Skip to main content

Advertisement

SpringerLink
Log in

Predictive Modeling of Prospectivity for VHMS Mineral Deposits, Northeastern Bathurst Mining Camp, NB, Canada, Using an Ensemble Regularization Technique

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

Abstract

The volcanic-hosted massive sulfide (VHMS) deposits of the Bathurst Mining Camp have been significant contributors to Canada's historic Zn, Pb, Cu, and Ag production. Most of these deposits are hosted by the Tetagouche Group, many of which occur at the top of the Nepisiguit Falls Formation (footwall) along the contact with the conformably overlying Flat Landing Brook Formation (hanging wall), i.e., the Brunswick belt. Exploration along this prolific belt can benefit greatly from predictive modeling of prospectivity for VHMS deposits using machine learning (ML)-based mineral prospectivity mapping (MPM), which is the chief objective of this study. However, ML-aided MPM of this belt is faced with three challenges: (i) weak geochemical and geophysical signatures of VHMS deposits (i.e., poor predictor variables) owing to the complex tectono-stratigraphy of the host sequence and extensive glacial cover, (ii) over-fitting stemming from its limited number of VHMS deposits, and (iii) stochastic uncertainties of predictive models linked to the diversity in local geological settings of various VHMS deposits. This study adopted an ensemble regularized regression methodology combining ensemble modeling with LASSO, Ridge, and Elastic Net regularized regression techniques for addressing the above challenges. Herein, we demonstrate that the adopted framework can reduce the severity of over-fitting, handle poor predictor variables, and mitigate the effects of stochastic uncertainties in ML-based MPM. These results are followed by discussions of the pros and cons of the framework adopted in this study.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  • Agterberg, F. P., & Bonham-Carter, G. F. (2005). Measuring the performance of mineral-potential maps. Natural Resources Research, 14(1), 1–17.

    Article  Google Scholar 

  • Ahrens, A., Hansen, C. B., & Schaffer, M. E. (2020). LASSO pack: model selection and prediction with regularized regression in Stata. The Stata Journal, 20(1), 176–235.

    Article  Google Scholar 

  • Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B (Methodological), 44, 139–160.

    Google Scholar 

  • Belland, M. (1993). The birth of the Bathurst mining camp. I. Discovery and development of the Austin brook iron mine. Canadian Mining and Metallurgical Bulletin (Canada), 86(968), 18.

    Google Scholar 

  • Brandmeier, M., Cabrera Zamora, I. G., Nykänen, V., & Middleton, M. (2020). Boosting for mineral prospectivity modeling: a new GIS toolbox. Natural Resources Research, 29(1), 71–88.

    Article  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.

    Article  Google Scholar 

  • Carranza, E. J. M. (2009). Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features. Ore Geology Reviews, 35(3–4), 383–400.

    Article  Google Scholar 

  • Carranza, E. J. M., Hale, M., & Faassen, C. (2008). Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore geology reviews, 33(3–4), 536–558.

    Article  Google Scholar 

  • Chen, Y., Lu, L., & Li, X. (2014). Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly. Journal of Geochemical Exploration, 140, 56–63.

    Article  Google Scholar 

  • Chen, Y., & Wu, W. (2017). Mapping mineral prospectivity using an extreme learning machine regression. Ore Geology Reviews, 80, 200–213.

    Article  Google Scholar 

  • Chen, Y., Zhao, Q., & Lu, L. (2021). Combining the outputs of various k-nearest neighbor anomaly detectors to form a robust ensemble model for high-dimensional geochemical anomaly detection. Journal of Geochemical Exploration, 231, 106875.

    Article  Google Scholar 

  • Chung, C. F., (2003). Use of airborne geophysical surveys for constructing mineral potential maps. In W. D. Goodfellow, S. R. McCutcheon, J. M. Peter, (eds.) Massive Sulfide Deposits of the Bathurst Mining Camp, New Brunswick, and Northern Maine: Economic Geology Monograph 11, Society of Economic Geologists, (pp. 879–891).

  • Cox, D. R., & Snell, E. J. (1989). Analysis of binary data. Routledge.

    Google Scholar 

  • Efron, B. (1992). Bootstrap methods: another look at the jackknife. In Breakthroughs in statistics (pp. 569-593). Springer, New York, NY.

  • Ford, A. (2020). Practical implementation of random forest-based mineral potential mapping for porphyry Cu–Au mineralization in the Eastern Lachlan Orogen, NSW. Australia. Natural Resources Research, 29(1), 267–283.

