Application of hyperspectral remote sensing for supplementary investigation of polymetallic deposits in Huaniushan ore region, northwestern China
Abstract A gold–silver–lead–zinc polymetallic ore was selected in Huaniushan, Gansu Province as the study area. Hyperspectral aerial images as the primary information source, ground spectrum tests, and sampling analysis were used as auxiliary techniques. They were combined with large-scale mineral a...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/8165746e001f4396842eab56eac5e3e5 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Abstract A gold–silver–lead–zinc polymetallic ore was selected in Huaniushan, Gansu Province as the study area. Hyperspectral aerial images as the primary information source, ground spectrum tests, and sampling analysis were used as auxiliary techniques. They were combined with large-scale mineral and geological maps and other high-resolution satellite remote sensing images. Hyperspectral remote sensing classification identification and quantitative analysis methods were used to study the main mineral resources and rock mass occurrence. Finally, deposit distribution information was extracted and validated. The results showed that the effective classification methods by hyperspectral images were spectral angle mapping, minimum noise fraction transform, and mixed tuned matched filtering. Based on the ground survey, combined with sampling analysis, the accuracy of classification was 80%. The recognition rate of the main ore body—the iron-manganese cap lead–zinc oxide ore—was as high as 81%. This research showed that hyperspectral remote sensing in this mining area has excellent demonstration effects and is worth completing and supplementing original mineral and geological maps. The targets are important areas for detailed follow-up on mineral resource exploration. |
|---|