Skip to main content

Advertisement

SpringerLink
Log in

Visual Interpretable Deep Learning Algorithm for Geochemical Anomaly Recognition

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

Abstract

Deep learning algorithms (DLAs) have achieved better results than traditional methods in the field of multivariate geochemical anomaly recognition because of their strong ability to extract feature from nonlinear data. However, most of DLAs are black-box approaches because of the high nonlinearity characteristics of the hidden layer. In addition, the integration of domain knowledge into the DLAs to ensure physical consistency is a challenge for DLAs in geoscience. In this study, we adopted the adversarial autoencoder (AAE) algorithm for geochemical anomaly detection. The interpretability of the model is improved by visualizing features and integrating geological domain knowledge into the loss function of the AAE. The feature visualization method was used to display the changes of information in the model calculation process to further understand the inherent operation law and principle of the neural network. The penalty term was added to the optimized loss function, and the spatiotemporal and genetic relationships between felsic intrusions and mineralization were integrated into the AAE with the aim of improving the geological interpretability of the network. The added penalty item can guide the changes in the stage of data reconstruction and improve the understandability of the results of geologically constrained AAE. In addition, the effectiveness of injecting the concept of physical constraints into the AAE can be verified via feature visualization. A case study in the southern Jiangxi Province and its surrounding areas was performed to identify multivariate geochemical anomalies. The results obtained by the geologically constrained AAE demonstrated a strong spatial correlation with the outcrop of intrusions in the study area, and most of the known mineral deposits are located in or near the highly anomalous areas.

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

modified from Makhzani et al. 2015)

Figure 2

modified from Albawi et al. 2017)

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

Similar content being viewed by others

References

  • Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160.

    Article  Google Scholar 

  • Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., & Kim, B. (2018). Sanity checks for saliency maps. arXiv preprint arXiv:1810.03292. Retrieved from http://arxiv.org/abs/1810.03292.

  • Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1–6). IEEE.

  • Ba, L. J., & Caruana, R. (2013). Do deep nets really need to be deep?. arXiv:1312.6184. Retrieved from http://arxiv.org/abs/1312.6184.

  • Castelvecchi, D. (2016). Can we open the black box of AI? Nature News, 538(7626), 20.

    Google Scholar 

  • Chen, C., Lin, K., Rudin, C., Shaposhnik, Y., Wang, S., & Wang, T. (2018). An interpretable model with globally consistent explanations for credit risk. arXiv:1811.12615. Retrieved from http://arxiv.org/abs/1811.12615.

  • Chen, L., Guan, Q., Xiong, Y., Liang, J., Wang, Y., & Xu, Y. (2019a). A spatially constrained multi-autoencoder approach for multivariate geochemical anomaly recognition. Computers & Geosciences, 125, 43–54.

    Article  Google Scholar 

  • Chen, L., Guan, Q., Feng, B., Yue, H., Wang, J., & Zhang, F. (2019b). A multi-convolutional autoencoder approach to multivariate geochemical anomaly recognition. Minerals, 9(5), 270.

    Article  Google Scholar 

  • Chen, Y., & Zhang, D. (2020). Physics-constrained deep learning of geomechanical logs. IEEE Transactions on Geoscience and Remote Sensing, 58(8), 5932–5943.

    Article  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.

    Article  Google Scholar 

  • Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P. M., Zietz, M., Hoffman, M. M., Xie, W., Rosen, G. L., Lengerich, B. J., Israeli, J., Lanchantin, J., Woloszynek, S., Carpenter, A. E., Shrikumar, A., Xu, J., & Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15(141), 20170387.

    Article  Google Scholar 

  • De Bézenac, E., Pajot, A., & Gallinari, P. (2019). Deep learning for physical processes: Incorporating prior scientific knowledge. Journal of Statistical Mechanics: Theory and Experiment, 2019(12), 124009.

    Article  Google Scholar 

  • Dong, G., Huang, W., Smith, W. A., & Ren, P. (2020). A shadow constrained conditional generative adversarial net for SRTM data restoration. Remote Sensing of Environment, 237, 111602.

    Article  Google Scholar 

  • Fan, F., Xiong, J., Li, M., & Wang, G. (2021). On interpretability of artificial neural networks: A survey. IEEE Transactions on Radiation and Plasma Medical Sciences, 5(6), 741–760.

    Article  Google Scholar 

  • Fang, X., & Yan, P. (2020). Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Transactions on Medical Imaging, 39(11), 3619–3629.