    Article  Google Scholar 

  • Ford, A., Peters, K. J., Partington, G. A., Blevin, P. L., Downes, P. M., Fitzherbert, J. A., & Greenfield, J. E. (2019). Translating expressions of intrusion-related mineral systems into mappable spatial proxies for mineral potential mapping: Case studies from the Southern New England Orogen. Australia Ore Geology Reviews, 111, 102943.

    Article  Google Scholar 

  • Galley, A.G., Hannington, M.D. and Jonasson, I.R. (2007) Volcanogenic massive sulphide deposits. In: Mineral deposits of Canada: a synthesis of major deposit-types, district metallogeny, the evolution of geological provinces, and exploration methods: Geological Association of Canada, Mineral Deposits Division, Special Publication, 5, (pp.141–161).

  • Gemmell, J. B., Large, R. R., & Zaw, K. (1998). Palaeozoic volcanic-hosted massive sulphides. AGSO Journal of Australian Geology and Geophysics, 17(4), 129–138.

    Google Scholar 

  • Goodfellow, W. D. (2007). Metallogeny of the Bathurst mining camp, northern New Brunswick. Mineral deposits of Canada. A synthesis of major deposit-type, district metallogeny, the evolution of geological provinces and exploration methods. Edited by WD Goodfellow. Geological Association of Canada, Mineral Deposits Division, Special Publication, 5, 449–469.

    Google Scholar 

  • Goodfellow, W. D., & McCutcheon, S. R. (2003). Geological and genetic attributes of volcanic-associated massive sulfide deposits of the Bathurst Mining Camp, northern New Brunswick-a synthesis. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive Sulfide Deposits of the Bathurst Mining Camp, New Brunswick, and Northern Maine: Economic Geology Monograph 11 (pp. 245–301). Society of Economic Geologists.

    Chapter  Google Scholar 

  • Goodfellow, W. D., McCutcheon, S. R., & Peter, J. M. (2003). Massive sulfide deposits of the Bathurst Mining Camp and northern Maine; introduction and summary of findings. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive Sulfide Deposits of the Bathurst Mining Camp, New Brunswick, and Northern Maine: Economic Geology Monograph 11 (pp. 1–16). p: Society of Economic Geologists.

    Chapter  Google Scholar 

  • Goodfellow, W. D., & Peter, J. M. (1999). Sulphur isotope composition of the Brunswick No. 12 massive sulphide deposit, Bathurst Mining Camp, N.B. Implications for Ambient Environment, Sulphur Source and Ore Genesis: Canadian Journal of Earth Sciences, 33, 231–251.

    Google Scholar 

  • Hall, G. E. M., Parkhill, M. A., & Bonham-Carter, G. F. (2003). Conventional and selective leach geochemical exploration methods applied to humus and B horizon soil overlying the Restigouche VMS deposit, Bathurst Mining Camp, New Brunswick. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern Maine: economic geology monograph 11 (pp. 763–782). Newyork p: Society of Economic Geologists.

    Google Scholar 

  • Harris, J. R., Grunsky, E., Behnia, P., & Corrigan, D. (2015). Data-and knowledge-driven mineral prospectivity maps for Canada’s North. Ore Geology Reviews, 71, 788–803.

    Article  Google Scholar 

  • Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44(1), 1–12.

    Article  Google Scholar 

  • Hengl, T. (2006). Finding the right pixel size. Computers & geosciences, 32(9), 1283–1298.

    Article  Google Scholar 

  • Hibbard, J., van Staal, C., Rankin, D., and Williams, D. (2006) Geology, lithotectonic map of the Appalachian Orogen, Canada-United States of America. Geological Survey of Canada. Map 02096A.

  • Hood, P. (1965). Gradient measurements in aeromagnetic surveying. Geophysics, 30(5), 891–902.

    Article  Google Scholar 

  • Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. Wiley.

    Book  Google Scholar 

  • Isles, D. J., & Rankin, L. R. (2013). Geological interpretation of aeromagnetic data. Society of Exploration Geophysicists and Australian Society of Exploration Geophysicists.

    Book  Google Scholar 

  • Jurado, K., Ludvigson, S. C., & Ng, S. (2015). Measuring uncertainty. American Economic Review, 105(3), 1177–1216.

