Constraints of deep crustal structures on large deposits in the Cloncurry district, Australia: Evidence from spatial analysis

Lu, Yao; Liu, Liangming; Xu, Guojian

doi:10.1016/j.oregeorev.2016.05.022

Cited by 9 publications

(5 citation statements)

References 58 publications

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“…Economic factors make it crucial to comprehend the structural mechanisms underlying hydrothermal ore deposits over deep crustal discontinuities, but this is still up for debate. Some scientists contend that fault bends are the main factor influencing ore clusters, while others think fault intersections are crucial (Lu et al, 2016). However, both situations can play a role in the genesis of hydrothermal ore deposits.…”

Section: Introductionmentioning

confidence: 99%

Highlighting Volcanic Outcrops by Mapping Geological Lineaments Using Satellite Data in the Saka Region, North-East Morocco

Elaaraj,

Lhachmi,

Tabyaoui

et al. 2024

Ecol. Eng. Environ. Technol.

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Section: Introductionmentioning

confidence: 99%

Highlighting Volcanic Outcrops by Mapping Geological Lineaments Using Satellite Data in the Saka Region, North-East Morocco

Elaaraj,

Lhachmi,

Tabyaoui

et al. 2024

Ecol. Eng. Environ. Technol.

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“…Spatial analysis is an important tool to extract useful spatial ore-controlling information from 3D models. Existing methods have a laudable ability to capture the geological body spatial features, such as field analysis, shape analysis, and model projection [20][21][22][23][24][25]. The model spatial features are commonly selected as critical predictive variables for 3D MPM, which has already contributed to new resource discoveries [12,14,[26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Mineral Prospectivity Modeling with the Integration of Ore-Forming Computational Simulation in the Xiadian Gold Deposit, Eastern China

Liu,

Guo,

Wang

et al. 2023

Applied Sciences

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Finding new, effective predictive variables for 3D mineral prospectivity modeling is both important and challenging. The 3D ore-forming numerical modeling quantitively characterizes the complex coupling-mineralization process of the structure, fluid, heat, and wall rock, which may be potential indicators for mineral exploration. We here conducted 3D mineral prospectivity modeling with the integration of ore-forming computational simulation information in the Xiadian orogenic gold deposit, China, to examine whether the simulation data input can improve the reliability of prospectivity modeling. First, we constructed the 3D models of the orebody and fault to extract the fault geometric features using spatial analysis, as they are always considered to be the crucial controls of gold distribution. Second, we performed 3D numerical modeling of the deformation–fluid–heat-coupling process of the structurally controlled hydrothermal Au system using the FLAC3D platform. Finally, the fault-geometry features (buffer, dip, dip variation, and undulation) and the ore-formation-simulation indices (volume strain, shear strain, temperature variation, and fluid flux) were integrated using Bayesian decomposition modeling, which has a promising nonlinear model ability and a flexible variable-integration ability. The prospectivity modeling results demonstrated that the model generated by combining geometry and simulation variables achieved significantly higher AUC, precision, accuracy, Kappa, and F1 scores compared to other models using a single-predictor-variable dataset. This suggests that the joint use of geometry and simulation variables construct a comprehensive association between gold and its ore-controlling factors, thereby resulting in a highly reliable prospectivity model. Thus, the approach of 3D mineral prospectivity modeling aided by ore-forming numerical simulation proves to be more useful in guiding mineral exploration, especially in the condition of fewer variables. Based on the prospectivity modeling outcomes, we identified four gold targets at depth in the Xiadian district that warrant focused exploration efforts.

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“…In recent years, the introduction of big data and artificial intelligence algorithms has been a major development in geological research fields. Machine learning and deep learning algorithms have been used to uncover hidden relationships in massive structured and unstructured geoscience data [1][2][3][4][5][6], identify geochemical anomalies [7][8][9][10][11][12], and predict potential mineral resources [13][14][15][16][17]. Furthermore, with the development of deep learning, image-based computer vision technology has made it possible to intelligently identify and classify rocks by their optical properties [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Multitarget Intelligent Recognition of Petrographic Thin Section Images Based on Faster RCNN

et al. 2023

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The optical features of mineral composition and texture in petrographic thin sections are an important basis for rock identification and rock evolution analysis. However, the efficiency and accuracy of human visual interpretation of petrographic thin section images have depended on the experience of experts for a long time. The application of image-based computer vision and deep-learning algorithms to the intelligent analysis of the optical properties of mineral composition and texture in petrographic thin section images (in plane polarizing light) has the potential to significantly improve the efficiency and accuracy of rock identification and classification. This study completed the transition from simple petrographic thin image classification to multitarget detection, to address more complex research tasks and more refined research scales that contain more abundant information, such as spatial, quantitative and category target information. Oolitic texture is an important paleoenvironmental indicator that widely exists in sedimentary records and is related to shallow water hydraulic conditions. We used transfer learning and image data augmentation in this paper to identify the oolitic texture of petrographic thin section images based on the faster region-based convolutional neural network (Faster RCNN) method. In this study, we evaluated the performance of Faster RCNN, a two-stage object detection algorithm, using VGG16 and ResNet50 as backbones for image feature extraction. Our findings indicate that ResNet50 outperformed VGG16 in this regard. Specifically, the Faster RCNN model with ResNet50 as the backbone achieved an average precision (AP) of 92.25% for the ooids test set, demonstrating the accuracy and reliability of this approach for detecting ooids. The experimental results also showed that the uneven distribution of training sample images and the complexity of images both significantly affect detection performance; however, the uneven distribution of training sample images has a greater impact. Our work is preliminary for intelligent recognition of multiple mineral texture targets in petrographic thin section images. We hope that it will inspire further research in this field.

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Constraints of deep crustal structures on large deposits in the Cloncurry district, Australia: Evidence from spatial analysis

Cited by 9 publications

References 58 publications

Highlighting Volcanic Outcrops by Mapping Geological Lineaments Using Satellite Data in the Saka Region, North-East Morocco

Highlighting Volcanic Outcrops by Mapping Geological Lineaments Using Satellite Data in the Saka Region, North-East Morocco

Three-Dimensional Mineral Prospectivity Modeling with the Integration of Ore-Forming Computational Simulation in the Xiadian Gold Deposit, Eastern China

Multitarget Intelligent Recognition of Petrographic Thin Section Images Based on Faster RCNN

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