    Article  Google Scholar 

  • Geneva, N., & Zabaras, N. (2020). Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks. Journal of Computational Physics, 403, 109056.

    Article  Google Scholar 

  • Geng, Z., & Wang, Y. (2020). Physics-guided deep learning for predicting geological drilling risk of wellbore instability using seismic attributes data. Engineering Geology, 279, 105857.

    Article  Google Scholar 

  • Gong, J., Li, F., Zhang, S., & Cui, F. (2005). Delineating anomalies using similarity coefficients based on element assemblage characteristics: An example of the Nanling area. Geology and Exploration, 51(2), 312–322. (in Chinese with English abstract).

    Google Scholar 

  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Advances in Neural Information Processing Systems, 27. arXiv:1406.2661. Retrieved from http://arxiv.org/abs/1406.2661.

  • Goovaerts, P. (1997). Geostatistics for natural resources evaluation (p. 482). Oxford University Press.

  • Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys (CSUR), 51(5), 1–42.

    Article  Google Scholar 

  • Guo, C., Chen, Y., Zeng, Z., & Lou, F. (2012). Petrogenesis of the Xihuashan granites in southeastern China: Constraints from geochemistry and in-situ analyses of zircon UPbHfO isotopes. Lithos, 148, 209–227.

    Article  Google Scholar 

  • Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.

    Article  Google Scholar 

  • Huang, Z., & Li, Y. (2020). Interpretable and accurate fine-grained recognition via region grouping. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8662–8672).

  • Huang, X., Li, G., Guo, J., Zhang, M., Hu, Z., Hua, R., & Wei, X. (2012). Characteristics of ore-forming granite and its mineralization in the Zhangdongkeng tungsten deposit of Southern Jiangxi Province Geology and Prospecting. Geology and Prospecting, 48(04), 685–692. (In Chinese with English abstract).

    Google Scholar 

  • Jiang, S., Zheng, Y., & Solomatine, D. (2020). Improving AI system awareness of geoscience knowledge: Symbiotic integration of physical approaches and deep learning. Geophysical Research Letters, 47(13), e2020L088229.

    Article  Google Scholar 

  • Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017a). Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2318–2331.

    Article  Google Scholar 

  • Karpatne, A., Watkins, W., Read, J., & Kumar, V. (2017b). Physics-guided neural networks (PGNN): An application in lake temperature modeling. arXiv:1710.11431. Retrieved from http://arxiv.org/abs/1710.11431.

  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.

    Google Scholar 

  • Lage, I., Ross, A., Gershman, S. J., Kim, B., & Doshi-Velez, F. (2018). Human-in-the-loop interpretability prior. arXiv preprint arXiv:1805.11571. Retrieved from https://arxiv.org/abs/1805.11571.

  • LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551.

    Article  Google Scholar 

  • Li, H., Li, X., Yuan, F., Jowitt, S. M., Zhang, M., Zhou, J., Zhou, T., Li, X., Ge, C., & Wu, B. (2020). Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian-Zhangbaling area, Anhui Province, China. Applied Geochemistry, 122, 104747.

    Article  Google Scholar 

  • Liu, Y., Xia, Q., Cheng, Q., & Wang, X. (2013). Application of singularity theory and logistic regression model for tungsten polymetallic potential mapping. Nonlinear Processes in Geophysics, 20(04), 445–453.

    Article  Google Scholar 

  • Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874. Retrieved from https://arxiv.org/abs/1705.07874.

  • Luo, Z., Xiong, Y., & Zuo, R. (2020). Recognition of geochemical anomalies using a deep variational autoencoder network. Applied Geochemistry, 122, 104710.

    Article  Google Scholar 

  • Luo, Z., Zuo, R., Xiong, Y., & Wang, X. (2021). Detection of geochemical anomalies related to mineralization using the GANomaly network. Applied Geochemistry, 131, 105043.

    Article  Google Scholar 

  • Mahendran, A., & Vedaldi, A. (2015). Understanding deep image representations by inverting them. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5188–5196).

  • Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015). Adversarial autoencoders. arXiv:1511.05644. Retrieved from http://arxiv.org/abs/1511.05644.

  • Moeini, H., & Torab, F. M. (2017). Comparing compositional multivariate outliers with autoencoder networks in anomaly detection at Hamich exploration area, east of Iran. Journal of Geochemical Exploration, 180, 15–23.

    Article  Google Scholar 

  • Nie, R., & Wang, X. (2007). Research progress of tungsten deposits in southern Jiangxi. China Tungsten Industry, 22(3), 1–5. (In Chinese with English abstract).

