On the public citation graph
A nonlinear controlling function of geological features on magmatic–hydrothermal mineralization
0 signed · 100 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s00531-014-1096-4openalex-snapshot
The mineralization age of the Makeng Fe deposit, South China: implications from U–Pb and Sm–Nd geochronology
inferredcites →https://doi.org/10.1007/s11053-008-9067-8openalex-snapshot
Are Fractal Dimensions of the Spatial Distribution of Mineral Deposits Meaningful?
inferredcites →https://doi.org/10.1007/s12583-013-0305-6openalex-snapshot
Fractals and spatial statistics of point patterns
inferredcites →https://doi.org/10.1007/s12583-015-0597-9openalex-snapshot
Spatial analysis of Fe deposits in Fujian Province, China: Implications for mineral exploration
inferredcites →https://doi.org/10.1016/j.cageo.2008.02.034openalex-snapshot
Singularity analysis of ore-mineral and toxic trace elements in stream sediments
inferredcites →https://doi.org/10.1016/j.jag.2009.07.001openalex-snapshot
Fractal characterization of the spatial distribution of geological point processes
inferredcites →https://doi.org/10.1016/j.oregeorev.2006.10.002openalex-snapshot
Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China
inferredcites →https://doi.org/10.1016/j.oregeorev.2014.08.010openalex-snapshot
Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm
inferredcites →https://doi.org/10.1016/j.oregeorev.2014.09.024openalex-snapshot
Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China
inferredcites →https://doi.org/10.1016/s0169-1368(02)00111-7openalex-snapshot
Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines
inferredcites →https://doi.org/10.1023/a:1010147818806openalex-snapshot
Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines
inferredcites →https://doi.org/10.1023/a:1014416319335openalex-snapshot
Spatial Association of Mineral Occurrences and Curvilinear Geological Features
inferredcites →https://doi.org/10.1038/srep19167openalex-snapshot
Fractal density and singularity analysis of heat flow over ocean ridges
inferredcites →https://doi.org/10.1126/science.156.3775.636openalex-snapshot
How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension
inferredcites →https://doi.org/10.1126/science.245.4913.76openalex-snapshot
Computer Programs for Mineral Exploration
inferredcites →https://doi.org/10.1130/0091-7613(1991)019<0111:sdood>2.3.co;2openalex-snapshot
Spatial distribution of ore deposits
inferredcites →https://doi.org/10.4095/128059openalex-snapshot
Weights of evidence modelling: a new approach to mapping mineral potential
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/pssa.202400629openalex-snapshot
High Coercivity and Strong [00l] Crystallographic Texture in Sm(CoFeCuZr)7.4 Nanoflakes Prepared by Surfactant‐Assisted Ball Milling
inferred← citeshttps://doi.org/10.1007/s11004-021-09979-1openalex-snapshot
A Physically Constrained Variational Autoencoder for Geochemical Pattern Recognition
inferred← citeshttps://doi.org/10.1007/s11004-023-10059-9openalex-snapshot
Deep Reinforcement Learning for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11004-023-10076-8openalex-snapshot
An Interpretable Graph Attention Network for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11004-023-10133-2openalex-snapshot
Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability
inferred← citeshttps://doi.org/10.1007/s11004-024-10141-wopenalex-snapshot
Geologically Constrained Convolutional Neural Network for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11004-025-10190-9openalex-snapshot
Geological Knowledge-Embedding Transfer-Learning Architecture for Geochemical Anomaly Identification
inferred← citeshttps://doi.org/10.1007/s11004-025-10191-8openalex-snapshot
Self-Supervised Graph Contrastive Learning for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11004-025-10213-5openalex-snapshot
Improving Geochemical Anomaly Recognition Associated with Mineralization via Geological Knowledge Interacting Reinforcement Learning
inferred← citeshttps://doi.org/10.1007/s11004-025-10218-0openalex-snapshot
Coupling Graph Attention Networks with Causal Discovery for Geochemical Anomaly Recognition
inferred← citeshttps://doi.org/10.1007/s11004-026-10297-7openalex-snapshot
Geologically Constrained Multi-scale Graph Convolutional Network Modeling of Stream Sediment Geochemical Data