    Article  Google Scholar 

  • Keating, P., Thomas, M. D., & Kiss, F. (2003). Significance of high resolution magnetic and electromagnetic survey for exploration and geological investigations, Bathurst Mining Camp. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive Sulfide deposits of the Bathurst Mining Camp, new Brunswick, and Northern maine: economic geology monograph 11 (pp. 783–798). Society of Economic Geologists.

    Google Scholar 

  • Keykhay-Hosseinpoor, M., Kohsary, A. H., Hossein-Morshedy, A., & Porwal, A. (2020). A machine learning-based approach to exploration targeting of porphyry Cu-Au deposits in the Dehsalm district, eastern Iran. Ore Geology Reviews, 116, 103234.

    Article  Google Scholar 

  • Knox-Robinson, C. M., & Wyborn, L. A. I. (1997). Towards a holistic exploration strategy: using geographic information systems as a tool to enhance exploration. Australian Journal of Earth Sciences, 44(4), 453–463.

    Article  Google Scholar 

  • Kreuzer, O. P., Buckingham, A., Mortimer, J., Walker, G., Wilde, A., & Appiah, K. (2019). An integrated approach to the search for gold in a mature, data-rich brownfields environment: a case study from Sigma-Lamaque. Quebec Ore Geology Reviews, 111, 102977.

    Article  Google Scholar 

  • Kreuzer, O. P., Etheridge, M. A., Guj, P., McMahon, M. E., & Holden, D. J. (2008). Linking mineral deposit models to quantitative risk analysis and decision-making in exploration. Economic Geology, 103(4), 829–850.

    Article  Google Scholar 

  • Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (p. 13). New York: Springer.

    Book  Google Scholar 

  • Large, R. R. (1992). Australian volcanic-hosted massive sulfide deposits; features, styles, and genetic models. Economic Geology, 87(3), 471–510.

    Article  Google Scholar 

  • Lentz, D., and Goodfellow, W. D., (1994). Character, distribution, and origin of zoned hydrothermal alteration features at the Brunswick No. 12 massive sulphide deposit, Bathurst Mining Camp, New Brunswick. In Abbott, S. A., (ed.), Current Research: New Brunswick Department of Natural Resources and Energy, Minerals Resources, Information Circular, 94–1, p. 94–119.

  • Lentz, D. R. (1999). Deformation-induced mass transfer in felsic volcanic rocks hosting the Brunswick No. 6 massive-sulfide deposit, New Brunswick; geochemical effects and petrogenetic implications. The Canadian Mineralogist, 37(2), 489–512.

    Google Scholar 

  • Lentz, D. R. (2002). Sphalerite and arsenopyrite at the Brunswick No. 12 massive-sulfide deposit, Bathurst Camp, New Brunswick: constraints on P-T evolution. The Canadian Mineralogist, 40(1), 19–31.

    Article  Google Scholar 

  • Lentz, D. R., & McCutcheon, S. R. (2006). The Brunswick No. 6 massive sulfide deposit, Bathurst Mining Camp, northern New Brunswick, Canada: a synopsis of the geology and hydrothermal alteration system. Exploration and Mining Geology, 15(3–4), 1–34.

    Article  Google Scholar 

  • Lentz, D. R. (1999b). Petrology, geochemistry, and oxygen isotope interpretation of felsic volcanic and related rocks hosting the Brunswick 6 and 12 massive sulfide deposits (Brunswick Belt), Bathurst mining camp, New Brunswick Canada. Economic Geology, 94(1), 57–86.

    Article  Google Scholar 

  • Lentz, D. R., & Goodfellow, W. D. (1996). Intense silicification of footwall sedimentary rocks in the stockwork alteration zone beneath the Brunswick No. 12 massive sulphide deposit, Bathurst, New Brunswick. Canadian Journal of Earth Sciences, 33(2), 284–302.

    Article  Google Scholar 

  • Lentz, D. R., Hall, D. C., & Hoy, L. D. (1997). Chemostratigraphic, alteration, and oxygen isotopic trends in a profile through the stratigraphic sequence hosting the Health Steele B zone massive sulfide deposit New Brunswick. The Canadian Mineralogist, 35(4), 841–874.

    Google Scholar 

  • Levinson, A. A. (1980). Exploration geochemistry (p. 924). IL: Applied Publishing, Wilmette.

    Google Scholar 

  • Li, T., Zuo, R., Zhao, X., & Zhao, K. (2022). Mapping prospectivity for regolith hosted REE deposits via convolutional neural network with generative adversarial network augmented data. Ore Geology Reviews, 142, 104693.

