Skip to main content
Log in

ArcFractal: An ArcGIS Add-In for Processing Geoscience Data Using Fractal/Multifractal Models

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

Fractal and multifractal models, including the concentration-area (C–A) fractal model, spectrum-area (S–A) multifractal model, and local singularity analysis (LSA) method, are widely applied when processing various geoscience datasets. However, there is lack of ArcGIS-based software that contains these popular fractal and multifractal models. Such a situation hinders the popularization and application of fractal and multifractal models. ArcFractal, an easy-to-use ArcGIS add-in for processing geoscience data using fractal and multifractal models, is introduced in this paper. It is developed using C# based on ArcObject for.Net, ArcEngine 10.2, ZedGraph, and Visual Studio 2010. ArcFractal operations require a Windows 7 operating system and ArcGIS Desktop, version 10.2 or higher. The main application of ArcFractal is to determine geochemical threshold/baseline for separating geochemical patterns into anomalous and background components using the C–A, S–A, and LSA techniques. A case study from China Geochemical Baselines Project (CGB) was used to demonstrate the advantage of ArcFractal for processing geochemical exploration data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  • Afzal, P., Fadakar, A. Y., Khakzad, A., Moarefvand, P., & Rashidnejad, O. N. (2011). Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. Journal of Geochemical Exploration,18, 220–232.

    Google Scholar 

  • Agterberg, F. P. (2012). Multifractals and geostatistics. Journal of Geochemical Exploration,122, 113–122.

    Google Scholar 

  • Bai, J., Porwal, A., Hart, C., Ford, A., & Yu, L. (2010). Mapping geochemical singularity using multifractal analysis: Application to anomaly definition on stream sediments data from Funin Sheet, Yunnan, China. Journal of Geochemical Exploration,104, 1–11.

    Google Scholar 

  • Carranza, E. J. M. (2009) Geochemical anomaly and mineral prospectivity mapping in GIS. In Handbook of exploration and environmental geochemistry (Vol. 11). Amsterdam: Elsevier.

  • Carranza, E. J. M., de Souza Filho, C. R., Haddad-Martim, P. M., Nagayoshi, K., & Shimizu, I. (2019). Macro-scale ore-controlling faults revealed by micro-geochemical anomalies. Scientific Reports,9, 4410.

    Google Scholar 

  • Chen, G., & Cheng, Q. (2016). Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration. Computers & Geosciences,87, 56–66.

    Google Scholar 

  • Chen, G., Cheng, Q., Zuo, R., Liu, T., & Xi, Y. (2015). Identifying gravity anomalies caused by granitic intrusions in Nanling mineral district, China: A multifractal perspective. Geophysical Prospecting,63, 256–270.

    Google Scholar 

  • Chen, Z., Cheng, Q., Chen, J., & Xie, S. (2007). A novel iterative approach for mapping local singularities from geochemical data. Nonlinear Processes in Geophysics,14, 317–324.

    Google Scholar 

  • Cheng, Q. (2000a). GeoData Analysis System (GeoDAS) for mineral exploration: User’s guide and exercise manual. Toronto: Material for the TrainingWorkshop on GeoDAS held at York University.

    Google Scholar 

  • Cheng, Q. (2000b). Interpolation by means of multiftractal, kriging and moving average techniques. In GeoCanada 2000, Proceedings of GAC/MAC Meeting, Calgary, AB, Canada [CD-ROM]. 29 May–2 June 2000. Geol. Assoc. Can., St. John’s, NF, Canada.

  • Cheng, Q. (2007). Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews,32, 314–324.

    Google Scholar 

  • Cheng, Q. (2008). Modeling local scaling properties for multiscale mapping. Vadose Zone Journal,7, 525–532.

    Google Scholar 

  • Cheng, Q. (2012). Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration,122, 55–70.

    Google Scholar 

  • Cheng, Q., & Agterberg, F. P. (1996). Muitifractal modeling and spatial statistics. Mathematical Geology,28, 1–16.

    Google Scholar 

  • Cheng, Q., & Agterberg, F. P. (2009). Singularity analysis of ore–mineral and toxic trace elements in stream sediments. Computers & Geosciences,35, 234–244.

    Google Scholar 

  • Cheng, Q., Agterberg, F. P., & Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration,51, 109–130.

    Google Scholar 

  • Cheng, Q., Xu, Y., & Grunsky, E. (1999). Integrated spatial and spectral analysis for geochemical anomaly separation. In Proceedings of the fifth annual conference of the international association for mathematical geology, Trondheim.

  • Cheng, Q., Xu, Y., & Grunsky, E. (2000). Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources Research,9, 43–52.

    Google Scholar 

  • Ge, Y., Cheng, Q., & Zhang, S. (2005). Reduction of edge effects in spatial information extraction from regional geochemical data: A case study based on multifractal filtering technique. Computers & Geosciences,31, 545–554.

    Google Scholar 

  • Gonçalves, M. A., Mateus, A., & Oliveira, V. (2001). Geochemical anomaly separation by multifractal modeling. Journal of Geochemical Exploration,72, 91–114.

