Skip to main content

Advertisement

SpringerLink
Log in

Automatic lithological mapping from potential field data using machine learning: a case study from Mundiyawas-Khera Cu deposit, Rajasthan, India

  • Research Article - Applied Geophysics
  • Published:
Acta Geophysica Aims and scope Submit manuscript

Abstract

Potential field data play a vital role in mineral resource mapping, especially in deriving the lithological information of poorly mapped terrains. The Mundiyawas-Khera area of the Alwar basin in Rajasthan, India, is known for Cu mineralization hosted within the felsic volcanic rocks. However, much of the area is covered with soil and needs detailed lithological mapping. In this study, different machine learning (ML) algorithms have been employed to integrate the digital elevation, drilling wells, gravity, and magnetic data, together with their derivatives, for obtaining accurate lithology information of the area. Initially, five different ML algorithms, random forest (RF), K-nearest neighbor, support vector machine, multi-layer perceptron (MLP), and gradient boosting (GB) were employed using 540 samples from six lithological units to obtain the refined lithologic map of the area. Subsequently, a stacking classifier was built, considering the best-performing ML models in the base learner. Comparison of evaluation matrices (precision, recall, and confusion matrix) of these ML algorithms suggests that RF, GB, and stack model (RF + GB + MLP with RF meta-classifier) provide the highest accuracy score (RF: 74.69%, GB: 74.69%, and stack: 75.31%) and class membership probabilities in predicting the lithology. Adding derivatives and analytic signal information to the input data improves the classification accuracy of ML models by ~ 5–8%. Overall the study results demonstrate that ensemble ML algorithms can aid in creating the first-pass lithology map of areas with limited outcrops, drilling, and geochemical data.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Abedi M, Norouzi GH (2012) Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. J Appl Geophys 83:35–45

    Article  Google Scholar 

  • Aitken ARA, Holden EJ, Dentith MC (2013) Semiautomated quantification of the influence of data richness on confidence in the geologic interpretation of aeromagnetic maps. Geophysics 78:J1–J13. https://doi.org/10.1190/geo2012-0033.1

    Article  Google Scholar 

  • Anifowose F, Labadin J, Abdulraheem A (2015) Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines. Appl Soft Comput 26:483–496

    Article  Google Scholar 

  • Azam MS, Khan MS, Raza M (2015) Petrogenetic study of Mesoproterozoic volcanic rocks of North Delhi fold belt, NW Indian shield: implications for mantle conditions during Proterozoic. Chin j Geochem 34:93–114

    Article  CAS  Google Scholar 

  • Bhattacharya S, Carr TR, Pal M (2016) Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: case studies from the Bakken and Mahantango-Marcellus Shale, USA. J Natl Gas Sci Eng 33:1119–1133

    Article  CAS  Google Scholar 

  • Blakely RJ (1995) Potential theory in gravity and magnetic applications. Cambridge University Press, Cambridge, p 437

    Book  Google Scholar 

  • Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2–16

    Article  Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24:23–140

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Bressan TS, de Souza MK, Girelli TJ, Junior FC (2020) Evaluation of machine learning methods for lithology classification using geophysical data. Comput Geosci 139:104475

    Article  Google Scholar 

  • Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd International conference on knowledge discovery and data mining, pp 785–794

  • Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27

    Article  Google Scholar 

  • Cracknell MJ, Reading AM (2014) Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Comput Geosci 63:22–33

    Article  Google Scholar 

  • Cracknell MJ, Reading AM, McNeill AW (2014) Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps. Aust J Earth Sci 61:287–304

    Article  CAS  Google Scholar 

  • Deb M (2014) Precambrian geodynamics and metallogeny of the Indian shield. Ore Geol Rev 57:1–28

    Article  Google Scholar 

  • Elbegue AA, Allek K, Zeghouane H (2022) Geological mapping using extreme gradient boosting and the deep neural networks: application to silet area, central Hoggar, Algeria. Acta Geophys 70:1581–1599