    Google Scholar 

  • Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A. (2018). The building blocks of interpretability. Distill, 3(3), e10.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., Yousefi, M., & Sadeghi, M. (2017). Multifractal analysis of stream sediment geochemical data: Implications for hydrothermal nickel prospection in an arid terrain, eastern Iran. Journal of Geochemical Exploration, 181, 305–317.

    Article  Google Scholar 

  • Raissi, M. (2018). Deep hidden physics models: Deep learning of nonlinear partial differential equations. The Journal of Machine Learning Research, 19(1), 932–955.

    Google Scholar 

  • Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707.

    Article  Google Scholar 

  • Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., & Carvalhais, N. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204.

    Article  Google Scholar 

  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.

    Article  Google Scholar 

  • Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv:1708.08296. Retrieved from http://arxiv.org/abs/1708.08296.

  • Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 618–626).

  • Simonyan, K., Vedaldi, A., & Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv:1312.6034. Retrieved from http://arxiv.org/abs/1312.6034.

  • Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014). Striving for simplicity: The all convolutional net. arXiv:1412.6806. Retrieved from http://arxiv.org/abs/1412.6806.

  • Voulodimos, A., Doulamis, N., Bebis, G., & Stathaki, T. (2018). Recent developments in deep learning for engineering applications. Computational Intelligence and Neuroscience, 2018, 1–2.

    Google Scholar 

  • Wang, C., Pan, Y., Chen, J., Ouyang, Y., Rao, J., & Jiang, Q. (2020a). Indicator element selection and geochemical anomaly mapping using recursive feature elimination and random forest methods in the Jingdezhen region of Jiangxi Province, South China. Applied Geochemistry, 122, 104760.

    Article  Google Scholar 

  • Wang, C. Y., Wei, B., Zhou, M. F., Minh, D. H., & Qi, L. (2018). A synthesis of magmatic Ni-Cu-(PGE) sulfide deposits in the~ 260 Ma Emeishan large igneous province, SW China and northern Vietnam. Journal of Asian Earth Sciences, 154, 162–186.

    Article  Google Scholar 

  • Wang, N., Zhang, D., Chang, H., & Li, H. (2020b). Deep learning of subsurface flow via theory-guided neural network. Journal of Hydrology, 584, 124700.

    Article  Google Scholar 

  • Wang, X., Zhang, Q., & Zhou, G. (2007). Nationalscale geochemical mapping projects in China. Geostandards and Geoanalytical Research, 31(4), 311–320.

    Article  Google Scholar 

  • Wang, T. (2019). Gaining free or low-cost interpretability with interpretable partial substitute. In International Conference on Machine Learning (pp. 6505–6514). PMLR.

  • Wen, G., & Wen, L. (1963). Further discussion on metallogenic specialization of magmatic rocks. Acta Geologica Sinica, 43(04), 64–79. (in Chinese with Russian abstract).

    Google Scholar 

  • Xie, X., Liang, T., Lu, L., Zhao, Z., Chen, Z., Chen, W., & Ding, M. (2017). Chemical composition and crystal texture of the Pangushan and Taoxikeng wolframite in Southern Jiangxi and its Indication significance. Acta Geologica Sinica, 91(04), 876–895. (in Chinese with English abstract).

    Google Scholar 

  • Xie, X., Mu, X., & Ren, T. (1997). Geochemical mapping in China. Journal of Geochemical Exploration, 60(01), 99–113.

    Article  Google Scholar 

  • Xiong, Y., & Zuo, R. (2016). Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences, 86, 75–82.

    Article  Google Scholar 

  • Xiong, Y., & Zuo, R. (2020). Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine. Computers & Geosciences, 140, 104484.

    Article  Google Scholar 

  • Xiong, Y., & Zuo, R. (2022). Robust feature extraction for geochemical anomaly recognition using a stacked convolutional denoising autoencoder. Mathematical Geosciences, 54, 623–644.

    Article  Google Scholar 

  • Xiong, Y., Zuo, R., Luo, Z., & Wang, X. (2022). A physically constrained variational autoencoder for geochemical pattern recognition. Mathematical Geosciences, 54, 783–806.

    Article  Google Scholar 

  • Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., & Lipson, H. (2015). Understanding neural networks through deep visualization. arXiv:1506.06579. Retrieved from http://arxiv.org/abs/1506.06579.

  • Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. IEEE Computational Intelligence Magazine, 13(3), 55–75.

