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Separation of the Total Magnetic Anomalies into Induced and Remanent Magnetization Based on Deep Learning

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A Correction to this article was published on 08 January 2024

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Abstract

The presence of remanent magnetization brings uncertainty to the processing and interpretation of magnetic data. Therefore, separating the contributions of the remanent magnetization from the total magnetic data is always the research hotspot. In the literature, numerous methods have been introduced to handle this issue. However, most of the existing methods are complex to calculate, have strict requirements on magnetic sources, and need prior information. In this study, a new method for automatically separating the total magnetic anomalies into the components due to induced and remanent magnetization based on deep learning has been presented. The presented method designs an end-to-end network structure based on the U-Net network structure and then performs continuous training and parameter optimization to determine the optimal network structure. Afterward, the presented method is tested on synthetic examples and actual magnetic data in Yeshan Region (Eastern China). The results demonstrate that the presented method can separate anomalies by induced and remanent magnetization.

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The data that support the findings of this study are available from the author, WeiChen Li, upon reasonable request.

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References

  • Baniamerian, J., Liu, S., Hu, X., Fedi, M., Chauhan, M. S., & Abbas, M. A. (2020). Separation of magnetic anomalies into induced and remanent magnetization contributions. Geophysical Prospecting, 68(7), 2320–2342. https://doi.org/10.1111/1365-2478.12993

    Article  ADS  Google Scholar 

  • Blakely, R. J. (1996). Potential theory in gravity and magnetic applications. Cambridge University Press.

    Google Scholar 

  • Bottou, L. (2012). Stochastic gradient descent tricks. In G. Montavon, G. Orr, & K. R. Müller (Eds.), Neural networks: Tricks of the trade (pp. 421–436). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-35289-8_25

    Chapter  Google Scholar 

  • Breiner, S. (1973). Applications manual for portable magnetometers (Vol. 395). Sunnyvale, California: Geometrics.

    Google Scholar 

  • Casamitjana, A., Puch, S., Aduriz, A., & Vilaplana, V. (2016). 3D convolutional neural networks for brain tumor segmentation: A comparison of multi-resolution architectures. International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 150–161). Cham: Springer. https://doi.org/10.1007/978-3-319-55524-9_15

    Chapter  Google Scholar 

  • Clark, D. A. (2014). Methods for determining remanent and total magnetizations of magnetic sources–a review. Exploration Geophysics, 45(4), 271–304. https://doi.org/10.1071/EG14013

    Article  ADS  Google Scholar 

  • Clark, D. A., & Emerson, D. W. (1991). Notes on rock magnetization characteristics in applied geophysical studies. Exploration Geophysics, 22(3), 547–555. https://doi.org/10.1071/EG991547

    Article  ADS  Google Scholar 

  • Cordell, L., & Taylor, P. T. (1971). Investigation of magnetization and density of a North Atlantic seamount using Poisson’s theorem. Geophysics, 36(5), 919–937. https://doi.org/10.1190/1.1440224

    Article  ADS  Google Scholar 

  • Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(7), 2121-2159.

    MathSciNet  Google Scholar 

  • Fedi, M. (1989). On the interpretation of magnetic anomalies for strong remanent magnetizations. Pure and Applied Geophysics, 130(4), 721–733. https://doi.org/10.1007/BF00881607

    Article  ADS  Google Scholar 

  • Gerovska, D., & Stavrev, P. (2006). Magnetic data analysis at low latitudes using magnitude transforms. Geophysical Prospecting, 54(1), 89–98. https://doi.org/10.1111/j.1365-2478.2006.00518.x

    Article  ADS  Google Scholar 

  • Guan, Z. N., Hou, J. S., Huang, L. P., & Yao, C. L. (1998). Inversion of Gravity and Magnetic Anomalies Using Pseudo-BP Neural Network Method and Its Application. Journal of Geophysics, 41(2), 10.

    Google Scholar 

  • Guo, L. H., Gao, R., & Zhang, G. L. (2014). Estimating the magnetization direction of sources through the correlation between reduced-to-pole anomaly and normalized source strength. Applied Mechanics and Materials, 644–650, 3793–3796. https://doi.org/10.4028/www.scientific.net/AMM.644-650.3793

    Article  Google Scholar 

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778. https://doi.org/10.1109/CVPR.2016.90

  • He, S., Cai, H., Liu, S., Xie, J., & Hu, X. (2021). Recovering 3D basement relief using gravity data through convolutional neural networks. Journal of Geophysical Research: Solid Earth, 126(10), e2021JB022611. https://doi.org/10.1029/2021JB022611

    Article  ADS  Google Scholar 

  • Hu, Z., Liu, S., Hu, X., Fu, L., Qu, J., Wang, H., & Chen, Q. (2021). Inversion of magnetic data using deep neural networks. Physics of the Earth and Planetary Interiors, 311, 106653.