    Article  Google Scholar 

  • Li, T., Zuo, R., Xiong, Y., & Peng, Y. (2021). Random-drop data augmentation of deep convolutional neural network for mineral prospectivity mapping. Natural Resources Research, 30(1), 27–38.

    Article  Google Scholar 

  • Liu, Y., Zhou, K., & Xia, Q. (2018a). A MaxEnt model for mineral prospectivity mapping. Natural Resources Research, 27(3), 299–313.

    Article  Google Scholar 

  • Liu, Y., Zhou, K., Zhang, N., & Wang, J. (2018b). Maximum entropy modeling for orogenic gold prospectivity mapping in the Tangbale-Hatu belt, western Junggar, China. Ore Geology Reviews, 100, 133–147.

    Article  Google Scholar 

  • Luff, W. M., Goodfellow, W. D., & Juras, S. J. (1992). Evidence for a feeder pipe and associated alteration at Brunswick No. 12 massive-sulfide deposit. Exploration and Mining Geology, 1, 167–185.

    Google Scholar 

  • Marquardt, D. W., & Snee, R. D. (1975). Ridge regression in practice. The American Statistician, 29(1), 3–20.

    Google Scholar 

  • McClenaghan, M.B. and Paulen, R.C., (2018) Application of till mineralogy and geochemistry to mineral exploration. In Past glacial environments (pp. 689–751). Elsevier.

  • McCutcheon, S. R., Luff, W. M., & Boyle, R. W. (2003). The Bathurst Mining Camp, New Brunswick, Canada: history of discovery and evolution of geological models. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern Maine: economic geology monograph 11 (pp. 17–35). Society of Economic Geologists.

    Google Scholar 

  • McCutcheon, S. R., & Walker, J. A. (2019). Great mining camps of Canada 7. The Bathurst mining camp, New Brunswick, part 1: geology and exploration history. Geoscience Canada: journal of the Geological Association of Canada/Geoscience Canada: Journal de l’Association Géologique du Canada, 46(3), 137–154.

    Google Scholar 

  • McCutcheon, S. R., & Walker, J. A. (2020). Great mining camps of Canada 8. The Bathurst mining camp, New Brunswick, part 2: mining history and contributions to society. Geoscience Canada: Journal of the Geological Association of Canada/Geoscience Canada: Journal de l’Association Géologique du Canada, 47(3), 143–166.

    Google Scholar 

  • McKay, G., & Harris, J. R. (2016). Comparison of the data-driven random forests model and a knowledge-driven method for mineral prospectivity mapping: a case study for gold deposits around the Huritz Group and Nueltin Suite, Nunavut, Canada. Natural Resources Research, 25(2), 125–143.

    Article  Google Scholar 

  • Morgan, L. A. (2012). Geophysical characteristics of volcanogenic massive sulfide deposits. Volcanogenic Massive Sulfide Occurrence Model. US Geological Survey, Reston, VA, 115, 131.

    Google Scholar 

  • Ohmoto, H. (1996). Formation of volcanogenic massive sulfide deposits: the Kuroko perspective. Ore Geology Reviews, 10(3–6), 135–177.

    Article  Google Scholar 

  • Parkhill, M. A., & Doiron, A. (2003). Quaternary geology of the Bathurst Mining Camp and implications for base metal exploration using drift prospecting. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern Maine: economic geology monograph 11 (pp. 631–660). Society of Economic Geologists.

    Google Scholar 

  • Parsa, M., & Pour, A. B. (2021). A simulation-based framework for modulating the effects of subjectivity in greenfields’ mineral prospectivity mapping with geochemical and geological data. Journal of Geochemical Exploration, 229, 106838.

    Article  Google Scholar 

  • Parsa, M. (2021). A data augmentation approach to XGboost-based mineral potential mapping: an example of carbonate-hosted ZnPb mineral systems of Western Iran. Journal of Geochemical Exploration, 228, 106811.

    Article  Google Scholar 

  • Parsa, M., & Carranza, E. J. M. (2021). Modulating the impacts of stochastic uncertainties linked to deposit locations in data-driven predictive mapping of mineral prospectivity. Natural Resources Research, 30(5), 3081–3097.

    Article  Google Scholar 

  • Parsa, M., Carranza, E. J. M., & Ahmadi, B. (2022). Deep GMDH neural networks for predictive mapping of mineral prospectivity in terrains hosting few but large mineral deposits. Natural Resources Research, 31(1), 37–50.

