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A Physically Constrained Variational Autoencoder for Geochemical Pattern Recognition
Quantification and recognition of geochemical patterns are extremely important for geochemical prospecting and can facilitate a better understanding...
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Identification of Geochemical Anomalies Using an End-to-End Transformer
Deep learning methods have demonstrated remarkable success in recognizing geochemical anomalies for mineral exploration. Typically, these methods...
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Visual Interpretable Deep Learning Algorithm for Geochemical Anomaly Recognition
Deep learning algorithms (DLAs) have achieved better results than traditional methods in the field of multivariate geochemical anomaly recognition...
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Recognizing Multivariate Geochemical Anomalies Related to Mineralization by Using Deep Unsupervised Graph Learning
The spatial structure of geochemical patterns is influenced by various geological processes, one of which may be mineralization. Thus, analysis of...
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Application of improved support vector machine in geochemical lithology identification
Lithology identification is an important task in oil and gas exploration. In recent years, machine learning methods have become a powerful tool for...
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Metallogenic-Factor Variational Autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms
Deep learning algorithms (DLAs) are becoming hot tools in processing geochemical survey data for mineral exploration. However, it is difficult to...
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Blind Source Separation of Spectrally Filtered Geochemical Signals to Recognize Multi-depth Ore-Related Enrichment Patterns
This contribution conceptualizes a blind source separation (BSS) model to recover sources of geochemical signals such that multi-depth ore-related...
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Geochemical soil survey for gold in Ogudu-Ogbagba Area, Osun State, southwestern Nigeria
Soil geochemical study was carried out in Ogudu-Ogbagba area of Osun State, Southwestern Nigeria to evaluate a prospect for gold mineralization....
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Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder
Deep neural networks perform very well in learning high-level representations in support of multivariate geochemical anomaly recognition. Geochemical...
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Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability
Effective geochemical anomaly identification is crucial in mineral exploration. Recent trends have favored deep learning (DL) to decipher geochemical...
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Mineral Prospectivity Prediction Based on Self-Supervised Contrastive Learning and Geochemical Data: A Case Study of the Gold Deposit in the Malanyu District, Hebei Province, China
Data-driven prospectivity modeling based on deep learning, particularly supervised learning, has demonstrated outstanding performance for mineral...
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Revealing Geochemical Patterns Associated with Mineralization Using t-Distributed Stochastic Neighbor Embedding and Random Forest
The identification of multivariate geochemical anomalies is critical in mineral exploration. Machine learning algorithms have been successfully...
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A Monte Carlo-based Workflow for Geochemical Anomaly Identification Under Uncertainty and Global Sensitivity Analysis of Model Parameters
Uncertainty associated with the identification of geochemical anomalies linked to mineralization has been a major concern in processing geochemical...
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Geological Map** Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data
Geochemical map** based on machine learning algorithms has been proven to significantly improve the efficiency of geological map** related to...
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Devonian palaeoenvironmental dynamics in Colombia: An integrated sedimentological and geochemical exploration
During the Devonian Period, many marine basins around the world reflected remarkably high global sea levels. In this context, the Devonian...
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Automated machine learning pipeline for geochemical analysis
Biplot diagrams are traditionally used for rock discrimination using geochemical data from samples. However, this approach has limitations when...
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Geochemical Characterization of Organic Rich Black Rocks of the Niutitang Formation to Reconstruct the Paleoenvironmental Settings during Early Cambrian Period from **angxi Area, Western Hunan, China
The Niutitang Formation in the South China Block might be a source of hydrocarbon as it contains an enormous quantity of organic matter. Black rock...
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Advanced Instruments for Identifying Geochemical Dependences of Radionuclide Migration in Natural Waters
The geochemical dependences of the migration of natural radionuclides (U and Th) in different species are analyzed for the Semipalatinsk test site...
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Identification of Multi-Element Geochemical Anomalies for Cu–Polymetallic Deposits Through Staged Factor Analysis, Improved Fractal Density and Expected Value Function
The success of exploration geochemistry requires identification of multi-element geochemical signatures. It has been revealed that Zhongdian island...
