计算机科学
雷达
火星探测计划
稳健性(进化)
探地雷达
合并(版本控制)
推论
人工神经网络
遥感
地质学
算法
合成孔径雷达
人工智能
数据挖掘
电信
生物化学
化学
物理
天文
情报检索
基因
作者
Giacomo Roncoroni,Emanuele Forte,M. Pipan
出处
期刊:Geophysics
[Society of Exploration Geophysicists]
日期:2022-11-23
卷期号:88 (2): A7-A12
被引量:3
标识
DOI:10.1190/geo2022-0466.1
摘要
The integration of ground-penetrating radar data at various frequencies collected with different antennas or with the use of swept-frequency radars opens up interesting perspectives in the study of the subsurface at different resolutions. Our methodology is a semisupervised deep learning algorithm based on bidirectional long short-term memory to automatically merge varying numbers of data sets at different frequencies. Neural network (NN) training is done directly on the inference data by minimizing a custom loss function based on the L2 norm of all the input data, weighted on the custom merging area, and the single output trace. The inference of the trained NN is applied to the same data. Our algorithm is tested on synthetic data simulating the Mars conditions and on Radar Imager for Mars’ Subsurface Exploration radar data collected in the Jezero crater during the Mars2020 mission of the Perseverance rover, showing successful performances and robustness.
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