A Geometric Reconstrction Method for Predicting Shape of Irregular Rocks under Moon’s Subsurface Using Lunar Penetrating Radar Based on a Deep Learning Algorithm

LI Yangping; HUANG Ling; WANG Ke; ZHAO Haifeng

doi:10.11999/JEIT211142

Volume 44 Issue 4

Apr. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(4): 1222-1230

LI Yangping, HUANG Ling, WANG Ke, ZHAO Haifeng. A Geometric Reconstrction Method for Predicting Shape of Irregular Rocks under Moon’s Subsurface Using Lunar Penetrating Radar Based on a Deep Learning Algorithm[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1222-1230. doi: 10.11999/JEIT211142

Citation:

LI Yangping, HUANG Ling, WANG Ke, ZHAO Haifeng. A Geometric Reconstrction Method for Predicting Shape of Irregular Rocks under Moon’s Subsurface Using Lunar Penetrating Radar Based on a Deep Learning Algorithm[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1222-1230. doi: 10.11999/JEIT211142

Citation:

LI Yangping, HUANG Ling, WANG Ke, ZHAO Haifeng. A Geometric Reconstrction Method for Predicting Shape of Irregular Rocks under Moon’s Subsurface Using Lunar Penetrating Radar Based on a Deep Learning Algorithm[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1222-1230. doi: 10.11999/JEIT211142

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A Geometric Reconstrction Method for Predicting Shape of Irregular Rocks under Moon’s Subsurface Using Lunar Penetrating Radar Based on a Deep Learning Algorithm

doi: 10.11999/JEIT211142

LI Yangping^{1, 2},
HUANG Ling³,
WANG Ke²,
ZHAO Haifeng^{1, 2
,
,}

1.
University of Chinese Academy of Sciences, Beijing 100086, China
2.
Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
3.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Funds: The National Natural Science Foundation of China (61827803, 42171445)

Received Date: 2021-10-18
Accepted Date: 2022-03-16
Rev Recd Date: 2022-03-14

Available Online: 2022-03-21

Publish Date: 2022-04-18

Abstract

Abstract

The subsurface structure and composition of moon are always heterogeneous, also, both geometric shape of buried materials and electromagnetic characteristics of formations are complicated. Therefore, it is very challenging to interpret Lunar Penetrating Radar (LPR) data and segment subsurface layers accurately and reliably. In this paper, deep learning method is utilized to reconstruct geological models from simulated LPR signal dataset. First, the geometric contours of lunar rock are extracted based on the photos of the lunar rock samples from Apollo 14, using image edge detection. The principal component analysis method is used to reduce the dimensionality of LPR data. Then, using the back propagation algorithm based on Root Mean Square prop (RMSprop), an artificial neural network is built to predict geometric characteristics of single buried basaltic rock. The results show that the depth of the buried rock with high-contrast dielectric constant and complex geometric features has been predicted with high accuracies, with the R-square of 0.93. Also, an artificial neural network model is also created to reconstruct geometric characteristics of heterogeneous model with randomly distributed lunar rocks. The preliminary results provide an initial attempt for development of data-driven subsurface imaging techniques in the geoscience field.
- Lunar Penetrating Radar (LPR),
- Deep learning,
- Inversion of geophysical formation,
- Numerical simulation

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References(20)

