Detection and Handling of Noise in Laser Altimetry Data: a Prerequisite for Tides Derivation

<p>The ESA/Jaxa mission BepiColombo[1] to Mercury will arrive in orbit in 2025. Onboard is the BepiColombo Laser Altimeter (BELA)[2], which will be used to scan the planetary surface. We plan on using these data to derive the tidal parameter h₂[3]. To achieve an accurate result it is necessary to identify and eliminate noise in the laser altimeter records[4]. We present a strategy to find[5], [6] and neutralize the non-Gaussian noise contributions which contribute the most to the uncertainty in the derived Love number.</p> <p>&#160;References</p> <p lang="zxx">[1] J. Benkhoff <em>et al.</em>, &#8216;BepiColombo&#8212;Comprehensive exploration of Mercury: Mission overview and science goals&#8217;, <em>Planet. Space Sci.</em>, vol. 58, no. 1, pp. 2&#8211;20, Jan. 2010, doi: 10.1016/j.pss.2009.09.020.</p> <p lang="zxx">[2] N. Thomas <em>et al.</em>, &#8216;The BepiColombo Laser Altimeter&#8217;, <em>Space Sci. Rev.</em>, vol. 217, no. 1, p. 25, Feb. 2021, doi: 10.1007/s11214-021-00794-y.</p> <p lang="zxx">[3] R. N. Thor <em>et al.</em>, &#8216;Prospects for measuring Mercury&#8217;s tidal Love number h2 with the BepiColombo Laser Altimeter&#8217;, <em>Astron. Astrophys.</em>, vol. 633, p. A85, Jan. 2020, doi: 10.1051/0004-6361/201936517.</p> <p lang="zxx">[4] O. J. Stenzel, I. Hall, and M. Hilchenbach, &#8216;Mercury Tide Parameter Estimation from Laser Altimeter Records&#8217;, LPI contributions vol. 2678, p. 1990, Mar. 2022.</p> <p lang="zxx">[5] O. Stenzel and M. Hilchenbach, &#8216;Towards machine learning assisted error identification in orbital laser altimetry for tides derivation&#8217;, pp. EPSC2021-688, Sep. 2021, doi: 10.5194/espc2021-688.</p> <p lang="zxx">[6] O. Stenzel, R. Thor, and M. Hilchenbach, &#8216;Error identification in orbital laser altimeter data by machine learning&#8217;, pp. EGU21-14749, Apr. 2021, doi: 10.5194/egusphere-egu21-14749.</p>