    Google Scholar 

  • Gonçalves, M. A., Mateus, A., Pinto, F., & Vieira, R. (2018). Using multifractal modelling, singularity mapping, and geochemical indexes for targeting buried mineralization: Application to the W-Sn Panasqueira ore-system, Portugal. Journal of Geochemical Exploration,189, 42–53.

    Google Scholar 

  • Grunsky, E. C. (2010). The interpretation of geochemical survey data. Geochemistry: Exploration, Environment, Analysis,10, 27–74.

    Google Scholar 

  • Lark, R., Patton, M., Ander, E., & Reay, D. (2018). The singularity index for soil geochemical variables, and a mixture model for its interpretation. Geoderma,323, 83–106.

    Google Scholar 

  • Lima, A., De, Vivo B., Cicchella, D., Cortini, M., & Albanese, S. (2003). Multifractal IDW interpolation and fractal filtering method in environmental studies: An application on regional stream sediments of (Italy), Campania region. Applied Geochemistry,18, 1853–1865.

    Google Scholar 

  • Lima, A., Plant, J. A., De Vivo, B., Tarvainen, T., Albanese, S., & Cicchella, D. (2008). Interpolation methods for geochemical maps: A comparative study using arsenic data from European stream waters. Geochemistry: Exploration, Environment, Analysis,8, 41–48.

    Google Scholar 

  • Lin, X., Wang, X., Zhou, J., Chi, Q., Nie, L., Zhang, B., et al. (2019). Concentrations, variations and distribution of molybdenum (Mo) in catchment outlet sediments of China: Conclusions from the China geochemical baselines project. Applied Geochemistry,103, 50–58.

    Google Scholar 

  • Liu, D., Chi, Q., Chen, Y., Wang, X., Zhou, J., Liu, H., et al. (2019a). Research on the structure and genesis of tungsten geochemical block in south china. Geotectonica et Metallogenia. https://doi.org/10.16539/j.ddgzyckx.2018.06.016 (In Chinese with English abstract).

  • Liu, Y., Cheng, Q., Carranza, E. J. M., & Zhou, K. (2019b). Assessment of geochemical anomaly uncertainty through geostatistical simulation and singularity analysis. Natural Resources Research,28, 199–212.

    Google Scholar 

  • Liu, Y., Xia, Q., & Carranza, E. J. M. (2019c). Integrating sequential indicator simulation and singularity analysis to analyze uncertainty of geochemical anomaly for exploration targeting of tungsten polymetallic mineralization, Nanling belt, South China. Journal of Geochemical Exploration,197, 143–158.

    Google Scholar 

  • Liu, Y., Zhou, K., & Cheng, Q. (2017). A new method for geochemical anomaly separation based on the distribution patterns of singularity indices. Computers & Geosciences,105, 139–147.

    Google Scholar 

  • Luz, F., Mateus, A., Matos, J. X., & Gonçalves, M. A. (2014). Cu- and Zn-soil anomalies in the NE border of the South Portuguese Zone (Iberian Variscides, Portugal) identified by multifractal and geostatistical analyses. Natural Resources Research,23, 195–215.

    Google Scholar 

  • Mao, J., Cheng, Y., Chen, M., & Pirajno, F. (2013). Major types and time–space distribution of Mesozoic ore deposits in South China and their geodynamic settings. Mineralium Deposita,48, 267–294.

    Google Scholar 

  • Mao, J., Hongyan, L., Shimazaki, H., Raimbault, L., & Guy, B. (1996). Geology and metallogeny of the Shizhuyuan skarn–greisen deposit, Hunan Province, China. International Geology Reviews,38, 1020–1039.

    Google Scholar 

  • Petrik, A., Jordan, G. Albanese, Lima, A., Rolandi, R., & De Vivo, B. (2018). Spatial pattern analysis of Ni concentration in topsoils in the Campania Region (Italy). Journal of Geochemical Exploration,195, 130–142.

    Google Scholar 

  • Sun, X., Gong, Q., Wang, Q., Yang, L., Wang, C., & Wang, Z. (2010). Application of local singularity model to delineate geochemical anomalies in Xiong’ershan gold and molybdenum ore district, Western Henan province, China. Journal of Geochemical Exploration,107, 21–29.

    Google Scholar 

  • Wang, D., Chen, Z., Huang, G., Wu, G., & Chen, F. (2012). Northwards and westwards prospecting for tungsten and its significance in South China. Geotectonica et Metallogenia,36, 322–329. (in Chinese with English abstract).

    Google Scholar 

  • Wang, W., Cheng, Q., Zhang, S., & Zhao, J. (2018). Anisotropic singularity: A novel way to characterize controlling effects of geological processes on mineralization. Journal of Geochemical Exploration,189, 32–41.

    Google Scholar 

  • Wang, X., Han, Z., Wang, W., Zhang, B., Wu, H., Nie, L., et al. (2019). Continental-scale geochemical survey of lead (Pb) in mainland China’s pedosphere: Concentration, spatial distribution and influences. Applied Geochemistry,100, 55–63.