    Article  Google Scholar 

  • Yuan, S., Williams-Jones, A. E., Mao, J., Zhao, P., Yan, C., & Zhang, D. (2018). The origin of the Zhangjialong tungsten deposit, South China: Implications for W-Sn mineralization in large granite batholiths. Economic Geology, 113(5), 1193–1208.

    Article  Google Scholar 

  • Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European Conference on Computer Vision (pp. 818–833). Springer, Cham.

  • Zeng, Z., & He, G. (2017). The division of tungsten mineralization units in southern Jiangxi and their mineralization geological characteristics. In Geological Society of Jiangxi Province. (eds.) 2016 Collection of Papers of the Geological Society of Jiangxi Province III (pp. 31–38) (in Chinese with English abstract).

  • Zhai, Y., Yao, S., Lin, X., Jin, F., Zhou, X., Wan, T., & Zhou, Z. (1992). Metallogenic regularity of iron and copper deposits in the middle and lower valley of the Yangtze river. Mineral Deposits, 11(01), 1–12. in Chinese with English abstract.

    Google Scholar 

  • Zhang, C., Zuo, R., & Xiong, Y. (2021a). Detection of the multivariate geochemical anomalies associated with mineralization using a deep convolutional neural network and a pixel-pair feature method. Applied Geochemistry, 130, 104994.

    Article  Google Scholar 

  • Zhang, C., & Zuo, R. (2021). Recognition of multivariate geochemical anomalies associated with mineralization using an improved generative adversarial network. Ore Geology Reviews, 136, 104264.

    Article  Google Scholar 

  • Zhang, C., Zuo, R., Xiong, Y., Zhao, X., & Zhao, K. (2022). A geologically-constrained deep learning algorithm for recognizing geochemical anomalies. Computers & Geosciences, 162, 105100.

    Article  Google Scholar 

  • Zhang, R., Liu, Y., & Sun, H. (2020). Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling. Engineering Structures, 215, 110704.

    Article  Google Scholar 

  • Zhang, Q., Wu, Y. N., & Zhu, S. C. (2018). Interpretable convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8827–8836).

  • Zhang, Y., Tiňo, P., Leonardis, A., & Tang, K. (2021b). A survey on neural network interpretability. IEEE Transactions on Emerging Topics in Computational Intelligence, 5, 726–742.

    Article  Google Scholar 

  • Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C. J., Li, X., & Qiu, G. Y. (2019). Physics-constrained machine learning of evapotranspiration. Geophysical Research Letters, 46(24), 14496–14507.

    Article  Google Scholar 

  • Zhao, Y., Lin, W., Bi, C., Li, D., & Jiang, C. (1990). Skarn deposits in China. Geological Publishing House, 1–354 (in Chinese).

  • Zhu, Y., Zabaras, N., Koutsourelakis, P. S., & Perdikaris, P. (2019). Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. Journal of Computational Physics, 394, 56–81.

    Article  Google Scholar 

  • Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2921–2929).

  • Zuo, R. (2016). A nonlinear controlling function of geological features on magmatic–hydrothermal mineralization. Scientific Reports, 6(1), 27127.

    Article  Google Scholar 

  • Zuo, R. (2018). A fractal measure of mass transfer in fluid–rock interaction. Ore Geology Reviews, 95, 569–574.

    Article  Google Scholar 

  • Zuo, R., Cheng, Q., & Agterberg, F. P. (2009). Application of a hybrid method combining multilevel fuzzy comprehensive evaluation with asymmetric fuzzy relation analysis to mapping prospectivity. Ore Geology Reviews, 35(1), 101–108.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zuo, R., Wang, J., Xiong, Y., & Wang, Z. (2021a). The processing methods of geochemical exploration data: Past, present, and future. Applied Geochemistry, 132, 105072.

    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., 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 

  • Zuo, R., Zhang, Z., Zhang, D., Carranza, E. J. M., & Wang, H. (2015). Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China. Ore Geology Reviews, 71, 502–515.

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by the National Natural Science Foundation of China (No. 42172326).

Author information

Authors and Affiliations

  1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China

    Zijing Luo, Renguang Zuo & Yihui Xiong

Authors
  1. Zijing Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Renguang Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yihui Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renguang Zuo.

Ethics declarations

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could affect the work reported in this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, Z., Zuo, R. & Xiong, Y. Visual Interpretable Deep Learning Algorithm for Geochemical Anomaly Recognition. Nat Resour Res 31, 2211–2223 (2022). https://doi.org/10.1007/s11053-022-10080-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-022-10080-5

Keywords

Search

Navigation