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

Nwaila, Glen T.; Zhang, Steven E.; Bourdeau, Julie E.; Ghorbani, Yousef; Carranza, Emmanuel John M.

doi:10.1016/j.aiig.2022.10.001

Cited by 9 publications

(7 citation statements)

References 43 publications

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“…1). Within MPM, and for strictly the purpose of data analysis, machine learning is used to primarily: (1) identify mineralization-related anomalies through unsupervised learning (e.g., Nwaila et al, 2022;Zhang et al, 2022b); (2) predict targets that are similar to known occurrences through supervised learning (e.g., Zuo & Carranza, 2011;Zhang et al, 2021;Senanayake et al, 2023); and (3) predict targets using reinforcement learning (e.g., Shi et al, 2023). Outside of data analysis, machine learning is also beginning to be used in: (1) data generation (e.g., Zhang et al, 2022b;Bourdeau et al, 2023); (2) data processing (e.g., Song et al, 2020;Nwaila et al, 2023;Zhang et al, 2023); and (3) simulations (e.g., Song et al, 2021).…”

Section: Review Of Data-driven Mpm Workflowsmentioning

confidence: 99%

“…A second major source of variability in data-driven workflows is the construction of covariates, which are referred to as Ôevidence layersÕ in the geospatial realm. Covariate data can range from mono-disciplinary, such as geochemical (e.g., Zhang et al, 2022b) or spectral (e.g., Nwaila et al, 2022), up to sizable combinations of qualitative and quantitative geoscientific data (e.g., Lawley et al, 2022). The variability encountered in covariate construction falls into two major categories: (1) the number of covariates; and (2) their quality.…”

Section: Review Of Data-driven Mpm Workflowsmentioning

confidence: 99%

See 1 more Smart Citation

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

Zhang,

Lawley,

Bourdeau

et al. 2024

Nat Resour Res

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The primary goal of mineral prospectivity mapping (MPM) is to narrow the search for mineral resources by producing spatially selective maps. However, in the data-driven domain, MPM products vary depending on the workflow implemented. Although the data science framework is popular to guide the implementation of data-driven MPM tasks, and is intended to create objective and replicable workflows, this does not necessarily mean that maps derived from data science workflows are optimal in a spatial sense. In this study, we explore interactions between key components of a geodata science-based MPM workflow on the geospatial outcome, within the modeling stage by modulating: (1) feature space dimensionality, (2) the choice of machine learning algorithms, and (3) performance metrics that guide hyperparameter tuning. We specifically relate these variations in the data science workflow to the spatial selectivity of resulting maps using uncertainty propagation. Results demonstrate that typical geodata science-based MPM workflows contain substantial local minima, as it is highly probable for an arbitrary combination of workflow choices to produce highly discriminating models. In addition, variable domain metrics, which are key to guide the iterative implementation of the data science framework, exhibit inconsistent relationships with spatial selectivity. We refer to this class of uncertainty as workflow-induced uncertainty. Consequently, we propose that the canonical concept of scientific consensus from the greater experimental science framework should be adhered to, in order to quantify and mitigate against workflow-induced uncertainty as part of data-driven experimentation. Scientific consensus stipulates that the degree of consensus of experimental outcomes is the determinant in the reliability of findings. Indeed, we demonstrate that consensus through purposeful modulations of components of a data-driven MPM workflow is an effective method to understand and quantify workflow-induced uncertainty on MPM products. In other words, enlarging the search space for workflow design and experimenting with workflow components can result in more meaningful reductions in the physical search space for mineral resources.