    Article  Google Scholar 

  • Peter, J. M., & Goodfellow, W. D. (2003). Hydrothermal sedimentary rocks of the Heath Steele Belt, Bathurst Mining Camp, New Brunswick 3. Application of mineralogy and mineral and bulk composition to massive sulfide exploration. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern maine: economic geology monograph 11 (pp. 417–433). Society of Economic Geologists.

    Google Scholar 

  • Peter, J. M. (2003). Ancient iron formations: their genesis and use in the exploration for stratiform base metal sulphide deposits, with examples from the Bathurst Mining Camp. GeoText, 4, 145–176.

    Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2003). Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province. Western India. Natural Resources Research, 12(3), 155–171.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2006). Bayesian network classifiers for mineral potential mapping. Computers & Geosciences, 32(1), 1–16.

    Article  Google Scholar 

  • Rogers, N., Wodicka, N., McNicoll, V., and van Staal, C.R., (1997). U-Pb ages of Tetagouche group felsic volcanic rocks, northern New Brunswick. In Geological Survey of Canada, Current Research 97-F (pp. 61–69).

  • Rogers, N., van Staal, C. R., McNicoll, V., & Theriault, R. (2003). Volcanology and tectonic setting of the northern Bathurst mining camp: part I-extension and rifting of the Popelogan arc. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern maine: economic geology monograph 11 (pp. 157–179). Society of Economic Geologists.

    Google Scholar 

  • Rogers, N., & van Staal, C. R. (2003). Volcanology and tectonic setting of the northern Bathurst mining camp: Part II-mafic volcanic constraints on back-arc opening. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern Maine: economic geology monograph 11 (pp. 181–201). Society of Economic Geologists.

    Google Scholar 

  • Rose, D.G. and Johnston, S.C, (1990). New Brunswick’s computerized mineral occurrence database. New Brunswick Department of Natural Resources, Minerals, Policy and Planning Division, Mineral Resource Report 3, 69 p. online database https://dnr-mrn.gnb.ca/mineraloccurrence/

  • Shives, R. B. K., Ford, K. L., & Peter, J. M. (2003). Mapping and exploration applications of gamma ray spectrometry in the Bathurst mining camp, northeastern New Brunswick. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern maine: economic geology monograph 11 (pp. 819–840). Society of Economic Geologists.

    Google Scholar 

  • Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1–48.

    Article  Google Scholar 

  • Solomon, M., & Groves, D. (1994). The geology and origin of Australia’s mineral deposits. Oxford University Press.

    Google Scholar 

  • Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293.

    Article  Google Scholar 

  • Tessema, A. (2017). Mineral systems analysis and artificial neural network modeling of chromite prospectivity in the Western Limb of the Bushveld Complex. South Africa. Natural Resources Research, 26(4), 465–488.

    Article  Google Scholar 

  • Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.

    Google Scholar 

  • van Staal, C. R. (1994). Brunswick subduction complex in the Canadian Appalachians: record of late Ordovician to late Silurian collision between Laurentia and the Gander margin of Avalon. Tectonics, 13, 946–962.

    Article  Google Scholar 

  • van Staal, C.R. and Fyffe, L.R., (1991). Dunnage and Gander zones, New Brunswick, Canadian Appalachian region. New Brunswick Department of Natural Resources and Energy, Mineral Resources Branch Geoscience Report (pp. 91) (2), 39.

  • van Staal, C. R., Wilson, R. A., Rogers, N., Fyffe, L. R., Langton, J. P., McCutcheon, S. R., McNicoll, V., & Ravenhurst, C. E. (2003). Geology and tectonic history of the Bathurst Supergroup, Bathurst mining camp, and its relationships to coeval rocks in southwestern New Brunswick and adjacent maine—a synthesis. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern maine: economic geology monograph 11 (pp. 17–35). Society of Economic Geologists.

    Google Scholar 

  • van Ravenzwaaij, D., Cassey, P., & Brown, S. D. (2018). A simple introduction to Markov Chain Monte-Carlo sampling. Psychonomic Bulletin & Review, 25(1), 143–154.

    Article  Google Scholar 

  • Wang, J., & Zuo, R. (2020). Assessing geochemical anomalies using geographically weighted LASSO. Applied Geochemistry, 119, 104668.

    Article  Google Scholar 

  • Williams, H. ed., (1995). Geology of the Appalachian—Caledonian Orogen in Canada and Greenland. Geological Society of America.

