78 citations as recorded by crossref.

1. Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas Q. Cheng https://doi.org/10.1016/j.gexplo.2012.07.007
2. Catchment basin modelling of stream sediment anomalies revisited: incorporation of EDA and fractal analysis E. Carranza https://doi.org/10.1144/1467-7873/09-224
3. Developments in Quantitative Assessment and Modeling of Mineral Resource Potential: An Overview Y. Liu et al. https://doi.org/10.1007/s11053-022-10075-2
4. Hydrothermal mineralising systems as chemical reactors: Wavelet analysis, multifractals and correlations A. Ord et al. https://doi.org/10.1016/j.oregeorev.2016.03.026
5. Fractal/multifractal modeling of geochemical data: A review R. Zuo & J. Wang https://doi.org/10.1016/j.gexplo.2015.04.010
6. Can multifractals be used for mineral resource appraisal? F. Agterberg https://doi.org/10.1016/j.gexplo.2017.06.022
7. Application of Singularity Theory to the Distribution of Heavy Metals in Surface Sediments of the Zhongsha Islands Y. Zhang et al. https://doi.org/10.3390/jmse10111697
8. GIS-based prospectivity-mapping based on geochemical multivariate analysis technology: A case study of MVT Pb–Zn deposits in the Huanyuan-Fenghuang district, northwestern Hunan Province, China K. Wang et al. https://doi.org/10.1016/j.oregeorev.2017.09.015
9. Singularity analysis of ore-mineral and toxic trace elements in stream sediments Q. Cheng & F. Agterberg https://doi.org/10.1016/j.cageo.2008.02.034
10. Application of a multi-fractal model for identification of Cu, Au and Zn anomalies in Western Yunnan, Southwestern China J. Huang & P. Zhao https://doi.org/10.1144/geochem2014-286
11. Blind Source Separation of Spectrally Filtered Geochemical Signals to Recognize Multi-depth Ore-Related Enrichment Patterns S. Esmaeiloghli et al. https://doi.org/10.1007/s11004-023-10101-w
12. Data-Driven Index Overlay and Boolean Logic Mineral Prospectivity Modeling in Greenfields Exploration M. Yousefi & E. Carranza https://doi.org/10.1007/s11053-014-9261-9
13. A spatially weighted singularity mapping method applied to identify epithermal Ag and Pb-Zn polymetallic mineralization associated geochemical anomaly in Northwest Zhejiang, China F. Xiao et al. https://doi.org/10.1016/j.gexplo.2017.03.017
14. Singularity analysis of global zircon U-Pb age series and implication of continental crust evolution Q. Cheng https://doi.org/10.1016/j.gr.2017.07.011
15. Applying Random Forests to Address the Problem of Elemental Background Variation in Stream Sediments: Case Study from Leiyang, SE China . Qinghao Zhang et al. https://doi.org/10.1134/S0016702924602079
16. Towards a new potential field theory of fractal objects M. Mostafa https://doi.org/10.1007/s12303-010-0030-4
17. Multifractal analysis of geochemical anomalies: A tool for assessing prospectivity at the SE border of the Ossa Morena Zone, Variscan Massif (Spain) M. Arias et al. https://doi.org/10.1016/j.gexplo.2012.08.007
18. The multifractal nature of the Ni geochemical field and implications for potential Ni mineral resources in the Huangshan–Jing'erquan area, Xinjiang, China D. Lou et al. https://doi.org/10.1016/j.gexplo.2015.06.012
19. Fractal/multifractal modelling of geochemical exploration data R. Zuo et al. https://doi.org/10.1016/j.gexplo.2012.09.009
20. Application of SVD combined with PCA in delineation and evaluation of ore-prospecting targets in the Gejiu tin polymetallic cluster region, SW China A. Zheng et al. https://doi.org/10.1016/j.oregeorev.2023.105571
21. Introduction to the Special Issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration A. Porwal & E. Carranza https://doi.org/10.1016/j.oregeorev.2015.04.017
22. Maximum Entropy and Random Forest Modeling of Mineral Potential: Analysis of Gold Prospectivity in the Hezuo–Meiwu District, West Qinling Orogen, China S. Zhang et al. https://doi.org/10.1007/s11053-018-9425-0
23. Application of fractal content-gradient method for delineating geochemical anomalies associated with copper occurrences in the Yangla ore field, China Z. Chen et al. https://doi.org/10.1016/j.gsf.2015.11.010
24. Mapping of anomalies in continuous and discrete fields of stream sediment geochemical landscapes E. Carranza https://doi.org/10.1144/1467-7873/09-223
25. Generalized binomial multiplicative cascade processes and asymmetrical multifractal distributions Q. Cheng https://doi.org/10.5194/npg-21-477-2014
26. CONVERGENCE INVESTIGATION OF MULTIFRACTAL ANALYSIS BASED ON Lp-NORM CONSTRAINT J. WANG et al. https://doi.org/10.1142/S0218348X2250116X
27. Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders S. Esmaeiloghli et al. https://doi.org/10.1016/j.chemer.2024.126111
28. Innovative monitoring tools for the complex spatial dynamics of river chemistry: case study for the Alpine region C. Gozzi et al. https://doi.org/10.1007/s12665-018-7756-0
29. Is Compositional Data Analysis (CoDA) a theory able to discover complex dynamics in aqueous geochemical systems? R. Sauro Graziano et al. https://doi.org/10.1016/j.gexplo.2020.106465
30. Singularity analysis of magmatic flare-ups caused by India – Asia collisions Q. Cheng https://doi.org/10.1016/j.gexplo.2017.08.012
31. Research on ore prospecting prediction based on maximum entropy model Z. Chen https://doi.org/10.1007/s12517-023-11814-2
32. Non-Linear Theory and Power-Law Models for Information Integration and Mineral Resources Quantitative Assessments Q. Cheng https://doi.org/10.1007/s11004-008-9172-6
33. Identification of ore-finding targets using the anomaly components of ore-forming element associations extracted by SVD and PCA in the Jiaodong gold cluster area, Eastern China Y. Chen et al. https://doi.org/10.1016/j.oregeorev.2022.104866
34. A new method for extraction of alteration information using the Landsat 8 imagery in a heavily vegetated and sediments‐covered region: A case study from Zhejiang Province, E. China L. Han et al. https://doi.org/10.1002/gj.2988
35. Application of spectrum–area fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type Cu deposit, Iran P. Afzal et al. https://doi.org/10.1016/j.chemer.2013.08.001
36. Analysis of the fractal clustering of ore deposits in the Spanish Iberian Pyrite Belt P. Gumiel et al. https://doi.org/10.1016/j.oregeorev.2010.08.001
37. The Geographical Pattern of Local Statistical Dispersion of Environmental Radon in Europe P. Bossew https://doi.org/10.1007/s11004-023-10073-x
38. Statistical Distribution Laws for Metallic Mineral Deposit Sizes A. Patiño Douce https://doi.org/10.1007/s11053-016-9297-0
39. Measuring the change under compositional data analysis (CoDA): Insight on the dynamics of geochemical systems A. Buccianti et al. https://doi.org/10.1016/j.gexplo.2017.05.006
40. Singularity mapping and spatially weighted principal component analysis to identify geochemical anomalies associated with Ag and Pb-Zn polymetallic mineralization in Northwest Zhejiang, China F. Xiao et al. https://doi.org/10.1016/j.gexplo.2012.04.010
41. Fractal projection pursuit classification model applied to geochemical survey data F. Xiao & J. Chen https://doi.org/10.1016/j.cageo.2011.10.019
42. Frequency Distributions of Geochemical Data, Scaling Laws, and Properties of Compositions A. Buccianti https://doi.org/10.1007/s00024-014-0963-z
43. Fractal Derivatives and Singularity Analysis of Frequency—Depth Clusters of Earthquakes along Converging Plate Boundaries Q. Cheng https://doi.org/10.3390/fractalfract7100721
44. Pareto-Lognormal Modeling of Known and Unknown Metal Resources. II. Method Refinement and Further Applications F. Agterberg https://doi.org/10.1007/s11053-017-9327-6
45. Delineation of geochemical anomalies using factor analysis and multifractal modeling based on stream sediments data in Sarajeh 1:100,000 sheet, Central Iran M. Meigoony et al. https://doi.org/10.1007/s12517-013-1074-3
46. Distribution patterns of petroleum indices based on multifractal and spatial PCA L. Lei et al. https://doi.org/10.1016/j.petrol.2018.07.081
47. Elucidating the complexity of metallogenic elements based on multifractal detrending moving average analysis L. Wan et al. https://doi.org/10.1016/j.physa.2019.123296
48. A knowledge-driven way to interpret the isometric log-ratio transformation and mixture distributions of geochemical data X. Liu et al. https://doi.org/10.1016/j.gexplo.2019.106417
49. Fractal density modeling of crustal heterogeneity from the KTB deep hole G. Chen & Q. Cheng https://doi.org/10.1002/2016JB013684
50. Extraction of Regional and Local Anomalies of Element Group in the Ailaoshan Tectonic Zone, Southwestern China X. Qi et al. https://doi.org/10.3390/min16060638
51. A new method for geochemical anomaly separation based on the distribution patterns of singularity indices Y. Liu et al. https://doi.org/10.1016/j.cageo.2017.05.008
52. Modelling magnetic anomalies of solid and fractal bodies with defined boundaries using the finite cube elements method M. Mostafa https://doi.org/10.1111/j.1365-246X.2008.04071.x
