Geological Mapping in Western Tasmania Using Radar and Random Forests

Radford, Declan D. G.; Cracknell, Matthew J.; Roach, Michael; Cumming, Grace

doi:10.1109/jstars.2018.2855207

Cited by 37 publications

(21 citation statements)

References 41 publications

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“…Other machine learning applications for mineral exploration have used random forest for lithological classification using geophysical data in the identification of nickel and base metal mineralization in western Tasmania (Australia) (Radford et al, 2018).…”

Section: Machine Learning and Mineral Explorationmentioning

confidence: 99%

Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model

Diaz-Rodriguez

Müller

Chandra

2021

Ore Geology Reviews

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Chandra. Your incredible patience, availability and support have been the cornerstone of this project. I am deeply grateful to you. I want to thank my dearest sister Laura and my brother Brendan for their incredible support during the past years in this beautiful city. To my parents Nancy and Luis, and to all my family in Colombia. This is also your achievement, and I will be forever grateful to you. I want to thank Mrs. Ana Juliana Villa for the conversations and the invaluable help with figures and maps construction. To Dr. Sara Moron Polanco for her incredible support and for the coffee delights during the "darkest hours". To Dr. Nathan Butterworth for his guidance during the early stages of this work. Also, to Mr. Michael Chin and Mr. Danial Azam for their technical support, and to my Madsen mate, coffee enthusiast and salsa guru: Julian Giordani. This building was (literally) empty when you were not around.

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Section: Machine Learning and Mineral Explorationmentioning

confidence: 99%

Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model

Diaz-Rodriguez

Müller

Chandra

2021

Ore Geology Reviews

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“…On the other hand, Haralick's descriptors can capture information on intensity and amplitude based on global statistics of SAR images. Radford et al [19] used textural information derived from GLCM, along with Random Forests, for geological mapping of remote and inaccessible localities; the authors obtained a classification accuracy of ≈ 90 %, even when using limited training data (≈ 0.15 % of the total data). Hagensieker and Waske [20] evaluated the synergistic contribution of multi-temporal L-, C-, and X-band data to tropical land cover mapping, comparing classification outcomes of ALOS-2 [21], RADARSAT-2 [22], and TerraSAR-X [23] datasets for a study site in the Brazilian Amazon using a wrapper approach.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Classification of SAR Textures Using Information Theory

Chagas

Frery

Rosso

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The use of Bandt-Pompe probability distributions and descriptors of Information Theory has been presenting satisfactory results with low computational cost in the time series analysis literature [1]-[3]. However, these tools have limitations when applied to data without time dependency. Given this context, we present a newly proposed technique for texture analysis and classification based on the Bandt-Pompe symbolization for SAR data. It consists of (i) linearize a 2-D patch of the image using the Hilbert-Peano curve, (ii) build an Ordinal Pattern Transition Graph that considers the data amplitude encoded into the weight of the edges; (iii) obtain a probability distribution function derived from this graph; (iv) compute Information Theory descriptors (Permutation Entropy and Statistical Complexity) from this distribution and use them as features to feed a classifier. The ordinal pattern graph we propose considers that the edges' weight is related to the absolute difference of observations, which encodes the information about the data amplitude. This modification considers the unfavorable signal-to-noise ratio of SAR images and leads to the characterization of several types of textures. Experiments with data from Munich urban areas, Guatemala forest regions, and Cape Canaveral ocean samples show the effectiveness of our technique in homogeneous areas, achieving satisfactory separability levels. The two descriptors chosen in this work are easy and quick to calculate and are used as input for a k-nearest neighbor classifier. Experiments show that this technique presents results similar to state-of-theart techniques that employ a much larger number of features and, consequently, impose a higher computational cost.

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“…ML algorithms such as artificial neural network (ANN), support vector machine (SVM), regression tree (RT), and random forest (RF) are powerful data driven methods that are becoming extremely popular in such applications as the mapping of mineral prospectivity [26][27][28], mapping geochemical anomalies [29][30][31], geological mapping [32][33][34][35], drill-core mapping [36][37][38], and mineral phase segmentation for X-ray microcomputed tomography data [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

2021

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Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

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Geological Mapping in Western Tasmania Using Radar and Random Forests

Cited by 37 publications

References 41 publications

Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model

Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model

Analysis and Classification of SAR Textures Using Information Theory

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

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