Skip to main content
SpringerLink
Log in

Computational Geoscience

  • Living reference work entry
  • First Online:
Encyclopedia of Mathematical Geosciences

Part of the book series: Encyclopedia of Earth Sciences Series ((EESS))

  • 28 Accesses

Abstract

A systematic approach to the evaluation of geochemical data involves the use of multivariate methods that identify processes. These processes are represented by element associations that reflect mineralogy. Processes may be linear or nonlinear, depending on the type of process. Different metrics can reflect different processes. Metrics with coordinates derived from principal component analysis, independent component analysis, and t-distributed stochastic embedding, to name a few, reflect different processes. The dominant components of these metrics can be used to enhance the signal/noise ratio in the data. An integral part of process discovery is the geospatial coherence of multivariate signatures. Models can be constructed by tagging the dominant components with attributes such as geology or mineral deposit information. These models can be tested using a range of multivariate classification/validation/prediction procedures from which probability-based measures of likelihood can be determined and displayed geospatially. The application of these techniques requires acknowledgment of the limitations inherent in the data.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Bibliography

  • Aitchison J (1986) The statistical analysis of compositional data. Chapman and Hall, New York, p 416

    Book  Google Scholar 

  • BC Geological Survey (1996) British Columbia mineral deposit profiles. BC Geological Survey. http://cmscontent.nrs.gov.bc.ca/geoscience/PublicationCatalogue/Miscellaneous/BCGS_MP-86.pdf. Accessed Nov 2019

  • BC Geological Survey (2019) MINFILE BC mineral deposits database. BC Ministry of Energy, Mines and Petroleum Resources. http://MINFILE.ca. Accessed Nov 2019

  • Cheng Q, Agterberg FP (1994) The separation of geochemical anomalies from background by fractal methods. J Geochem Explor 51:109–130

    Article  Google Scholar 

  • Cheng Q, Zhao P (2011) Singularity theories and methods for characterizing mineralization processes and mapping geo-anomalies for mineral deposit prediction. Geoscience Fontiers 2(1):67–79

    Google Scholar 

  • Cheng Q, Xu Y, Grunsky EC (2000) Integrated spatial and spectrum analysis for geochemical anomaly separation. Nat Resour Res 9(1):43–51

    Article  Google Scholar 

  • Comon P (1994) Independent component analysis: a new concept? Signal Process 36:287–314

    Article  Google Scholar 

  • Cox MAA (2001) Multidimensional scaling. Chapman and Hall, New York

    Google Scholar 

  • Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barcelo-Vidal C (2003) Isometric logratio transformations for compositional data analysis. Math Geol 35(3):279–300

    Article  Google Scholar 

  • Greenacre M, Grunsky E, Bacon-Shone J, Erb I, Quinn T (2022) Atichison’s Compositional Data Analysis 40 Years On: A Reappraisal. arXiv:submit/4116835 [stat.ME] 13 Jan 2022

    Google Scholar 

  • Grunsky EC (2010) The interpretation of geochemical survey data. Geochem Explor Environ Anal 10(1):27–74. https://doi.org/10.1144/1467-7873/09-210

    Article  Google Scholar 

  • Grunsky EC (2012) Editorial, special issue on spatial multivariate methods. Math Geosci 44(4):379–380

    Article  Google Scholar 

  • Grunsky EC, Arne D (2020) Mineral-resource prediction using advanced data analytics and machine learning of the QUEST-South stream-sediment geochemical data, southwestern British Columbia, Canada. Geochem Explor Environ Anal. https://doi.org/10.1144/geochem2020-054

  • Grunsky EC, de Caritat P (2019) State-of-the-art analysis of geochemical data for mineral exploration. Geochem Explor Environ Anal. Special issue from Exploration 17, October, 2017, Toronto, Canada. https://doi.org/10.1144/geochem2019-031

  • Hariharan S, Tirodkar S, Porwal A, Bhattacharya A, Joly A (2017) Random forest-based prospectivity modelling of greenfield terrains using sparse deposit data: an example from the Tanami Region, Western Australia. Nat Resour Res 26:489–507. https://doi.org/10.1007/s11053-017-9335-6

    Article  Google Scholar 

  • Joliffe IT (1986) Principal component analysis. Springer, Berlin. https://doi.org/10.1007/b98835

    Book  Google Scholar 

  • McInnes L, Healy J, Melville J (2020) UMAP: Uniform Manifold Approximation and Projection for dimension reduction. arXiv:1802.03426v3 [stat.ML], 18 Sep 2020

    Google Scholar 

  • McKinley JM, Hron K, Grunsky EC, Reimann C, de Caritat P, Filzmoser P, van den Boogaart KG, Tolosana-Delgada R (2016) The single component geochemical map: fact or fiction? J Geochem Explor 162:16–28. ISSN 0375-6742. https://doi.org/10.1016/j.gexplo.2015.12.005

    Article  Google Scholar 

  • Mueller U, Tolosana-Delgado R, Grunsky EC, McKinley JM (2020) Biplots for compositional data derived from generalised joint diagonalization methods. Appl Comput Geosci. https://doi.org/10.1016/j.acags.2020.100044

  • Palarea-Albaladejo J, Martín-Fernández JA, Buccianti A (2014) Compositional methods for estimating elemental concentrations below the limit of detection in practice using R. J Geochem Explor 141:71–77

    Article  Google Scholar 

  • Pearce TH (1968) A contribution to the theory of variation diagrams. Contrib Mineral Petrol 19:142–157

    Article  Google Scholar 

  • Pebesma EJ (2004) Multivariable geostatistics in S: the gstat package. Comput Geosci 30:683–691

    Article  Google Scholar 

  • Switzer P, Green A (1984) Min/max autocorrelation factors for multivariate spatial imaging, Technical report no. 6. Department of statistics, Stanford University, Stanford

    Google Scholar 

  • Thompson M, Howarth RJ (1976a) Duplicate analysis in practice – part 1. Theoretical approach and estimation of analytical reproducibility. Analyst 101:690–698

    Article  Google Scholar 

  • Thompson M, Howarth RJ (1976b) Duplicate analysis in practice – part 2. Examination of proposed methods and examples of its use. Analyst 101:699–709

    Article  Google Scholar 

  • van der Maaten LJP, Hinton GE (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON, Canada

    Eric Grunsky

Authors
  1. Eric Grunsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. System Science & Informatics Unit, Indian Statistical Institute- Bangalore Centre, Bangalore, India

    B. S. Daya Sagar

  2. Insititue of Earth Sciences, China University of Geosciences, Beijing, China

    Qiuming Cheng

  3. School of Natural and Built Environment, Queen's University Belfast, Belfast, UK

    Jennifer McKinley

  4. Canada Geological Survey, Ottawa, ON, Canada

    Frits Agterberg

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Grunsky, E. (2022). Computational Geoscience. In: Daya Sagar, B.S., Cheng, Q., McKinley, J., Agterberg, F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_6-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26050-7_6-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26050-7

  • Online ISBN: 978-3-030-26050-7

  • eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences

Publish with us

Policies and ethics

Search

Navigation