inferred← citeshttps://doi.org/10.1007/s11004-026-10332-7openalex-snapshot
Improving the Understanding of AI-driven Geochemical Exploration: Combining Correlation-Augmentation Graph Reinforcement Learning and Inverse Reinforcement Learning
inferred← citeshttps://doi.org/10.1007/s11004-026-10351-4openalex-snapshot
MPM-KAN: An Interpretable Data-Driven Framework for Mineral Prospectivity Mapping Based on Kolmogorov–Arnold Networks
inferred← citeshttps://doi.org/10.1007/s11053-017-9357-0openalex-snapshot
Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods
inferred← citeshttps://doi.org/10.1007/s11053-019-09565-7openalex-snapshot
Geochemical Anomaly and Mineral Prospectivity Mapping for Vein-Type Copper Mineralization, Kuhsiah-e-Urmak Area, Iran: Application of Sequential Gaussian Simulation and Multivariate Regression Analysis
inferred← citeshttps://doi.org/10.1007/s11053-020-09658-8openalex-snapshot
Weighted Photolineaments Factor (WPF): An Enhanced Method to Generate a Predictive Structural Evidential Map with Low Uncertainty, a Case Study in Chahargonbad Area, Iran
inferred← citeshttps://doi.org/10.1007/s11053-021-09956-9openalex-snapshot
Spatial Association Between Orogenic Gold Mineralization and Structures Revealed by 3D Prospectivity Modeling: A Case Study of the Xiadian Gold Deposit, Jiaodong Peninsula, China
inferred← citeshttps://doi.org/10.1007/s11053-021-09979-2openalex-snapshot
Mineral Prospectivity Mapping via Gated Recurrent Unit Model
inferred← citeshttps://doi.org/10.1007/s11053-022-10050-xopenalex-snapshot
A Geologically Constrained Variational Autoencoder for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11053-022-10080-5openalex-snapshot
Visual Interpretable Deep Learning Algorithm for Geochemical Anomaly Recognition
inferred← citeshttps://doi.org/10.1007/s11053-022-10142-8openalex-snapshot
Mineral Prospectivity Mapping Using Deep Self-Attention Model
inferred← citeshttps://doi.org/10.1007/s11053-023-10237-wopenalex-snapshot
A New Generation of Artificial Intelligence Algorithms for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11053-023-10286-1openalex-snapshot
ArcMPM: An ArcEngine-Based Software for Mineral Prospectivity Mapping via Artificial Intelligence Algorithms
inferred← citeshttps://doi.org/10.1007/s11053-024-10321-9openalex-snapshot
Prospectivity and Uncertainty Analysis of Tungsten Polymetallogenic Mineral Resources in the Nanling Metallogenic Belt, South China: A Comparative Study of AdaBoost, GBDT, and XgBoost Algorithms
inferred← citeshttps://doi.org/10.1007/s11053-024-10344-2openalex-snapshot
A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11053-024-10390-wopenalex-snapshot
Mineral Reconnaissance Through Scientific Consensus: First National Prospectivity Maps for PGE–Ni–Cu–Cr and Witwatersrand-type Au Deposits in South Africa
inferred← citeshttps://doi.org/10.1007/s11053-024-10433-2openalex-snapshot
Identification of Geochemical Anomalies Using a Memory-Augmented Autoencoder Model with Geological Constraint
inferred← citeshttps://doi.org/10.1007/s11053-024-10438-xopenalex-snapshot
Pan-Canadian Predictive Modeling of Lithium–Cesium–Tantalum Pegmatites with Deep Learning and Natural Language Processing
inferred← citeshttps://doi.org/10.1007/s11053-025-10510-0openalex-snapshot
Recursive Annotation for Negative Labeling in Data-Driven Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11053-026-10691-2openalex-snapshot
Targeting Sensitivity and Selectivity in Data-Driven Mineral Prospectivity Mapping in Response to Mineral System-Based Geodata Annotation: Experimental Studies Using Pan-Canadian Copper Annotation and Philosophical Implications
inferred← citeshttps://doi.org/10.1007/s11053-026-10760-6openalex-snapshot
The Effects of Smoothing-Induced Weak Annotation in Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.1007/s11430-024-1309-9openalex-snapshot
Explainable artificial intelligence models for mineral prospectivity mapping
inferred← citeshttps://doi.org/10.1007/s11430-025-1622-1openalex-snapshot
Key technology for intelligent mineral prospectivity mapping: Challenges and solutions
inferred← citeshttps://doi.org/10.1007/s12145-026-02206-7openalex-snapshot
Data-driven tectonic prospectivity modeling for granite-hosted uranium systems: integrating empirical scaling and spatial statistics
inferred← citeshttps://doi.org/10.1007/s12517-021-06731-1openalex-snapshot
