A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

Chen, Lirong; Guan, Qingfeng; Feng, Bin; Yue, Hanqiu; Wang, Junyi; Zhang, Fan

doi:10.3390/min9050270

Cited by 45 publications

(9 citation statements)

References 59 publications

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“…The total collected samples were 5,376 stream sediment samples (Figure 3) from stream systems in eastern Thailand, with a density of a sample per 5 km 2 . The data were collected under the Department of Mineral Resources in Thailand (DMR), covering 34,380 km 2 of territory with multielement analyses of 12 elements, with elements As (arsenic), Ba (barium), Co (cobalt), Cr (chromium), Cu (copper), Fe [75], Mg (magnesium), Mn (manganese), Ni (nickel), Pb (lead), V (vanadium), and 6 Zn (zinc). The samples were collected from active stream sediment [76] consisting of clay to sandsized particles along a 30-50 m stretch of the stream.…”

Section: Geochemical Datamentioning

confidence: 99%

Prediction of Au-Associated Minerals in Eastern Thailand Based on Stream Sediment Geochemical Data Analysis by S-A Multifractal Model

Yaisamut¹,

Xie²,

Charusiri³

et al. 2023

Preprint

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Conducted within the scope of geochemical exploration in Eastern Thailand, this study aims to detect geochemical anomalies and potential mineral deposits. The objective was to interpret intricate spatial dispersion patterns and concentration levels of deposit pathfinder elements, specifically arsenic (As), copper (Cu), and zinc (Zn), using a comprehensive array of stream sediment geochemistry data. Methodologies involved integrating multifractal properties and traditional statistics, facilitated by the GeoDAS and ArcGIS platforms as instrumental analytical tools. In total, 5,376 stream sediment samples were collected and evaluated, leading to the development of an in-depth geochemical map. The results indicated distinct geological units marked by substantially elevated average values of the aforementioned elements. Identification of geochemical anomalies was achieved through the spatial distribution method and the subsequent application of the Spectrum-Area (S-A) multifractal model. An intriguing link was found between high As concentrations and gold deposits in the area, suggesting As as a viable pathfinder element for gold mineralization. The anomaly maps, generated from the stream sediment data, spotlighted potential zones of interest, offering valuable guidance for future mineral exploration and geological inquiries. Nonetheless, it is vital to recognize that the increased values noted in these maps may be influenced by regional geological factors, emphasizing the necessity for a diverse set of analytical methods for accurate interpretation. The study's significance lies in its pioneering use of the S-A multifractal model in geochemical data analysis. This innovative approach has deepened our comprehension of geochemical dispersion patterns and improved the precision of mineral exploration.

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Section: Geochemical Datamentioning

confidence: 99%

Prediction of Au-Associated Minerals in Eastern Thailand Based on Stream Sediment Geochemical Data Analysis by S-A Multifractal Model

Yaisamut¹,

Xie²,

Charusiri³

et al. 2023

Preprint

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“…In order to use the advantages of both autoencoders and CNNs, CAEs are used in this study, which usually use convolution and pooling layers to extract the key features of the input data and compress them (encoding) and deconvolution and unpooling layers to reconstruct the original data from the compressed form (decoding) [36]. Figure 2 shows an illustrative example of the structure of a two-dimensional (2D) CAE.…”

Section: Overview Of Autoencodersmentioning

confidence: 99%

Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder

Rastin

Amiri

Darvishan

2021

Shock and Vibration

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Structural health monitoring (SHM) is a hot research topic with the main purpose of damage detection in a structure and assessing its health state. The major focus of SHM studies in recent years has been on developing vibration-based damage detection algorithms and using machine learning, especially deep learning-based approaches. Most of the deep learning-based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. As it is not usually possible to collect data from damaged states of a large civil structure, using such algorithms for these structures may be impractical. This paper proposes a new unsupervised deep learning-based method for structural damage detection based on convolutional autoencoders (CAEs). The main objective of the proposed method is to identify and quantify structural damage using a CAE network that employs raw vibration signals from the structure and is trained by the signals solely acquired from the healthy state of the structure. The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. Applications on the two numerical models of IASC-ASCE benchmark structure and a grid structure located at the University of Central Florida, as well as the full-scale Tianjin Yonghe Bridge, prove the efficiency of the proposed algorithm in assessing the global health state of the structures and quantifying the damage.

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“…Applying the deep learning algorithms as a subcategory of machine learning algorithms can lead to improving the accuracy of classification or prediction by replacing the manual selection [45]. Such techniques have been employed in recognizing geochemical anomalies related to mineralization via deep autoencoder networks [46], deep variational autoencoder network [45], convolutional autoencoder networks [91], and combining deep learning with other anomaly detection methods [54,56].…”

Section: The Outputs Predicted/modeled Using ML In the Selected Literature And The Inputs Utilizedmentioning

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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A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

Cited by 45 publications

References 59 publications

Prediction of Au-Associated Minerals in Eastern Thailand Based on Stream Sediment Geochemical Data Analysis by S-A Multifractal Model

Prediction of Au-Associated Minerals in Eastern Thailand Based on Stream Sediment Geochemical Data Analysis by S-A Multifractal Model

Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder

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

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