Semantic Scholar Open Access 2018 198 sitasi

Mapping mineral prospectivity through big data analytics and a deep learning algorithm

Yihui Xiong R. Zuo E. Carranza

Lihat Sumber DOI

Abstrak

Abstract Identification of anomalies related to mineralization and integration of multi-source geoscience data are essential for mapping mineral prospectivity. In this study, we applied big data analytics and a deep learning algorithm to process geoscience data to identify and integrate anomalies related to skarn-type Iron mineralization in the southwestern Fujian metallogenic zone of China. Based on the geological setting and environment for the formation of skarn-type Iron mineralization, 42 relevant variables, including two geological, one geophysical, and 39 geochemical variables, were analyzed and integrated for detecting anomalies related to mineralization using a deep autoencoder network. The results indicate that the mapped prospectivity areas have a strong spatial relationship with the locations of known mineralization and demonstrate that big data analytics supported by deep learning methods is a potential technique to be considered for use in mineral prospectivity mapping.

Topik & Kata Kunci

Geology

Penulis (3)

Yihui Xiong

R. Zuo

E. Carranza

Format Sitasi

APA MLA BibTeX

Xiong, Y., Zuo, R., Carranza, E. (2018). Mapping mineral prospectivity through big data analytics and a deep learning algorithm. https://doi.org/10.1016/J.OREGEOREV.2018.10.006

Akses Cepat

Lihat di Sumber doi.org/10.1016/J.OREGEOREV.2018.10.006

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 198×
Sumber Database: Semantic Scholar
DOI: 10.1016/J.OREGEOREV.2018.10.006
Akses: Open Access ✓