Abstract
Mimoun et al. (Space Sci Rev 211(1–4):383–428, 2017) developed a pre-landing noise model of the Martian seismometer package SEIS onboard InSight that analysed all the external and internal noise sources. We updated the environmental and instrumental parameters of the model as well as the ground properties with InSight mission data. We compared the output of the in situ noise model to the actual noise measured during the full mission for each individual noise source as well as for the full noise model. We evaluate in detail the efficiency of the model to fit the measured data and discuss the transient noise and other sources that were not included in the model. The main noise sources in the seismic bandwidth are the pressure noise and the lander noise, which is increased from the pre-landing model and overestimated when compared to the data; the magnetic field noise was overestimated in the pre-landing model and is now found to be negligible. The conclusions and models from this study could benefit future space missions.
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Acknowledgements
This study benefited from the financial support of the Centre National d’Études Spatiales (CNES) and ISAE-SUPAERO. AS acknowledges funding from the CNES Postdoctoral fellowship. This work was performed in part at the Jet Propulsion Laboratory, California Institute of Technology under a grant from the National Aeronautics and Space Administration (80NM0018D0004). LP was supported by appointments to the NASA Postdoctoral Program at the NASA Jet Propulsion Laboratory, California Institute of Technology, administered by Oak Ridge Associated Universities under contract with NASA. Thanks to Charles Yana (CNES) and Emilien Gaudin (Telespazio) for providing the list of VBB and TCDM operations. Thanks to Nicolas Verdier (CNES) for our discussions on the thermal noise of the VBB. We acknowledge NASA, CNES, their partner agencies and Institutions (UKSA, SSO, DLR, JPL, IPGP-CNRS, ETHZ, IC, MPS-MPG) and the flight operations team at JPL, SISMOC, MSDS, IRIS-DMC and PDS for providing SEED SEIS data. This is InSight Contribution Number 314.
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Note by the Editor: This is a Special Communication linked to the topic collection on the InSight mission published in Space Science Reviews. In addition to invited review papers and topical collections, Space Science Reviews publishes unsolicited Special Communications. These are papers linked to an earlier topical volume/collection, report-type papers, or timely papers dealing with a strong space-science-technology combination (such papers summarize the science and technology of an instrument or mission in one paper).
Appendices
Appendix A: Minor Contributors Unchanged from Mimoun et al. (2017)
The self noise and the acquisition noise are modelled the same way as in (Mimoun et al. 2017). They are shown on Fig. 18 for the self noise and Fig. 19 for the acquisition noise. Some of the noise at night is at the self noise level.
Appendix B: Comparison Between Environment Assumptions from Mimoun et al. (2017) and Measurements
This appendix compares the actual environment characteristics spectral measurements by InSight to the models of Mimoun et al. (2017). The magnetic field assumptions are shown on Fig. 20, the wind speed squared on Fig. 21 and the temperature inside the WTS is shown on Fig. 22.
The differences between the SCIT and the assumptions from Mimoun et al. (2017) can be explained by Fig. 23, which shows that the first order low-pass filter model of the WTS is insufficient. In particular, the slope and amplitude of the SCIT is not fitted well by the atmospheric temperature filtered by a modelled WTS.
Appendix C: Environment Parameters Detailed Assumptions
3.1 C.1 Wind Probabilistic ASD
The ASD of the wind speed squared is computed for all the mission duration (using TWINS data at 1 sample per second, via a Welch’s power spectral density estimate algorithm with a 10-minute long Hamming window and \(50\%\) overlap, see Sect. 2). A bimodal Gaussian distribution is fitted to the data, for each frequency bin. Figure 24 shows a vertical cut through the probabilistic ASD of Fig. 6, at frequency \(4.4\times10^{-2} \text{ Hz}\), i.e. the distribution of the wind speed squared ASD values for the frequency bin at \(4.4\times10^{-2} \text{ Hz}\).
3.2 C.2 Wind Speed
The wind speed on Mars is often modelled with a Weibull distribution (Lorenz 1996). Figure 25 shows the statistics of the wind speed as measured by the TWINS instrument, and the fitted Weibull distribution for day and night, with the median and \(\pm 1 \sigma \) values. The parameters of the fitted Weibull distribution are coherent with those found by Viúdez-Moreiras et al. (2022), as shown in Table 9. The differences are most likely due to the different datasets used (different start and end times and data selection), but the overall values and evolution with local time remain consistent.
3.3 C.3 Wind Direction
Figure 26 shows the statistics of the wind direction as measured by the TWINS instrument. The highest peak has been chosen for each period for the lander noise calculation. A more complex model could take into account the variation of angle with each hour and throughout the year, but this was not included in this work since it is more advanced than what was done in Mimoun et al. (2017).
3.4 C.4 Ground Structure and Compliance
Figure 27 shows the ground structure used for the modelling of the pressure noise and the lander noise. The structure below the surface is extracted from Carrasco et al. (2023). From this structure, we model the horizontal and vertical compliance below, as well as the tilt compliance entering in the computation of the horizontal pressure noise (Fig. 28).
Appendix D: Probability Distributions of the Measured Noise and Some Noise Sources at 0.1 Hz and 0.9 Hz
In order to have a better picture of the measured noise and the modelled noise, we plot a vertical cut through the probabilistic ASD of Fig. 2 for two frequencies, showing the probability distribution of the noise sources and the histogram of the noise. The two selected frequencies are 0.1 Hz and 0.9 Hz (and not 1 Hz to avoid the tick noise). We model the noise sources as Gaussian distributions, but this is only an approximation: the computations in the model only estimate the median and \(\pm 1 \sigma \) for each noise source, not the full distribution. We show the results in Figs. 29, 30, 31 and 32 for each component and day and night.
Appendix E: Noise Before Deployment of the Wind and Thermal Shield
The VBB measurements before the deployment of the Wind and Thermal Shield allow to characterise the noise without the WTS, and therefore generated by the wind drag on the seismometer. There are a few caveats to the comparison to the noise for the full mission which has been done on Fig. 33: first of all, the different instrument mode used (the instrument is in engineering mode before the WTS installation and scientific mode after that, with a different self noise); also, there are fewer data before the installation of the WTS, and the data are not continuously recorded throughout the day.
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Pinot, B., Mimoun, D., Murdoch, N. et al. The In Situ Evaluation of the SEIS Noise Model. Space Sci Rev 220, 26 (2024). https://doi.org/10.1007/s11214-024-01056-3
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DOI: https://doi.org/10.1007/s11214-024-01056-3