    Article  Google Scholar 

  • Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378

    Article  Google Scholar 

  • Fu G, Lü Q, Yan J, Farquharson CG, Qi G, Zhang K, Zhang Y, Wang H, Luo F (2021) 3D mineral prospectivity modeling based on machine learning: a case study of the Zhuxi tungsten deposit in northeastern Jiangxi Province, South China. Ore Geol Rev 13:104010

    Article  Google Scholar 

  • Hall B (2016) Facies classification using machine learning. Lead Edge 35:906–909

    Article  Google Scholar 

  • Haykin S (2009) Neural networks and learning machines, 3rd edn. Prentice Hall, New York, p 938

    Google Scholar 

  • Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1. IEEE, pp 278–282

  • Holden EJ, Fu SC, Kovesi P, Dentith M, Bourne B, Hope M (2011) Automatic identification of responses from porphyry intrusive systems within magnetic data using image analysis. Appl Geophys 74:255–262

    Article  Google Scholar 

  • Holden EJ, Wong JC, Kovesi P, Wedge D, Dentith M, Bagas L (2012) Identifying structural complexity in aeromagnetic data: an image analysis approach to greenfields gold exploration. Ore Geol Rev 46:47–59. https://doi.org/10.1016/j.oregeorev.2011.11.002

    Article  Google Scholar 

  • Jia R, Lv Y, Wang G, Carranza E, Chen Y, Wei C, Zhang Z (2021) A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China). Comput Geosci 151:104754

    Article  Google Scholar 

  • Kaur P, Zeh A, Okrusch M, Chaudhri N, Gerdes A, Brätz H (2016) Separating regional metamorphic and metasomatic assemblages and events in the northern Khetri complex, NW India: evidence from mineralogy, whole-rock geochemistry and U-Pb monazite chronology. J Asian Earth Sci 129:117–141

    Article  Google Scholar 

  • Kaur P, Zeh A, Chaudhri N (2019) Archean crustal evolution of the Aravalli Banded Gneissic Complex, NW India: Constraints from zircon U-Pb ages, Lu-Hf isotope systematics, and whole-rock geochemistry of granitoids. Precambrian Res 327:81–102

    Article  CAS  Google Scholar 

  • Khan I, Rai DK, Sahoo PR (2013) A note on new find of thick copper and associated precious metal mineralisation from Alwar basin of North Delhi Fold Belt, Rajasthan. J Geol Soc India 82:495

    Article  CAS  Google Scholar 

  • Khan I, Sahoo PR, Rai DK (2014) Proterozoic felsic volcanics in Alwar Basin of North Delhi Fold Belt, Rajasthan: implication for copper mineralization. Curr Sci 106:27–28

    Google Scholar 

  • Khan I, Chauhan A (2018) Final report on preliminary exploration for copper and associated precious metals in Mundiyawas block, Mundiyawas-Khera area, Alwar district, Rajasthan (stage: unfc G-3); Final Report 2016– 2017. Unpubld Geol Surv India

  • Khan I, Sahoo PR, Rai DK (2015) Geological set up of low grade copper-gold mineralization at Mundiyawas-Khera area, Alwar district, Rajasthan; In: Golani PR (ed) Recent developments in metallogeny and mineral exploration in Rajasthan, vol 101. Geol Soc India Spec Publ. pp 43–58

  • Khan I (2021) Metallogenetic significance and exploration strategies of copper-gold mineralization near Mundiyawas-Khera Area, Alwar Basin, Rajasthan, Western India. PhD Thesis Indian Institute of Technology (Indian School of Mines) Dhanbad India (Unpublished)

  • Knight J (2002) The Khetri copper belt, Rajasthan: iron-oxide copper-gold terrane in the proterozoic of NW India. Hydrothermal Iron Oxide Copper Gold Relat Deposit Glob Perspect 2:321–341

    Google Scholar 

  • Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268

    Google Scholar 

  • Kuhn S, Cracknell MJ, Reading AM (2018) Lithologic mapping using random forests applied to geophysical and remote-sensing data: a demonstration study from the Eastern Goldfields of Australia. Geophysics 83:B183–B193