    Article  Google Scholar 

  • Kansas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., & Glocker, B. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61–78. https://doi.org/10.1016/j.media.2016.10.004

    Article  Google Scholar 

  • Kingma, D., & Ba, J. (2014). Adam: A method for stochastic optimization. Computer Science. https://doi.org/10.48550/arxiv.1412.6980

    Article  Google Scholar 

  • 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. https://doi.org/10.1162/neco.1989.1.4.541

    Article  Google Scholar 

  • Li, Y., Shearer, S. E., Haney, M. M., & Dannemiller, N. (2010). Comprehensive approaches to 3D inversion of magnetic data affected by remanent magnetization. Geophysics, 75(1), L1–L11.

    Article  ADS  Google Scholar 

  • Li, Y., Tschirhart, V., & Thomas, M. D. (2017). From susceptibility to magnetization: Advances in the 3D inversion of magnetic data in the presence of significant remanent magnetization. In V. Tschirhart & M. D. Thomas (Eds.), Proceedings of Exploration 17: Proceedings of the Sixth Decennial International Conference on Mineral Exploration (pp. 239–260). Toronto: Canada.

    Google Scholar 

  • Liu, S., Fedi, M., Hu, X., Ou, Y., Baniamerian, J., & Zuo, B. (2018). Three-dimensional inversion of magnetic data in the simultaneous presence of significant remanent magnetization and self-demagnetization: Example from Daye iron-ore deposit, Hubei province, China. Geophysical Journal International, 215(1), 614–634. https://doi.org/10.1093/gji/ggy299

    Article  ADS  Google Scholar 

  • Liu, S., Feng, J., Gao, W. L., Qiu, L. Q., Liu, T. Y., & Hu, X. Y. (2013). 2D inversion for borehole magnetic data in the presence of significant remanence and demagnetization. Chinese Journal of Geophysics, 56(12), 4297–4309.

    Google Scholar 

  • Liu, S., Hu, X., Xi, Y., Liu, T., & Xu, S. (2015). 2D sequential inversion of total magnitude and total magnetic anomaly data affected by remanent magnetization. Geophysics, 80(3), K1–K12. https://doi.org/10.1190/geo2014-0019.1

    Article  ADS  Google Scholar 

  • Liu, S., Hu, X., Zhang, D., Wei, B., Geng, M., Zuo, B., & Vatankhah, S. (2020). The IDQ curve: A tool for evaluating the direction of remanent magnetization from magnetic anomalies. Geophysics, 85(5), J85–J98. https://doi.org/10.1190/geo2019-0545.1

    Article  Google Scholar 

  • Miller, C. A., Schaefer, L. N., Kereszturi, G., & Fournier, D. (2020). Three-dimensional mapping of Mt Ruapehu volcano, New Zealand, from aeromagnetic data inversion and hyperspectral imaging. Journal of Geophysical Research: Solid Earth, 125(2), e2019JB018247.

    Article  ADS  Google Scholar 

  • Pilkington, M., & Beiki, M. (2013). Mitigating remanent magnetization effects in magnetic data using the normalized source strength. Geophysics, 78(3), J25–J32. https://doi.org/10.1190/geo2012-0225.1

    Article  Google Scholar 

  • Prion, S., & Haerling, K. A. (2014). Making sense of methods and measurement: Pearson product-moment correlation coefficient. Clinical Simulation in Nursing, 10(11), 587–588.

    Article  Google Scholar 

  • Queitsch, M., Schiffler, M., Stolz, R., Rolf, C., Meyer, M., & Kukowski, N. (2019). Investigation of three-dimensional magnetization of a dolerite intrusion using airborne full tensor magnetic gradiometry (FTMG) data. Geophysical Journal International, 217(3), 1643–1655.

    CAS  ADS  Google Scholar 

  • Roest, W. R., & Pilkington, M. (1993). Identifying remanent magnetization effects in magnetic data. Geophysics, 58(5), 653–659. https://doi.org/10.1190/1.1443449

    Article  ADS  Google Scholar 

  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention (pp. 234–241). Cham: Springer.

    Google Scholar 

  • Schmidt, P. W., & Lackie, M. A. (2014). Practical considerations: Making measurements of susceptibility, remanence and Q in the field. Exploration Geophysics, 45(4), 305–313.

    Article  CAS  ADS  Google Scholar 

  • Shearer, S. E., & Li, Y. (2004). 3D Inversion of magnetic total gradient in the presence of remanent magnetization. In AGU Fall Meeting Abstracts (Vol. 2004, pp. NG31B-0871).

  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).

  • Wang, F., Chen, S., & Liu, Y. (2019). Deep learning for gravity and magnetic data interpolation. In SEG International Exposition and Annual Meeting (p. D043S117R004).

  • Wang, J., Zhou, Z., Meng, X., Liu, Y., & Fang, Y. (2023). A Novel Method for Eliminating the Strip-Shaped Interferences in Aeromagnetic Anomaly Based on Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–11.

