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2023-05-20
Estimation of Thickness and Dielectric Characteristics of Sea Ice from Near-Field EM Measurements Using Deep Learning for Large Scale Polar Ice Probing
By
Progress In Electromagnetics Research B, Vol. 100, 73-89, 2023
Abstract
The near and far field EM responses over layered media have long been exploited in diversified applications such as remote sensing, monitoring and communication. In this work, we utilize the near field dependence of the EM fields of a three layered structure resembling air-sea ice-sea water to estimate the thickness and dispersion characteristics of sea ice using deep learning technique. We explore two key methods of field measurement termed as the fixed and scaled sweep methods. In the fixed radial sweep method, the receiver distance and height from the source are kept constant, and in the scaled sweep method both the receiver distance and height are set as a scaled function of the operating wavelength. A synthetic training dataset has been generated (using analytical computation and FEM simulation) in the low MHz band, which is used to train a deep learning model. The model is tested on different test datasets with frequencies inside, below and above the training limits. Even though the fixed sweep method is simpler to implement, the scaled sweep appears to perform better across the wide range of test frequency, both in and outside the training range. When the test frequency is inside the training range, the percentage errors for thickness, dielectric constant, and loss tangent were found to be <2%, <10%, and <5%, respectively, for the fixed radial sweep, whereas for the scaled sweep the percentage error is < 1% for all three measurement parameters. When the test frequency deviates further from the training range, the percentage error gradually increases. Later, we investigate the problem of determining sea ice thickness assuming a priori knowledge of sea ice dielectric parameters, and results show that the model estimates the thickness of the sea ice bulk with error as low as 0.1%.
Citation
Mohammad Shifatul Islam, Sadman Shafi, and Mohammad Ariful Haque, "Estimation of Thickness and Dielectric Characteristics of Sea Ice from Near-Field EM Measurements Using Deep Learning for Large Scale Polar Ice Probing," Progress In Electromagnetics Research B, Vol. 100, 73-89, 2023.
doi:10.2528/PIERB22122005
References

1. Annan, A. P, "Radio interferometry depth sounding: Part I --- Theoretical discussion," Geophysics, Vol. 38, No. 3, 557-580, Jun. 1973. [Online]. Available: https://doi.org/10.1190/1.1440360.
doi:10.1190/1.1440360

2. Rossiter, J. R., G. A. LaTorraca, A. P. Annan, D. W. Strangway, and G. Simmons, "Radio interferometry depth sounding: Part II --- Experimental results," Geophysics, Vol. 38, No. 3, 581-599, Jun. 1973.
doi:10.1190/1.1440361

3. Tsang, L., J. A. Kong, and G. Simmons, "Interference patterns of a horizon-tal electric dipole over layered dielectric media," Journal of Geophysical Re-search (1896-1977), Vol. 78, No. 17, 3287-3300, Jun. 1973. [Online]. Available: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/JB078i017p03287.

4. Liao, D. H. and K. Sarabandi, "Near-earth wave propagation characteristics of electric dipole in presence of vegetation or snow layer," IEEE Transactions on Antennas and Propagation, Vol. 53, No. 11, 3747-3756, Nov. 2005.
doi:10.1109/TAP.2005.856347

5. Kong, J., "Electromagnetic fields due to dipole antennas over strati ed anisotropic media," Geophysics, Vol. 37, No. 6, 985-996, 1972.
doi:10.1190/1.1440321

6. Chew, W. C. and J. A. Kong, "Electromagnetic eld of a dipole on a two-layer Earth," Geophysics, Vol. 46, No. 3, 309-315, Mar. 1981.
doi:10.1190/1.1441201

7. Lee, J., A. J. Park, Y. Tanabe, A. S. Poon, and S. Kim, "A microwave method to remotely assess the abdominal fat thickness," AIP Advances, Vol. 11, No. 3, 035111, 2021.
doi:10.1063/5.0025865

8. Shah, S. R. M., N. B. Asan, J. Velander, J. Ebrahimizadeh, M. D. Perez, V. Mattsson, T. Blokhuis, and R. Augustine, "Analysis of thickness variation in biological tissues using microwave sensors for health monitoring applications," IEEE Access, Vol. 7, 156 033-156 043, 2019.
doi:10.1109/ACCESS.2019.2949179

9. Scharfetter, H., T. Schlager, R. Stollberger, R. Felsberger, H. Hutten, and H. Hinghofer-Szalkay, "Assessing abdominal fatness with local bioimpedance analysis: Basics and experimental ndings," International Journal of Obesity, Vol. 25, No. 4, 502-511, 2001.
doi:10.1038/sj.ijo.0801556

10. Donelli, M., "A rescue radar system for the detection of victims trapped under rubble based on the independent component analysis algorithm," Progress In Electromagnetics Research M, Vol. 19, 173-181, 2011.
doi:10.2528/PIERM11061206

11. Pasolli, E., F. Melgani, M. Donelli, R. Attoui, and M. De Vos, "Automatic detection and classi cation of buried objects in GPR images using genetic algorithms and support vector machines," IGARSS 2008 --- 2008 IEEE International Geoscience and Remote Sensing Symposium, Vol. 2, II-525, IEEE, 2008.