53. New Insights into Element Distribution Patterns in Geochemistry: A Perspective from Fractal Density Y. Liu et al. https://doi.org/10.1007/s11053-018-9374-7
54. A multifractal simulation model for the distribution of VMS deposits in the Spanish segment of the Iberian Pyrite Belt M. Arias et al. https://doi.org/10.1016/j.cageo.2011.07.012
55. Multifractals and geostatistics F. Agterberg https://doi.org/10.1016/j.gexplo.2012.04.001
56. Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications L. Wang et al. https://doi.org/10.5194/hess-19-4001-2015
57. Pareto–Lognormal Modeling of Known and Unknown Metal Resources F. Agterberg https://doi.org/10.1007/s11053-016-9305-4
58. Fractal modeling of oil and gas geochemical data in the Taiwan Strait Basin Y. Zhang et al. https://doi.org/10.1016/j.gexplo.2023.107353
59. Metallic Mineral Resources in the Twenty-First Century: II. Constraints on Future Supply A. Patiño Douce https://doi.org/10.1007/s11053-015-9265-0
60. Weathering reactions and isometric log-ratio coordinates: Do they speak to each other? A. Buccianti & R. Zuo https://doi.org/10.1016/j.apgeochem.2016.08.007
61. Using multifractal modelling, singularity mapping, and geochemical indexes for targeting buried mineralization: Application to the W-Sn Panasqueira ore-system, Portugal M. Gonçalves et al. https://doi.org/10.1016/j.gexplo.2017.07.008
62. Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang, China J. Wang et al. https://doi.org/10.1016/j.apgeochem.2020.104679
63. Multifractal analysis of stream sediment geochemical data: Implications for hydrothermal nickel prospection in an arid terrain, eastern Iran M. Parsa et al. https://doi.org/10.1016/j.gexplo.2016.11.013
64. Multifractal characteristics of gold grades series in the Dayingezhuang Deposit, Jiaodong Gold Province, China L. Wan et al. https://doi.org/10.1007/s12145-015-0218-2
65. Fractal-Based Wavelet Filter for Separating Geophysical or Geochemical Anomalies from Background G. Chen & Q. Cheng https://doi.org/10.1007/s11004-017-9707-9
66. Application of anomaly components of multi-element combination extracted from stream sediments data by BEMD in mapping ore prospectivity within the JGCR, Eastern China A. Zheng et al. https://doi.org/10.1016/j.apgeochem.2024.106034
67. Spatial analysis and visualization of exploration geochemical data R. Zuo et al. https://doi.org/10.1016/j.earscirev.2016.04.006
68. An expanded de Wijs model for multifractal analysis of mineral production data A. Ford & T. Blenkinsop https://doi.org/10.1007/s00126-008-0213-3
69. Remote-sensing ore prediction in and around the Linghou copper-polymetal deposit, southeastern China F. Yang et al. https://doi.org/10.1088/1755-1315/69/1/012020
70. Mapping geochemical singularity using multifractal analysis: Application to anomaly definition on stream sediments data from Funin Sheet, Yunnan, China J. Bai et al. https://doi.org/10.1016/j.gexplo.2009.09.002
71. Determination of lithium prospects using fractal modeling and staged factor analysis in Torud region, NE Iran N. Fyzollahhi et al. https://doi.org/10.1016/j.gexplo.2017.09.017
72. Vertical distribution of elements in regolith over mineral deposits and implications for mapping geochemical weak anomalies in covered areas Q. Cheng https://doi.org/10.1144/geochem2012-174
73. Identifying geochemical anomaly through spatially anisotropic singularity mapping: A case study from silver-gold deposit in Pangxidong district, SE China F. Xiao et al. https://doi.org/10.1016/j.gexplo.2019.106453
74. Application of principal component analysis and a spectrum-area fractal model to identify geochemical anomalies associated with vanadium mineralization in northeastern Jiangxi Province, South China H. Li et al. https://doi.org/10.1144/geochem2021-090
75. Infomax-based deep autoencoder network for recognition of multi-element geochemical anomalies linked to mineralization S. Esmaeiloghli et al. https://doi.org/10.1016/j.cageo.2023.105341
76. Extraction of gravity components related to W-Sn polymetallic deposits using Bi-dimensional empirical mode decomposition (BEMD) in the Laojunshan Ore Cluster Region, SW China X. Zhu et al. https://doi.org/10.1016/j.jappgeo.2023.105259
77. Fractal-domain transformer based on learnable multifractal spectrum for chaotic systems classification G. Xiong et al. https://doi.org/10.1016/j.physa.2024.130276
78. A Novel Multifractal Method for Geochemical Element Distribution Analysis M. Zhao et al. https://doi.org/10.1007/s11053-024-10450-1