31 citations as recorded by crossref.

1. Application of hierarchical clustering, singularity mapping, and Kohonen neural network to identify Ag-Au-Pb-Zn polymetallic mineralization associated geochemical anomaly in Pangxidong district X. Yu et al. https://doi.org/10.1016/j.gexplo.2019.04.007
2. 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
3. Identification of Geochemical Anomalies Using an End-to-End Transformer S. Yu et al. https://doi.org/10.1007/s11053-024-10334-4
4. Exploratory data analysis and singularity mapping in geochemical anomaly identification in Karamay, Xinjiang, China Z. Shuguang et al. https://doi.org/10.1016/j.gexplo.2014.12.007
5. Fractal/multifractal modeling of geochemical data: A review R. Zuo & J. Wang https://doi.org/10.1016/j.gexplo.2015.04.010
6. A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas R. Zuo et al. https://doi.org/10.1016/j.apgeochem.2013.02.009
7. Mapping local singularities using magnetic data to investigate the volcanic rocks of the Qikou depression, Dagang oilfield, eastern China G. Chen et al. https://doi.org/10.5194/npg-20-501-2013
8. Identification of geochemical anomalies through combined sequential Gaussian simulation and grid-based local singularity analysis J. Wang & R. Zuo https://doi.org/10.1016/j.cageo.2018.05.010
9. A batch sliding window method for local singularity mapping and its application for geochemical anomaly identification F. Xiao et al. https://doi.org/10.1016/j.cageo.2015.11.001
10. The singularity index for soil geochemical variables, and a mixture model for its interpretation R. Lark et al. https://doi.org/10.1016/j.geoderma.2018.02.032
11. ArcFractal: An ArcGIS Add-In for Processing Geoscience Data Using Fractal/Multifractal Models R. Zuo & J. Wang https://doi.org/10.1007/s11053-019-09513-5
12. Mineral prospectivity modelling using singularity mapping and multifractal analysis of stream sediment geochemical data from the auriferous Hutti-Maski schist belt, S. India S. Behera & M. Panigrahi https://doi.org/10.1016/j.oregeorev.2021.104029
13. A quadtree-based batch method for local singularity mapping and application for geochemical anomaly identification F. Xu et al. https://doi.org/10.1007/s12517-021-08393-5
14. 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
15. 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
16. Identifying gravity anomalies caused by granitic intrusions in Nanling mineral district, China: a multifractal perspective G. Chen et al. https://doi.org/10.1111/1365-2478.12187
17. Multi-parameter Analysis of Local Singularity Mapping and Its Application to Identify Geochemical Anomalies in the Xishan Gold Deposit, North China S. Xu et al. https://doi.org/10.1007/s11053-020-09669-5
18. Combination of Kriging methods and multi-fractal analysis for estimating spatial distribution of geotechnical parameters C. Wang & H. Zhu https://doi.org/10.1007/s10064-015-0742-9
19. Time-scale energy based analysis of contours of real-world shapes V. Bruni et al. https://doi.org/10.1016/j.matcom.2010.11.013
20. Multifractals and geostatistics F. Agterberg https://doi.org/10.1016/j.gexplo.2012.04.001
21. Identification of REE mineralization-related geochemical anomalies using fractal/multifractal methods in the Nanling belt, South China Y. Liu et al. https://doi.org/10.1007/s12665-014-3385-4
22. 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
23. Mineral exploration and regional surface geochemical datasets: An anomaly detection and k-means clustering exercise applied on laterite in Western Australia M. Gonçalves et al. https://doi.org/10.1016/j.gexplo.2024.107400
24. 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
25. Assessment of Geochemical Anomaly Uncertainty Through Geostatistical Simulation and Singularity Analysis Y. Liu et al. https://doi.org/10.1007/s11053-018-9388-1
26. Unveiling Temporal Cyclicities in Seismic b-Values and Major Earthquake Events in Japan by Local Singularity Analysis and Wavelet Methods S. Li et al. https://doi.org/10.3390/fractalfract8060359
27. Delimiting geochemical anomalies in the exploration of covered deposits with multifractal methods and using stream sediment data from the Iberian Pyrite Belt, Southwest Iberia M. Gonçalves & A. Mateus https://doi.org/10.1016/j.oregeorev.2019.103018
28. An improved solution of local window parameters setting for local singularity analysis based on Excel VBA batch processing technology D. Zhang et al. https://doi.org/10.1016/j.cageo.2015.12.012
29. Anisotropic singularity: A novel way to characterize controlling effects of geological processes on mineralization W. Wang et al. https://doi.org/10.1016/j.gexplo.2017.07.019
30. 3D Mineral Prospectivity Mapping of Zaozigou Gold Deposit, West Qinling, China: Machine Learning-Based Mineral Prediction Y. Kong et al. https://doi.org/10.3390/min12111361
31. Uncertainty Analysis of Geochemical Anomaly by Combining Sequential Indicator Co-simulation and Local Singularity Analysis Y. Liu & E. Carranza https://doi.org/10.1007/s11053-021-10001-y