Geospatial analysis of stream sediment samples for gold and base metal concentration in Daya Dawa, West Guji, Oromia Region, Southern Ethiopia
inferred← citeshttps://doi.org/10.1007/s12583-017-0772-2openalex-snapshot
A fractal measure of spatial association between landslides and conditioning factors
inferred← citeshttps://doi.org/10.1007/s12665-022-10470-1openalex-snapshot
Landslide susceptibility mapping with the integration of information theory, fractal theory, and statistical analyses at a regional scale: a case study of Altay Prefecture, China
inferred← citeshttps://doi.org/10.1016/j.apgeochem.2023.105765openalex-snapshot
Recognition of multivariate geochemical anomalies using a geologically-constrained variational autoencoder network with spectrum separable module – A case study in Shangluo District, China
inferred← citeshttps://doi.org/10.1016/j.apgeochem.2024.106124openalex-snapshot
Data generation for exploration geochemistry: Past, present and future
inferred← citeshttps://doi.org/10.1016/j.apgeochem.2024.106137openalex-snapshot
Geologically constrained unsupervised dual-branch deep learning algorithm for geochemical anomalies identification
inferred← citeshttps://doi.org/10.1016/j.cageo.2017.10.005openalex-snapshot
GIS-based rare events logistic regression for mineral prospectivity mapping
inferred← citeshttps://doi.org/10.1016/j.cageo.2021.104974openalex-snapshot
Mineral prospectivity mapping using a joint singularity-based weighting method and long short-term memory network
inferred← citeshttps://doi.org/10.1016/j.cageo.2023.105490openalex-snapshot
A physically constrained hybrid deep learning model to mine a geochemical data cube in support of mineral exploration
inferred← citeshttps://doi.org/10.1016/j.cageo.2025.105913openalex-snapshot
Geological-knowledge-guided graph self-supervised pretraining framework for identifying mineralization-related geochemical anomalies
inferred← citeshttps://doi.org/10.1016/j.cageo.2025.106090openalex-snapshot
An interpretable causal variational autoencoder for geochemical anomalies recognition by regarding ore-controlling factors
inferred← citeshttps://doi.org/10.1016/j.chemgeo.2019.119458openalex-snapshot
Modeling singular mineralization processes due to fluid pressure fluctuations
inferred← citeshttps://doi.org/10.1016/j.earscirev.2025.105209openalex-snapshot
Recent advances and future research directions in deep learning as applied to geochemical mapping
inferred← citeshttps://doi.org/10.1016/j.gexplo.2017.03.017openalex-snapshot
A spatially weighted singularity mapping method applied to identify epithermal Ag and Pb-Zn polymetallic mineralization associated geochemical anomaly in Northwest Zhejiang, China
inferred← citeshttps://doi.org/10.1016/j.gexplo.2017.06.021openalex-snapshot
Identification of geochemical anomalies via local RX anomaly detector
inferred← citeshttps://doi.org/10.1016/j.gexplo.2017.10.015openalex-snapshot
Characterization and origin of granites from the Luoyang Fe deposit, southwestern Fujian Province, South China
inferred← citeshttps://doi.org/10.1016/j.gexplo.2017.10.020openalex-snapshot
Selection of an elemental association related to mineralization using spatial analysis
inferred← citeshttps://doi.org/10.1016/j.gexplo.2019.106453openalex-snapshot
Identifying geochemical anomaly through spatially anisotropic singularity mapping: A case study from silver-gold deposit in Pangxidong district, SE China
inferred← citeshttps://doi.org/10.1016/j.gexplo.2023.107157openalex-snapshot
Empirical mode decomposition and power spectrum filtering for detection of frequency channels related to multi-scale geochemical anomalies: Metal exploration targeting in Moalleman district, NE Iran
inferred← citeshttps://doi.org/10.1016/j.gexplo.2025.107789openalex-snapshot
Mineral prediction research using the Geology-based Cyclic Adversarial Network for Anomaly Detection
inferred← citeshttps://doi.org/10.1016/j.gsf.2020.02.010openalex-snapshot
A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping
inferred← citeshttps://doi.org/10.1016/j.jafrearsci.2016.11.021openalex-snapshot
Multifractal interpolation and spectrum–area fractal modeling of stream sediment geochemical data: Implications for mapping exploration targets
inferred← citeshttps://doi.org/10.1016/j.jgsce.2023.205176openalex-snapshot
Identificating favourable reservoir areas of coalbed methane based on multifractal and gated circulation unit
