Towards machine learning assisted error identification in orbital laser altimetry for tides derivation

Laser altimetry experiments on the NASA MESSENGER mission [1], and on the currently on cruise ESA/JAXA BepiColombo Mission [2,3] did and are going to yield, respectively, a plethora of range measurements of the surface of Mercury. Orbital laser altimetry can be used to derive tidal parameters, which can in turn be used to infer properties of a body’s interior [4,5]. The derivation of tidal parameters requires large datasets of precise and accurate measurements. Errors as well as outliers can degrade the quality of the computed tidal parameters. While many outliers can be filtered though conventional automated processes, other errors could only be identified by human supervision. In the face of the amount of data involved, systematic user interaction at the error identification step becomes unpractical. A neural network trained with user expertise could help spotting outliers and errors and would improve the derived parameters in accuracy and precession. We started developing a neural network based on the pytorch framework[6] and compared the performance with a small training dataset form the MESSENGER Laser Altimeter (MLA) for a linear and a convolutional network. The results were much in favour of the linear network [7]. In this presentation we explore the reasons behind bad convolutional network performance with extended training and test datasets. We are going to show our results for the filtered datasets and the impact this has on the derived tidal parameters. The filtering with an artificial neural network might be useful for other applications, as well.

1. Cavanaugh, J. F. et al. The Mercury Laser Altimeter Instrument for the MESSENGER Mission. Space Sci Rev 131, 451–479 (2007).

2. Benkhoff, J. et al. BepiColombo—Comprehensive exploration of Mercury: Mission overview and science goals. Planetary and Space Science 58, 2–20 (2010).

3. Thomas, N. et al. The BepiColombo Laser Altimeter. Space Sci Rev 217, 25 (2021).

4. Thor, R. N. et al. Determination of the lunar body tide from global laser altimetry data. J Geod 95, 4 (2021).

5. Thor, R. N. et al. Prospects for measuring Mercury’s tidal Love number h2 with the BepiColombo Laser Altimeter. A&A 633, A85 (2020).

6. Paszke, A., et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, In: Advances in Neural Information Processing Systems 32, pp 8024–8035, 2019.

7. Stenzel, O., Thor, R., and Hilchenbach, M.: Error identification in orbital laser altimeter data by machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14749, https://doi.org/10.5194/egusphere-egu21-14749, 2021.