    Article  Google Scholar 

  • Kumar T, Seelam NK, Rao GS (2022) Lithology prediction from well log data using machine learning techniques: a case study from Talcher coalfield, Eastern India. J Appl Geophys 199:1–15

    Article  Google Scholar 

  • Lal RK, Ackermand D (1981) Phase-petrology and polyphase andalusite-sillimanite type regional metamorphism in pelitic schists of the area around Akhwali, Khetri Copper Belt, Rajasthan, India. Neues Jahrb Für Mineral Abhandlungen 141(2):161–185

    CAS  Google Scholar 

  • Maria Navin JR, Pankaja R (2016) Performance analysis of text classification algorithms using confusion matrix. Int J Eng Tech Res 6:75–78

    Google Scholar 

  • Masoumi F, Eslamkish T, Abkar AA, Honarmand M, Harris JR (2017) Integration of spectral, thermal, and textural features of ASTER data using random forests classification for lithological mapping. J Afr Earth Sci 129:445–457

    Article  Google Scholar 

  • McCormack MD (1991) Neural computing in geophysics. Lead Edge 10:11–15

    Article  Google Scholar 

  • Mehdi M, Kumar S, Pant NC (2015) Low grade metamorphism in the Lalsot-Bayana sub-basin of the North Delhi Fold Belt and its tectonic implication. J Geol Soc India 85:397–410

    Article  CAS  Google Scholar 

  • Mukhopadhyay S (2009) STM of Delhi Supergroup of rocks in Jodhawas-Kushalgarh area, Alwar district, Rajasthan. Unpubld Geol Surv India Progress Report FS 2006-07

  • Nabighian MN (1984) Toward a three-dimensional automatic interpretation of potential field data via generalized Hilbert transforms; fundamental relations. Geophysics 49:780–786. https://doi.org/10.1190/1.1441706

    Article  Google Scholar 

  • Naha K, Mitra SK, Biswal TK (1987) Structural history of the rocks of the Delhi Group around Todgarh, Central Rajasthan. Indian J Geol 59:126–156

    Google Scholar 

  • Nathan D, Aitken A, Holden EJ, Wong J (2020) Imaging sedimentary basins from high-resolution aeromagnetics and texture analysis. Comput Geosci 136:104396

    Article  Google Scholar 

  • Powar KB, Patwardhan AM (1984) Tectonic evolution and base-metal mineralisation in the Aravalli-Delhi belt, India. Precambrian Res 25:309–323

    Article  CAS  Google Scholar 

  • Pradhan B, Jena R, Talukdar D, Mohanty M, Sahu BK, Raul AK, Abdul Maulud KN (2022) A new method to evaluate gold mineralisation-potential mapping using deep learning and an explainable artificial intelligence (XAI) model. Remote Sens 14:4486

    Article  Google Scholar 

  • Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Rao GS, Arasada RC, Sahoo PR, Khan I (2019) Integrated geophysical investigations in the Mudiyawas-Khera block of the Alwar basin of North Delhi Fold Belt (NDBF): implications on copper and associated mineralisation. J Earth Syst Sci 128:1–13

    Article  Google Scholar 

  • Roest WR, Verhoef J, Pilkington M (1992) Magnetic interpretation using the 3-D analytic signal. Geophysics 57:116–125

    Article  Google Scholar 

  • Sarkar SC, Dasgupta S (1980) Geologic setting, genesis and transformation of sulfide deposits in the northern part of Khetri copper belt, Rajasthan, India—an outline. Miner Deposita 15:117–137

    Article  CAS  Google Scholar 

  • Serbouti I, Raji M, Hakdaoui M, El Kamel F, Pradhan B, Gite S, Alamri A, Maulud KNA, Dikshit A (2022) Improved lithological map of large complex semi-arid regions using spectral and textural datasets within google earth engine and fused machine learning multi-classifiers. Remote Sens 1414:5498

    Article  Google Scholar 

  • Singh SP (1988) Sedimentation patterns of the Proterozoic Delhi Supergroup, northeastern Rajasthan, India, and their tectonic implications. Sediment Geol 58:79–94