12. Pasolli, E., F. Melgani, and M. Donelli, "Gaussian process approach to buried object size estimation in GPR images," IEEE Geoscience and Remote Sensing Letters, Vol. 7, No. 1, 141-145, 2009.
doi:10.1109/LGRS.2009.2028697

13. Alibakhshikenari, M., B. S. Virdee, C. H. See, R. A. Abd-Alhameed, F. Falcone, and E. Limiti, "Super-wide impedance bandwidth planar antenna for microwave and millimeter-wave applications," Sensors, Vol. 19, No. 10, 2306, 2019.
doi:10.3390/s19102306

14. Alibakhshikenari, M., B. S. Virdee, C. H. See, P. Shukla, S. Salekzamankhani, R. A. Abd-Alhameed, F. Falcone, and E. Limiti, "Study on improvement of the performance parameters of a novel 0.41{ 0.47 THz on-chip antenna based on metasurface concept realized on 50 μm GAAS-layer," Scienti c Reports, Vol. 10, No. 1, 11034, 2020.
doi:10.1038/s41598-020-68105-z

15. Altaf, A., A. Iqbal, A. Smida, J. Smida, A. A. Althuwayb, S. Hassan Kiani, M. Alibakhshikenari, F. Falcone, and E. Limiti, "Isolation improvement in UWB-MIMO antenna system using slotted stub," Electronics, Vol. 9, No. 10, 1582, 2020.
doi:10.3390/electronics9101582

16. Alibakhshikenari, M., B. S. Virdee, P. Shukla, Y. Wang, L. Azpilicueta, M. Naser-Moghadasi, C. H. See, I. Elfergani, C. Zebiri, R. A. Abd-Alhameed, and et, "Impedance bandwidth improvement of a planar antenna based on metamaterial-inspired T-matching network," IEEE Access, Vol. 9, 67916-67927, 2021.
doi:10.1109/ACCESS.2021.3076975

17. Kiourti, A., C. W. Lee, J. Chae, and J. L. Volakis, "A wireless fully passive neural recording device for unobtrusive neuropotential monitoring," IEEE Transactions on Biomedical Engineering, Vol. 63, No. 1, 131-137, 2015.
doi:10.1109/TBME.2015.2458583

18. Lee, C. W., A. Kiourti, and J. L. Volakis, "Miniaturized fully passive brain implant for wireless neuropotential acquisition," IEEE Antennas and Wireless Propagation Letters, Vol. 16, 645-648, 2016.

19. Balanis, C. A., Antenna Theory: Analysis and Design, John Wiley & Sons, 2015.

20. Islam, M. S., S. Sha , M. I. Hasan, and M. A. Haque, "Low frequency near field interferometry for characterization of lossy dielectric and an investigation on sea ice," IEEE Transactions on Geoscience and Remote Sensing (Early Access), 1-11, 2020.

21. Trentini, G. V., "Partially reflecting sheet arrays," IRE Transactions on Antennas and Propagation, Vol. 4, No. 4, 666-671, 1956.
doi:10.1109/TAP.1956.1144455

22. Islam, M. S., S. Sha , and M. A. Haque, "Development of an experimental model of low frequency dipole radiation in the presence of multilayered structures," SoutheastCon 2021, 1-6, IEEE, 2021.

23. Chollet, F., Deep Learning with Python, Simon and Schuster, 2021.

24. Heaton, J., "Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning," Genetic Programming and Evolvable Machines, Vol. 19, 305-307, 2018.
doi:10.1007/s10710-017-9314-z

25. Gao, Y., H. Liu, X. Wang, and K. Zhang, "On an artificial neural network for inverse scattering problems," Journal of Computational Physics, Vol. 448, 110771, 2022.
doi:10.1016/j.jcp.2021.110771

26. Zhang, P., P. Meng, W. Yin, and H. Liu, "A neural network method for time-dependent inverse source problem with limited-aperture data," Journal of Computational and Applied Mathematics, Vol. 421, 114842, 2023.
doi:10.1016/j.cam.2022.114842

27. Yin, W., W. Yang, and H. Liu, "A neural network scheme for recovering scattering obstacles with limited phaseless far-field data," Journal of Computational Physics, Vol. 417, 109594, 2020.
doi:10.1016/j.jcp.2020.109594

28. Yin, W., J. Ge, P. Meng, and F. Qu, "A neural network method for the inverse scattering problem of impenetrable cavities," Electronic Research Archive, Vol. 28, No. 2, 1123-1142, 2020.
doi:10.3934/era.2020062

29. Islam, M. S., S. Sha , and M. A. Haque, "Low-frequency electromagnetic characterization of layered media using deep neural network," 2021 International Symposium on Antennas and Propagation (ISAP), 1-2, IEEE, 2021.