  • Wilson, R. A., van Staal, C. R., & McClelland, W. C. (2015). Synaccretionary sedimentary and volcanic rocks in the Ordovician Tetagouche back arc basin, New Brunswick, Canada: evidence for a transition from foredeep to forearc basin sedimentation. American Journal of Science, 315, 958–1001.

    Article  Google Scholar 

  • Wright, D. F., Chung, C. F., & Leybourne, M. I. (2003). Volcanic-hosted massive sulfide favorability mapping using the Kernel method analysis of stream Sediment and till geochemical data in the Western Bathurst mining camp. In W. D. Goodfellow, S. R. McCutcheon, & J. M. Peter (Eds.), Massive sulfide deposits of the Bathurst mining camp, New Brunswick, and Northern maine: economic geology monograph 11 (pp. 893–909). Society of Economic Geologists.

    Google Scholar 

  • Yin, B., Zuo, R., & Xiong, Y. (2022). Mineral prospectivity mapping via gated recurrent unit model. Natural Resources Research, 31(4), 2065–2079.

    Article  Google Scholar 

  • Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (statistical methodology), 67(2), 301–320.

    Article  Google Scholar 

  • Zulu, J. D., Lentz, D. R., Walker, J. A., & McFarlane, C. R. (2016). Recognizing and quantifying metamorphosed alteration zones through amphibolite facies metamorphic overprint at the Key Anacon Zn–Pb–Cu–Ag deposits, Bathurst Mining Camp, New Brunswick, Canada. Journal of Geochemical Exploration, 165, 143–158.

    Article  Google Scholar 

  • Zuo, R., & Xu, Y. (2022). Graph deep learning model for mapping mineral prospectivity. Mathematical Geosciences. https://doi.org/10.1007/s11004-022-10015-z

    Article  Google Scholar 

  • Zuo, R. (2020). Geodata science-based mineral prospectivity mapping: a review. Natural Resources Research, 29(6), 3415–3424.

    Article  Google Scholar 

  • Zuo, R., & Carranza, E. J. M. (2011). Support vector machine: a tool for mapping mineral prospectivity. Computers & Geosciences, 37(12), 1967–1975.

    Article  Google Scholar 

  • Zuo, R., Kreuzer, O. P., Wang, J., Xiong, Y., Zhang, Z., & Wang, Z. (2021a). Uncertainties in GIS-based mineral prospectivity mapping: key types, potential impacts and possible solutions. Natural Resources Research, 30(5), 3059–3079.

    Article  Google Scholar 

  • Zuo, R., Luo, Z., Xiong, Y., & Yin, B. (2022). A geologically constrained variational autoencoder for mineral prospectivity mapping. Natural Resources Research, 31(3), 1121–1133.

    Article  Google Scholar 

  • Zuo, R., Wang, J., & Yin, B. (2021b). Visualization and interpretation of geochemical exploration data using GIS and machine learning methods. Applied Geochemistry, 134, 105111.

    Article  Google Scholar 

  • Zuo, R., & Wang, Z. (2020). Effects of random negative training samples on mineral prospectivity mapping. Natural Resources Research, 29(6), 3443–3455.

    Article  Google Scholar 

  • Zuo, R., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019). Deep learning and its application in geochemical mapping. Earth-Science Reviews, 192, 1–14.

    Article  Google Scholar 

Download references

Acknowledgments

The expert handling of Prof. Renguang Zuo and the comments and suggestions of two anonymous reviewers greatly benefited this manuscript. The first author was supported by a McCain Foundation postdoctoral fellowship. He also appreciates the help of staff at Brunswick Exploration Co.

Author information

Authors and Affiliations

  1. Department of Earth Sciences, University of New Brunswick, Fredericton, NB, Canada

    Mohammad Parsa & David R. Lentz

  2. New Brunswick Department of Natural Resources and Energy Development, Geological Surveys Branch, 2574 Route 180 South Tetagouche, Fredericton, NB, Canada

    James A. Walker

Authors
  1. Mohammad Parsa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David R. Lentz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. James A. Walker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Parsa.

Ethics declarations

Conflict of Interests

The authors declare no conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parsa, M., Lentz, D.R. & Walker, J.A. Predictive Modeling of Prospectivity for VHMS Mineral Deposits, Northeastern Bathurst Mining Camp, NB, Canada, Using an Ensemble Regularization Technique. Nat Resour Res 32, 19–36 (2023). https://doi.org/10.1007/s11053-022-10133-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-022-10133-9

Keywords

Search

Navigation