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2018.03.008openalex-snapshot
A fractal measure of mass transfer in fluid–rock interaction
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2018.10.006openalex-snapshot
Mapping mineral prospectivity through big data analytics and a deep learning algorithm
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2019.02.027openalex-snapshot
Mapping geochemical anomalies related to Fe–polymetallic mineralization using the maximum margin metric learning method
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2019.102935openalex-snapshot
Fractal dimension and its translation into a model of gold spatial proxy
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2019.103028openalex-snapshot
Controls on and prospectivity mapping of volcanic-type uranium mineralization in the Pucheng district, NW Fujian, China
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107215openalex-snapshot
WTCNN–Transformer: An unsupervised model for mineral prospectivity mapping based on wavelet transform, convolutional neural network, and transformer
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107425openalex-snapshot
A hybrid gold prospectivity mapping model integrating computer vision and token selective attention: a case study of the As-Safra region
inferred← citeshttps://doi.org/10.1029/2024jh000468openalex-snapshot
Geological Knowledge‐Guided Dual‐Branch Deep Learning Model for Identification of Geochemical Anomalies Related to Mineralization
inferred← citeshttps://doi.org/10.1029/2026jh001368openalex-snapshot
Attention and Geological Knowledge Guided Spectral‐Spatial Networks for Geochemical Anomalies Recognition
inferred← citeshttps://doi.org/10.1038/s41598-017-12181-1openalex-snapshot
Interaction between karst terrain and bauxites: evidence from Quaternary orebody distribution in Guangxi, SW China
inferred← citeshttps://doi.org/10.1038/s41598-024-56644-8openalex-snapshot
Geologically-constrained GANomaly network for mineral prospectivity mapping through frequency domain training data
inferred← citeshttps://doi.org/10.1038/s41598-025-27332-yopenalex-snapshot
Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering of frequency domain information
inferred← citeshttps://doi.org/10.1111/1755-6724.14992openalex-snapshot
Single‐element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis
inferred← citeshttps://doi.org/10.1130/g52970.1openalex-snapshot
A novel data-knowledge dual-driven model coupling artificial intelligence with a mineral systems approach for mineral prospectivity mapping
inferred← citeshttps://doi.org/10.1144/geochem2016-013openalex-snapshot
Identification and mapping of lithogeochemical signatures using staged factor analysis and fractal/multifractal models
inferred← citeshttps://doi.org/10.1144/geochem2016-444openalex-snapshot
Singularity mapping of fracture fills and its relationship to deep concealed orebodies – a case study of the Shaxi porphyry Cu-Au deposit, China
inferred← citeshttps://doi.org/10.1144/geochem2024-002openalex-snapshot
Identification of multi-mineral-species geochemical anomalies using Bayesian maximum entropy and the spectrum separable module-constrained convolutional autoencoder
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1092127/v1openalex-snapshot
Integration of Information Theory, Fractal Theory and Statistical Analyses for Landslide Susceptibility Mapping at Regional Scales
inferred← citeshttps://doi.org/10.3390/min10030233openalex-snapshot
3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China
inferred← citeshttps://doi.org/10.3390/min16030330openalex-snapshot
GeoCLA: An Integrated CNN-BiLSTM-Attention Framework for Geochemical Anomaly Detection in the Hatu Region, Xinjiang
inferred← citeshttps://doi.org/10.3390/mining6040091openalex-snapshot
Hyperparameter Optimization of Convolutional Neural Networks Using the Grey Wolf Optimizer for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.5194/gmd-19-2219-2026openalex-snapshot
Recognizing spatial geochemical anomaly patterns using deformable convolutional networks guided with geological knowledge
inferred← citeshttps://doi.org/10.5194/gmd-19-8755-2026openalex-snapshot
Uncertainty quantification of deep learning algorithms for mineral prospectivity mapping
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.