30. Stogryn, A., "Equations for calculating the dielectric constant of saline water (correspondence)," IEEE Transactions on Microwave Theory and Techniques, Vol. 19, No. 8, 733-736, 1971.
doi:10.1109/TMTT.1971.1127617

31. Addison, J. R., "Electrical properties of saline ice," Journal of Applied Physics, Vol. 40, No. 8, 3105-3114, 1969.
doi:10.1063/1.1658149

32. Stogryn, A., "An analysis of the tensor dielectnc constant of sea ice at microwave frequencies," IEEE Transactions on Geoscience and Remote Sensing, No. 2, 147-158, 1987.
doi:10.1109/TGRS.1987.289814

33. Wentworth, F. and M. Cohn, "Electrical properties of sea ice at 0.1 to 30 mc/s," J. Res. NBS, Vol. 68, 681-691, 1964.

34. Evans, S., "Dielectric properties of ice and snow --- A review," Journal of Glaciology, Vol. 5, No. 42, 773-792, 1965.
doi:10.3189/S0022143000018840

35. Buchanan, S., M. Ingham, and G. Gouws, "The low frequency electrical properties of sea ice," Journal of Applied Physics, Vol. 110, No. 7, 074908, 2011.
doi:10.1063/1.3647778

36. Hallikainen, M. and D. P. Winebrenner, "The physical basis for sea ice remote sensing," Washington DC American Geophysical Union Geophysical Monograph Series, Vol. 68, 29-46, 1992.

37. Holt, B., P. Kanagaratnam, S. P. Gogineni, V. C. Ramasami, A. Mahoney, and V. Lytle, "Sea ice thickness measurements by ultrawideband penetrating radar: First results," Cold Regions Science and Technology, Vol. 55, No. 1, 33-46, 2009.
doi:10.1016/j.coldregions.2008.04.007

38. Tilling, R. L., A. Ridout, and A. Shepherd, "Estimating arctic sea ice thickness and volume using cryosat-2 radar altimeter data," Advances in Space Research, Vol. 62, No. 6, 1203-1225, 2018, The CryoSat Satellite Altimetry Mission: Eight Years of Scienti c Exploitation. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0273117717307901.
doi:10.1016/j.asr.2017.10.051

39. Lindsay, R. and A. Schweiger, "Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations," The Cryosphere, Vol. 9, No. 1, 269-283, 2015.
doi:10.5194/tc-9-269-2015

40. Schanda, E., Physical Fundamentals of Remote Sensing, Springer Science & Business Media, 2012.

41. Bourke, R. H. and R. P. Garrett, "Sea ice thickness distribution in the arctic ocean," Cold Regions Science and Technology, Vol. 13, No. 3, 259-280, 1987.
doi:10.1016/0165-232X(87)90007-3

42. Eicken, H., W. Tucker, and D. Perovich, "Indirect measurements of the mass balance of summer arctic sea ice with an electromagnetic induction technique," Annals of Glaciology, Vol. 33, 194-200, 2001.
doi:10.3189/172756401781818356

43. Thomas, D. and G. Dieckmann, "Sea Ice," 2nd Edition, Wiley-Blackwell, Jan. 2010.

44. Chew, W., Waves and Fields in Inhomogeneous Media, Springer, 1990.

45. Cheng, D. K., Field and Wave Electromagnetics, Pearson Education, India, 1989.

46. Wait, J. R., A. Cullen, V. Fock, J. Wait, and H. Hagger, "Electromagnetic waves in strati ed media," Physics Today, Vol. 17, No. 4, 76, 1964.
doi:10.1063/1.3051553

47. Jackson, J. D., Classical Electrodynamics, 1999.

48. Inc., C., "Comsol,", 2020. [Online]. Available: http://www.comsol.com/products/multiphysics/.

49. Klein, L. and C. Swift, "An improved model for the dielectric constant of sea water at microwave frequencies," IEEE Transactions on Antennas and Propagation, Vol. 25, No. 1, 104-111, 1977.
doi:10.1109/TAP.1977.1141539

50. Haque, M. A., M. S. Islam, S. Sha , M. Hasan, et al. "Non-invasive measurement of sea ice thickness using low frequency EM waves," Anyeshan Limited, Tech. Rep., 2022.