    Article  Google Scholar 

  • Smyth P (1999) Probabilistic model-based clustering of multivariate and sequential data. In: Proceedings of the seventh international workshop on AI and statistics. Morgan Kaufman, San Francisco, pp 299–304

  • Sun Z, Jiang B, Li X, Li J, Xiao K (2020) A data driven approach for lithology identification based on parameter-optimized ensemble learning. Energies 13:3903

    Article  Google Scholar 

  • Ting KM, Witten IH (1999) Issues in stacked generalization. J Artif Intell Res 10:271–289

    Article  Google Scholar 

  • Van der Baan M, Jutten C (2000) Neural networks in geophysical applications. Geophysics 65:1032–1047

    Article  Google Scholar 

  • Vapnik V (1995) The nature of statistical learning theory. Springer, New York, p 188

    Book  Google Scholar 

  • Wang K, Zhang L (2008) Predicting formation lithology from log data by using a neural network. Pet Sci 5:242–246

    Article  Google Scholar 

  • Waske B, Benediktsson JA, Árnason K, Sveinsson JR (2009) Mapping of hyperspectral AVIRIS data using machine-learning algorithms. Can J Remote Sens 35:S106–S116

    Article  Google Scholar 

  • Wijns C, Perez C, Kowalczyk P (2005) Theta map: edge detection in magnetic data. Geophysics 70:L39–L43

    Article  Google Scholar 

  • Wolpert DH (1992) Stacked generalization. Neural Netw 5:249–251

    Article  Google Scholar 

  • Xie Y, Zhu C, Zhou W, Li Z, Liu X, Tu M (2017) Evaluation of machine learning methods for formation lithology identification: a comparison of tuning processes and model performances. J Pet Sci Eng 160:182–193

    Article  Google Scholar 

  • Xu Y, Li Z, Xie Z, Cai H, Niu P, Liu H (2021) Mineral prospectivity mapping by deep learning method in Yawan-Daqiao area. Gansu Ore Geol Rev 13:104316

    Article  Google Scholar 

  • Yu L, Porwal A, Holden EJ, Dentith MC (2012) Towards automatic lithological classification from remote sensing data using support vector machines. Comput Geosci 45:229–239

    Article  Google Scholar 

Download references

Acknowledgements

GSR acknowledges the Department of Science & Technology and Science & Engineering Research Board (SERB) for the research grants (DST-FIST/197/2018-19/580 & CRG/2021/006513).

Funding

This work was supported by the Department of Science & Technology (DST-FIST/197/2018-19/580) and the Science & Engineering Research Board (CRG/2021/006513), Government of India.

Author information

Authors and Affiliations

  1. Department of Applied Geophysics, IIT (ISM) Dhanbad, Jharkhand, 826004, India

    Bhawesh Kumar Singh, Srinivasa Rao Gangumalla & Rama Chandrudu Arasada

  2. CMPDIL, Coal India Limited, Ranchi, 834 008, India

    Thinesh Kumar

Authors
  1. Bhawesh Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Srinivasa Rao Gangumalla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rama Chandrudu Arasada
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thinesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Bhawesh Kumar Singh and G Srinivasa Rao performed algorithm development, data analysis, and preparation of the first draft of the manuscript. Thinesh Kumar and Rama Chandrudu Arasada commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Srinivasa Rao Gangumalla.

Ethics declarations

Conflict of interest

Authors declare they have no financial interests.

Additional information

Edited by Dr. Qamar Yasin (ASSOCIATE EDITOR) / Prof. Gabriela Fernández Viejo (CO-EDITOR-IN-CHIEF).

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

Singh, B.K., Gangumalla, S.R., Arasada, R.C. et al. Automatic lithological mapping from potential field data using machine learning: a case study from Mundiyawas-Khera Cu deposit, Rajasthan, India. Acta Geophys. 72, 777–792 (2024). https://doi.org/10.1007/s11600-023-01151-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11600-023-01151-z

Keywords

Search

Navigation