MERTIS at Mercury: Mapping the Hermean Surface Mineralogy

Introduction: 

The MERTIS (MErcury Radiometer and Thermal Infrared Spectrometer) is a mid-infrared imaging instrument onboard the BepiColombo ESA/JAXA mission to Mercury expected to arrive in 2026. Part of the instrument suite is MERTIS, the Thermal Infrared spectrometer (TIS), covering the wavelength range from 7 to 14 µm, which will map the mineralogy of the surface of Mercury [1,2].

MERTIS provided the first thermal infrared data of the hermean surface from a spacecraft after Mariner 10 [3], since it was among the few instruments used during the 5th flyby at Mercury. Owing to the distance of nearly 40000 km during the flyby, the pixel footprint is relatively large (~30 km), but already allows distinguishing surface details.

The main challenge even with the first batch of spectra is to obtain quantitative mineralogical information from the vast amount of data. The standard methods of quantitative mineralogical data analysis are unmixing routines. However, for the accurate modal mineralogy, these routines but are time-intensive and require additional inputs [e.g., 4]. Band ratios are a powerful alternative to obtain a fast first impression of the surface mineralogy and perform mapping.

Techniques: 

The characteristic bands of minerals are difficult to identify in mixtures as expected for surface regolith owing to overlapping features and additional physical effects (e.g., temperature). Our goal is to find easy to identify bands in complex mineral mixtures.

In the first step to identify such band ratios, 28 synthetic mixtures with exactly defined modal mineralogy were used [5,6]. We used only the spectra of the finest size fraction (0-25µm), which is probably the dominant grain size on the hermean surface [2]. We computed emissivity spectra from our reflectance spectra using Kirchhoffs‘ law [7]. While this is a simplified approach, future studies will take the directional hemispherical laboratory setup into account.

In order to avoid absolute spectral intensities, we modelled band ratios between features, where the integrated area of two bands is divided. MERTIS spectra consist of 80 channels, binned by a factor of 2.

Using a Python code, ,we calculated all possible ratio combinations for the spectra of all mixtures – starting with bands consisting of one channel, to bands with a width of 40 channels. The results are 28 arrays with all possible band ratio intensities for each spectrum.

To relate these ratios with mineralogical composition, all the band ratios for a given synthetic mixture were correlated with the known modal mineralogy for 6 phases of this mixture (Glass, Forsterite, Diopside, Plagioclase, Enstatite and Quartz). Thus, we identified the band ratios with the strongest correlations to a given mineral phase (Table 1). 

Data Processing:

We used  Python to develop routines for data processing. Open-source package we used were Pandas, NumPy, SciPy, and Matplotlib [8-11].

Results: For all six phases, remarkably high correlations r = 0.89 – 0.99 were found (r=correlation coefficient). Table 1 presents the wavelength ranges for the two bands of the highest correlations of each mineral phase.

Summary and Outlook:

We have identified characteristic band ratios based on synthetic laboratory spectra for the use on hermean surface spectra. In the following step, we will apply these ratios to create mineral maps of the hermean surface using the processed MERTIS emissivity spectra of the 5th flyby.

Acknowledgments:  MPR, JHP, MPR, IW, AM, KEB, and JHP were funded by DLR grant number 50 QW 2201 A. KW and MT were partially funded by DLR grant number 50 QW 2201 B.

 

References: [1] Benkhoff J. et al. (2010) Planetary and Space Science 58, 2-20 [2] Hiesinger H. et al. (2020) Space Science Reviews, 216, 1-37, 115498 [3] Chase, S. C. (1976) Icarus 28, 565-578 [4] Bauch K.E. et al. (2023) LPSC 54, 2247 [5] Morlok A. et al.  (2024) Icarus 425, 116078 [6] Morlok A. et al. (2023) Icarus 396 [7] King, P. et al. (2004) In: Mineral, Assoc. of can. Short Course Ser. 33. Min. Ass. of Canada, Ottawa, 93-133 [8] McKinney, W. (2010) Proceedings 9th Python Sci. Conf. 56-61 [9] Harris et al.  (2020) Nature 585,357-362 [10] Virtanen, P. (2020) Nature Methods 17, 261-272 [11] Hunter, J.D. (2007) Computing in Sci. & Eng. 9, 90-95

 

Phase	r	BAND 1	BAND 2
ID 158 Glass	0.89	8.49-10.41	8.84-10.59
ID 249 Forsterite	0.97	7.96-8.84	7.53-9.19
ID 22 Diopside	0.96	9.19-12.08	7.18-9.63
ID 28 Plagioclase	0.94	12.16-12.86	12.78-13.83
ID 53 Enstatite	0.98	7.18-12.43	7.18-13.83
ID 13 Quartz	0.99	7.18-8.31	7.35-8.40

Table 1: Band pairs BAND 1 and BAND 2 (range of each band in µm) of the highest correlations. r = corre-lation coefficient. ID = database identification number.

Figure 1: Comparison of laboratory spectra. For presentation purposes we show the data in reflectance. The light gray and pink shaded areas: range for the two bands having the highest correlation with the given mineral phase (Table 1). ID = database identification number