Image of Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager

Text

Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager

Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geochemical and geophysical surveys, and/or remote sensing. Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials, prompting exploration geologists to seek more efficient and inventive ways for processing various data types at different phases of mineral exploration. Remote sensing is one of the most sought-after tools for early-phase mineral prospecting because of its broad coverage and low cost. Remote sensing images from satellites are publicly available and can be utilised for lithological mapping and mineral exploitation. In this study, we extend an artificial intelligence-based, unsupervised anomaly detection method to identify iron deposit occurrence using Landsat-8 Operational Land Imager (OLI) satellite imagery and machine learning. The novelty in our method includes: (1) knowledge-guided and unsupervised anomaly detection that does not assume any specific anomaly signatures; (2) detection of anomalies occurs only in the variable domain; and (3) a choice of a range of machine learning algorithms to balance between explain-ability and performance. Our new unsupervised method detects anomalies through three successive stages, namely (a) stage I – acquisition of satellite imagery, data processing and selection of bands, (b) stage II – predictive modelling and anomaly detection, and (c) stage III – construction of anomaly maps and analysis. In this study, the new method was tested over the Assen iron deposit in the Transvaal Supergroup (South Africa). It detected both the known areas of the Assen iron deposit and additional deposit occurrence features around the Assen iron mine that were not known. To summarise the anomalies in the area, principal component analysis was used on the reconstruction errors across all modelled bands. Our method enhanced the Assen deposit as an anomaly and attenuated the background, including anthropogenic structural anomalies, which resulted in substantially improved visual contrast and delineation of the iron deposit relative to the background. The results demonstrate the robustness of the proposed unsupervised anomaly detection method, and it could be useful for the delineation of mineral exploration targets. In particular, the method will be useful in areas where no data labels exist regarding the existence or specific spectral signatures of anomalies, such as mineral deposits under greenfield exploration.

Ketersediaan
277551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
15 hlm PDF, 14.623 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.3, December 2022
Subjek
Machine Learning
Remote sensing
Anomaly detection
Iron deposit
Lansat-8
Exploration
Prospecting
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager
    Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geochemical and geophysical surveys, and/or remote sensing. Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials, prompting exploration geologists to seek more efficient and inventive ways for processing various data types at different phases of mineral exploration. Remote sensing is one of the most sought-after tools for early-phase mineral prospecting because of its broad coverage and low cost. Remote sensing images from satellites are publicly available and can be utilised for lithological mapping and mineral exploitation. In this study, we extend an artificial intelligence-based, unsupervised anomaly detection method to identify iron deposit occurrence using Landsat-8 Operational Land Imager (OLI) satellite imagery and machine learning. The novelty in our method includes: (1) knowledge-guided and unsupervised anomaly detection that does not assume any specific anomaly signatures; (2) detection of anomalies occurs only in the variable domain; and (3) a choice of a range of machine learning algorithms to balance between explain-ability and performance. Our new unsupervised method detects anomalies through three successive stages, namely (a) stage I – acquisition of satellite imagery, data processing and selection of bands, (b) stage II – predictive modelling and anomaly detection, and (c) stage III – construction of anomaly maps and analysis. In this study, the new method was tested over the Assen iron deposit in the Transvaal Supergroup (South Africa). It detected both the known areas of the Assen iron deposit and additional deposit occurrence features around the Assen iron mine that were not known. To summarise the anomalies in the area, principal component analysis was used on the reconstruction errors across all modelled bands. Our method enhanced the Assen deposit as an anomaly and attenuated the background, including anthropogenic structural anomalies, which resulted in substantially improved visual contrast and delineation of the iron deposit relative to the background. The results demonstrate the robustness of the proposed unsupervised anomaly detection method, and it could be useful for the delineation of mineral exploration targets. In particular, the method will be useful in areas where no data labels exist regarding the existence or specific spectral signatures of anomalies, such as mineral deposits under greenfield exploration.
Komentar
Anda harus masuk sebelum memberikan komentar