    Article  CAS  Google Scholar 

  • Wang, M. Y., Di, Q. Y., Xu, K., & Wang, R. (2004). Magnetization vector inversion equations and 2D forward and inversed model study. Chinese Journal of Geophysics - Chinese Edition, 47(3), 528–534.

    Google Scholar 

  • Wang, R., Xiong, J., Liu, Q., & Xue, R. J. (2022). Inversion of gravity anomalies based on a deep neural network. Geophysical and Geochemical Exploration, 46(2), 451–458.

    Google Scholar 

  • Wang, Y. C., Liu, L. T., & Xu, H. Z. (2020). The identification of gravity anomaly body based on the convolutional neural network. Geophysical and Geochemical Exploration, 2, 394–400.

    Google Scholar 

  • Yang, Q., Hu, X., Liu, S., Jie, Q., Wang, H., & Chen, Q. (2021). 3-D gravity inversion based on deep convolution neural networks. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    CAS  Google Scholar 

  • Zeiler, M. D. (2012). Adadelta: an adaptive learning rate method. Computer Science. https://doi.org/10.48550/arxiv.1212.5701

    Article  Google Scholar 

  • Zhang, L., Zhang, G., Liu, Y., & Fan, Z. (2021a). Deep Learning for 3-D Inversion of Gravity Data. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–18.

    Google Scholar 

  • Zhang, X., Wang, H., Wang, Y., Liu, J., & Chen, J. (2016). Application of high-precision aeromagnetic data to expanding prospecting potential of the Yeshan iron deposit. Geology and Exploration, 52(6), 1138–1146.

    CAS  Google Scholar 

  • Zhang, Z., Liao, X., Cao, Y., Hou, Z., Fan, X., Xu, Z., & Shi, Z. (2021b). Joint gravity and gravity gradient inversion based on deep learning. Chinese Journal of Geophysics, 64(4), 1435–1452.

    Google Scholar 

  • Zhang, Z., Lu, R., Liao, X., Xu, Z., Qiao, Z., Fan, X., & Lu, S. (2021c). Inversion of magnetic anomaly and magnetic gradient anomaly based on fully convolution network. Progress in Geophysics, 1, 325–337.

    Google Scholar 

  • Zhang, Z., Yao, Y., & Shi, Z. (2022). Deep learning for potential field edge detection. Chinese Journal of Geophysics, 65(5), 1785–1801.

    Google Scholar 

  • Zheng, Y., Xiao, W., Zheng, Y. F., Xiao, W. J., & Zhao, G. (2013). Introduction to tectonics of China. Gondwana Research, 23(4), 1189–1206. https://doi.org/10.1016/j.gr.2012.10.001

    Article  ADS  Google Scholar 

  • Zhou, Z., Wang, J., Meng, X., & Fang, Y. (2023). High-Precision Intelligence Denoising of Potential Field Data Based on RevU-Net. IEEE Geoscience and Remote Sensing Letters, 20, 1–5.

    Google Scholar 

  • Zhu, D., Hu, X., Liu, S., Li, H., & Zuo, B. (2022). Can Targeted Source Information be Extracted from Superimposed Magnetic Anomalies? Journal of Geophysical Research: Solid Earth. https://doi.org/10.1029/2022JB024279

    Article  Google Scholar 

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Funding

This work was supported in part by National Natural Science Foundation of China under Grant 41974161; in part by the Fundamental Research Funds for the Central Universities under Grant 2-9-2019-040.

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Authors and Affiliations

  1. School of Geophysics and Information Technology, China University of Geosciences, Beijing, China

    Weichen Li, Jun Wang & XiaoHong Meng

  2. CAS Engineering Laboratory for Deep Resources Eguipment and Technology, Insititute of Geology and Geophyics, Chinese Academy of Sciences, Beijing, China

    Biao Xi

  3. College of Earth and Planetary Sciences, Chinese Academy of Sciences, Beijing, China

    Biao Xi

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  1. Weichen Li
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  2. Jun Wang
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  4. Biao Xi
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Contributions

WeiChen Li, Jun Wang, and XiaoHong Meng contributed to the study conception and design. Material preparation, data collection and analysis were performed by WeiChen Li and Jun Wang. The first draft of the manuscript was written by WeiChen Li. WeiChen Li, Jun Wang, and Biao Xi helped perform the analysis with constructive discussions. All authors read and approved the final manuscript.

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Correspondence to Jun Wang or XiaoHong Meng.

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The original online version of this article was revised: In this article the author’s name WeiChen Li was incorrectly written as WeChen Li.

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Li, W., Wang, J., Meng, X. et al. Separation of the Total Magnetic Anomalies into Induced and Remanent Magnetization Based on Deep Learning. Pure Appl. Geophys. 181, 151–169 (2024). https://doi.org/10.1007/s00024-023-03391-y

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