Mineral Prospectivity Mapping Using Deep Self-Attention Model

Yin, Bojun; Sun, Siquan

doi:10.1007/s11053-022-10142-8

Cited by 25 publications

(3 citation statements)

References 92 publications

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“…This is because performance evaluations are typically made on an experimentation of algorithm selection, while controlling other portions of the workflow. This is exemplified by a sizable body of literature, whose raison dÕe ˆtre is to demonstrate the effectiveness of novel algorithms in MPM tasks (Chen & Wu, 2017;Xiong et al, 2018;Chen et al, 2020;Wang et al, 2020;Yang et al, 2022;Yin & Li, 2022;Zuo et al, 2022;Gharehchopogh et al, 2023;Li et al, 2023;Yin et al, 2023). There also have been recent efforts to examine specifically this type of uncertainty, in a GIS knowledge-driven framework (Daviran et al, 2022).…”

Section: Review Of Data-driven Mpm Workflowsmentioning

confidence: 99%

“…The range of all possible algorithms is unknowable because there are emerging algorithms and variations of existing ones, either as architectural modifications (e.g., changes in neural network architecture) or as add-ons (e.g., optimization algorithms; Chen et al, 2020;Yin & Li, 2022;Gharehchopogh et al, 2023). An empirical analysis revealed that algorithms used by various authors include (non-exhaustively): Bayes network (Porwal et al, 2006;Yin & Li, 2022); logistic regression (Agterberg & Bonham-Carter, 1999;Carranza & Hale, 2001;Karbalaei Ramezanali et al, 2020;Lin et al, 2020;Zhang et al, 2022c); support vector machines (Zuo & Carranza, 2011;Zhang et al, 2021;Senanayake et al, 2023); tree-based methods, such as random forest, extra trees and XGBoost (Chen & Wu, 2019;Sun et al, 2019;Zhang et al, 2022a); artificial neural networks, such as extreme learning machines (Chen & Wu, 2017); deep learning methods (Xiong et al, 2018;Wang et al, 2020;Yang et al, 2022;Zuo et al, 2022Li et al, 2023;Yin et al, 2023;Zuo & Xu, 2023); and reinforcement learning (Shi et al, 2023). There are also applicative MPM studies that employed ensemble learning, which is an approach to improve outcome reliability by integrating the output of multiple independent models (e.g., Senanayake et al, 2023;Shetty et al, 2023).…”

Section: Review Of Data-driven Mpm Workflowsmentioning

confidence: 99%

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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

View full text Add to dashboard Cite

show abstract

“…Based on the above analysis, the next step is to determine the transition probability threshold P k Θ for each order. Firstly, the self-attention mechanism [15] is used to obtain the weight values of each edge. The self-attention mechanism can independently calculate attention weights at each position, which has advantages over traditional attention mechanisms.…”

Section: Local Convolutional Channelmentioning

confidence: 99%

Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm

Niu

2023

Future Internet

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The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problems: the fixed threshold of transfer probability leads to a decrease in filtering effect of neighborhood information; the K-means clustering algorithm is prone to trapping clustering results into local optima, resulting in incomplete global interaction graphs; and the impact of time factors on the predicted results was not considered. To solve these problems, a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF) is proposed. Firstly, the self-attention mechanism and weighted average method are used to calculate the threshold of neighborhood transition probability for each order in local convolutional channels; secondly, the K-means++ clustering algorithm is used to determine the clustering center in the global convolutional channel, and the fuzzy C-means clustering algorithm is used for clustering to solve the local optimal problem; then, time factor is introduced to further improve predicted results, making them more accurate. Comparative experiments using normalized discounted cumulative gain (NDCG) and recall as evaluation metrics on three publicly available datasets showed that DCFECF improved by up to 2.3% and 4.1% on two metrics compared to DCCF.

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Projection Pursuit Random Forest for Mineral Prospectivity Mapping

Chen

Xiao

2023

Math Geosci

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Mineral Prospectivity Mapping Using Deep Self-Attention Model

Cited by 25 publications

References 92 publications

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm

Projection Pursuit Random Forest for Mineral Prospectivity Mapping

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