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Section: Review Of Data-driven Mpm Workflowsmentioning

confidence: 99%

Section: Review Of Data-driven Mpm Workflowsmentioning

confidence: 99%

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

Zhang,

Lawley,

Bourdeau

et al. 2024

Nat Resour Res

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“…In addition, a blue band (band 1: 0.433-0.453 µm) and a cirrus band (band 9: 1.360-1.390 µm) are added, which can be applied to coastal observation and cloud detection, respectively. Furthermore, all the OLI and TIRS spectral bands are stored as geolocated 16-bit digital numbers in the same Level 1 terrain corrected (L1T) file, which facilitates the differentiating of materials more efficiently than ETM+ imagery stored as 8-bit numbers [47,48]. Although multispectral sensors such as TM, ETM+, and OLI render insufficient spectral resolution for discriminating specific minerals, achieving effective processing, or running data analysis, they yield useful image products for regional exploration and discovery when they are combined with a good understanding of the associated landforms [46].…”

Section: Data Sourcesmentioning

confidence: 99%

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Yang,

Zheng,

Chen

et al. 2023

Applied Sciences

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Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of the Yarlung Zangbo ophiolite belt, particularly in the harzburgite unit of the mantle section. Conducting field surveys in complex and poorly accessible terrain is challenging, expensive, and time-consuming. Remote sensing is an advanced method of achieving modern geological work and is a powerful technical means of geological research and mineral exploration. In order to delineate outcrops of chromite-bearing mantle peridotite, the present research study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite, for the interpretation of Landsat8 OLI and WorldView-2 satellite data. This integrated approach allows the effective discrimination of chromite-containing peridotite outcrops in the Luobusa area, Tibet. The interpretation results derived from these integrated image-processing techniques were systematically verified in the field and formed the basis of the feature selection process of different lithologies supported by the support vector machine algorithm. Furthermore, the distribution range of the ferric contamination anomaly is detected through the de-interference abnormal principal component thresholding technique, which shows a high spatial matching relationship with mantle peridotite. This is the first study to utilize Landsat8 OLI and WorldView-2 remote sensing satellite data to explore the largest chromite deposit in China, which enriches the research methods for the chromite deposits in the Luobusa area. Accordingly, the results of this investigation indicate that the integration of information extracted from image-processing algorithms using remote sensing data could be a broadly applicable tool for prospecting chromite ore deposits associated with ophiolitic complexes in mountainous and inaccessible regions such as Tibet’s ophiolitic zones.

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“…ML algorithms have been employed in the study and exploration of mineral deposits to identify and model patterns and regularities that are unobvious (Srinivasan & Fisher, 1995;Galetakis et al, 2022;Mery & Marcotte, 2022). This has been demonstrated in studies such as applying ML algorithms to satellite imagery to locate and study mineral deposits, and to improve mineral exploration (Maxwell et al, 2018;Cevik et al, 2021;Diaz-Gonzalez et al, 2022;Liu et al, 2022;Nwaila et al, 2022). Particularly related to this study is the use of ML to perform geodomain boundary delineation (Zhang et al, 2023), which is a task that is required in the geostatistical treatment of mapping to resource estimation.…”

Section: Introductionmentioning

confidence: 99%

Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models

Nwaila,

Zhang,

Bourdeau

et al. 2023

Nat Resour Res

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In geospatial data interpolation, as in mapping, mineral resource estimation, modeling and numerical modeling in geosciences, kriging has been a central technique since the advent of geostatistics. Here, we introduce a new method for spatial interpolation in 2D and 3D using a block discretization technique (i.e., microblocking) using purely machine-learning algorithms and workflow design. This paper addresses the challenges of modeling spatial patterns and regularities in nature, and how different approaches have been used to cope with these challenges. We specifically explore the advantages and drawbacks of kriging while highlighting the long and complex sequence of procedures associated with block kriging. We argue that machine-learning techniques offer opportunities to simplify and streamline the process of mapping and mineral resource estimation, especially in cases of strong spatial relationships between sample location and resource concentration. To test the new method, synthetic 2D and 3D data were used for both 2D block modeling and geometallurgical modeling of a synthetic porphyry Cu deposit. The synthetic porphyry Cu data were very useful in validating the performance of the proposed microblocking technique as we were able to reproduce known values at unsampled locations. Our proposed method delivers the benefits of a machine learning-based block modeling approach, which includes its simplicity (a minimum of 2 hyperparameters), speed and familiarity to data scientists. This enables data scientists working on spatial data to employ workflows familiar to their training, to tackle problems that were previously solely in the domain of geoscience. In exchange, we expect that our method will be a gateway to attract more data scientist to become geodata scientists, benefitting the modern data-driven mineral value chain.

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

Cited by 9 publications

References 43 publications

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models

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