References

[1]	YANG Wei and LIN Yangting. New lunar samples returned by Chang’E-5: Opportunities for new discoveries and international collaboration[J]. The Innovation, 2021, 2(1): 100070. doi: 10.1016/j.xinn.2020.100070
[2]	ZHANG Jinhai, ZHOU Bin, LIN Yangting, et al. Lunar regolith and substructure at Chang’E-4 landing site in South Pole–Aitken basin[J]. Nature Astronomy, 2021, 5(1): 25–30. doi: 10.1038/s41550-020-1197-x
[3]	ZHANG Jinhai, YANG Wei, HU Sen, et al. Volcanic history of the Imbrium basin: A close-up view from the lunar rover Yutu[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(17): 5342–5347. doi: 10.1073/pnas.1503082112
[4]	ZHANG Ling, ZENG Zhaofa, LI Jing, et al. A story of regolith told by lunar penetrating radar[J]. Icarus, 2019, 321: 148–160. doi: 10.1016/j.icarus.2018.11.006
[5]	TRAVASSOS X L, AVILA S L, and IDA N. Artificial neural networks and machine learning techniques applied to ground penetrating radar: A review[J]. Applied Computing and Informatics, 2020, 17(2): 296–308. doi: 10.1016/j.aci.2018.10.001
[6]	侯斐斐, 施荣华, 雷文太, 等. 面向探地雷达B-scan图像的目标检测算法综述[J]. 电子与信息学报, 2020, 42(1): 191–200. doi: 10.11999/JEIT190680 HOU Feifei, SHI Ronghua, LEI Wentai, et al. A review of target detection algorithm for GPR B-scan processing[J]. Journal of Electronics &Information Technology, 2020, 42(1): 191–200. doi: 10.11999/JEIT190680
[7]	王建, 袁宵, 李禹, 等. 利用互相关和Hough变换快速检测探地雷达目标[J]. 电子与信息学报, 2013, 35(5): 1156–1162. doi: 10.3724/SP.J.1146.2012.01134 WANG Jian, YUAN Xiao, LI Yu, et al. Fast detection of ground penetrating radar objects based on cross correlation and Hough transform[J]. Journal of Electronics &Information Technology, 2013, 35(5): 1156–1162. doi: 10.3724/SP.J.1146.2012.01134
[8]	GAO Jie, YUAN Dongdong, TONG Zheng, et al. Autonomous pavement distress detection using ground penetrating radar and region-based deep learning[J]. Measurement, 2020, 164: 108077. doi: 10.1016/j.measurement.2020.108077
[9]	LI Shuwei, GU Xingyu, XU Xiangrong, et al. Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm[J]. Construction and Building Materials, 2021, 273: 121949. doi: 10.1016/j.conbuildmat.2020.121949
[10]	KHUDOYAROV S, KIM N, and LEE J J. Three-dimensional convolutional neural network–based underground object classification using three-dimensional ground penetrating radar data[J]. Structural Health Monitoring, 2020, 19(6): 1884–1893. doi: 10.1177/1475921720902700
[11]	LIU T, KLOTZSCHE A, PONDKULE M, et al. Estimation of subsurface cylindrical object properties from GPR full-waveform inversion[C]. The 2017 9th International Workshop on Advanced Ground Penetrating Radar, Edinburgh, UK, 2017: 1–4.
[12]	GIANNAKIS I, GIANNOPOULOS A, and WARREN C. A machine learning scheme for estimating the diameter of reinforcing bars using ground penetrating radar[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(3): 461–465. doi: 10.1109/LGRS.2020.2977505
[13]	CARLSON I C and WALTON W J A. Apollo-14 rock samples[R]. JSC 14240, 1978.
[14]	GIANNOPOULOS A. Modelling ground penetrating radar by GprMax[J]. Construction and Building Materials, 2005, 19(10): 755–762. doi: 10.1016/j.conbuildmat.2005.06.007
[15]	DENG Caixia, WANG Guibin, and YANG Xinrui. Image edge detection algorithm based on improved canny operator[C]. 2013 International Conference on Wavelet Analysis and Pattern Recognition, Tianjin, China, 2013: 168–172.
[16]	GOLD T, BILSON E, and BARON R L. Electrical properties of Apollo 17 rock and soil samples and a summary of the electrical properties of lunar material at 450 MHz frequency[C]. The 7th Lunar Science Conference, New York, USA, 1976: 2593–2603.
[17]	WOLD S, ESBENSEN K, and GELADI P. Principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 1987, 2(1/3): 37–52. doi: 10.1016/0169-7439(87)80084-9
[18]	PENNINGTON J, SCHOENHOLZ S S, and GANGULI S. Resurrecting the sigmoid in deep learning through dynamical isometry: Theory and practice[C]. The 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 4788–4798.
[19]	HINTON G, SRIVASTAVA N, and SWERSKY K. Neural networks for machine learning lecture 6a overview of mini-batch gradient descent[J]. Cited on, 2012, 14(8): 2.
[20]	FIELD A. Logistic Regression[M]. FIELD A. Discovering Statistics Using SPSS. London: SAGE Publications Ltd, 2009: 264–315.