    Google Scholar 

  • Wang, X., & the CGB Sampling Team. (2015). China geochemical baselines: Sampling methodology. Journal of Geochemical Exploration,148, 25–39.

    Google Scholar 

  • Wang, W., Zhao, J., & Cheng, Q. (2013). Fault trace–oriented singularity mapping technique to characterize anisotropic geochemical signatures in Gejiu mineral district, China. Journal of Geochemical Exploration,134, 27–37.

    Google Scholar 

  • Wang, J., & Zuo, R. (2015). A MATLAB-based program for processing geochemical data using fractal/multifractal modeling. Earth Science Informatics,8, 937–947.

    Google Scholar 

  • Wang, J., & Zuo, R. (2018). Identification of geochemical anomalies through combined sequential Gaussian simulation and grid-based local singularity analysis. Computers & Geosciences,118, 52–64.

    Google Scholar 

  • Wang, J., & Zuo, R. (2019). Recognizing geochemical anomalies via stochastic simulation-based local singularity analysis. Journal of Geochemical Exploration,198, 29–40.

    Google Scholar 

  • Xia, Q., Wang, X., Liu, Z., Li, T., Xiao, W., Feng, L., et al. (2017). Mineral potential prediction of W mineral deposits in China (p. 305). New York: Geological Press.

    Google Scholar 

  • Xiao, F., Chen, Z., Chen, J., & Zhou, Y. (2016). A batch sliding window method for local singularity mapping and its application for geochemical anomaly identification. Computers & Geosciences,90, 189–201.

    Google Scholar 

  • Xiao, F., Chen, J., Hou, W., Wang, Z., Zhou, Y., & Erten, O. (2018). A spatially weighted singularity mapping method applied to identify epithermal Ag and Pb-Zn polymetallic mineralization associated geochemical anomaly in Northwest Zhejiang, China. Journal of Geochemical Exploration,189, 122–137.

    Google Scholar 

  • Xiao, F., Wang, C., Chen, J., Zhang, Z., Wu, G., & Agterberg, F. P. (2012). Singularity mapping and spatially weighted principal component analysis to identify geochemical anomalies associated with Ag and Pb-Zn polymetallic mineralization in northwest Zhejiang, China. Journal of Geochemical Exploration,122, 90–100.

    Google Scholar 

  • Xie, S., Cheng, Q., Chen, G., Chen, Z., & Bao, Z. (2007). Application of local singularity in prospecting potential oil/gas targets. Nonlinear Processes in Geophysics,14, 285–292.

    Google Scholar 

  • Zaw, K., Peters, S. G., Cromie, P., Burrett, C., & Hou, Z. (2007). Nature, diversity of deposit types and metallogenic relations of South China. Ore Geology Reviews,31, 3–47.

    Google Scholar 

  • Zhang, D., Cheng, Q., Agterberg, F. P., & Chen, Z. (2016). An improved solution of local window parameters setting for local singularity analysis based on Excel VBA batch processing technology. Computers & Geosciences,88, 54–66.

    Google Scholar 

  • Zuo, R. (2011a). Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China. Applied Geochemistry,26, S271–S273.

    Google Scholar 

  • Zuo, R. (2011b). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). Journal of Geochemical Exploration,111, 13–22.

    Google Scholar 

  • Zuo, R., & Cheng, Q. (2008). Mapping singularities—A technique to identify potential Cu mineral deposits using sediment geochemical data, an example for Tibet, west China. Mineralogical Magazine,72, 531–534.

    Google Scholar 

  • Zuo, R., Cheng, Q., Agterberg, F. P., & Xia, Q. (2009). Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration,101, 225–235.

    Google Scholar 

  • Zuo, R., & Wang, J. (2016). Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration,164, 33–41.

    Google Scholar 

  • Zuo, R., Wang, J., Chen, G., & Yang, M. (2015). Identification of weak anomalies: A multifractal perspective. Journal of Geochemical Exploration,148, 12–24.

    Google Scholar 

  • Zuo, R., Xia, Q., & Zhang, D. (2013). A comparison study of the C-A and S–A models with singularity analysis to identify geochemical anomalies in covered areas. Applied Geochemistry,33, 165–172.

    Google Scholar 

Download references

Acknowledgments

Thanks are due to Prof. John Carranza, Editor-in-Chief for Natural Resources Research, Dr. Pablo Gumiel and two anonymous reviewers for their comments and suggestions, which helped us improve this paper. This study was supported jointly by the National Natural Science Foundation of China (41772344), the Natural Science Foundation of Hubei Province (China) (2017CFA053), and the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (MSFGPMR03–3).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Renguang Zuo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zuo, R., Wang, J. ArcFractal: An ArcGIS Add-In for Processing Geoscience Data Using Fractal/Multifractal Models. Nat Resour Res 29, 3–12 (2020). https://doi.org/10.1007/s11053-019-09513-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-019-09513-5

Keywords

Navigation