara (Rosvohotu), Rovaniemi, Lapland, Finland
References
Chudasama, B., Torppa, J., Nykänen, V., Kinnunen, J., Lerssi, J., & Salmirinne, H. (2022). Target-scale prospectivity modeling for gold mineralization within the rajapalot Au-Co project area in northern Fennoscandian Shield, Finland. Part 2: application of knowledge-driven-and machine learning-based-hybrid-expert systems for exploration targeting and addressing model-based uncertainties. Ore Geology Reviews, 104937.
Chudasama, B., Torppa, J., Nykänen, V., Kinnunen, J., Lerssi, J., & Salmirinne, H. (2022). Target-scale prospectivity modeling for gold mineralization within the rajapalot Au-Co project area in northern Fennoscandian Shield, Finland. Part 1: application of knowledge-driven-and machine learning-based-hybrid-expert systems for exploration targeting and addressing model-based uncertainties. Ore Geology Reviews, 104936.
 
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