VEM-window data are shown to distinguish among key rock types on Venus, and evaluate redox state and transition metal contents of Venus surface rocks

Boucher, T

D'Amore, M.

Dyar, M. D.

Ferrari, Sabrina

Helbert, Jörn

Marcq, E.

Maturilli, Alessandro

Müller, N.

Smrekar, S.E.

Walter, Ingo

Wendler, Dennis

Widemann, T.

Institute of Transport Research:Publications

PROBING ROCK TYPE, IRON REDOX STATE, AND TRANSITION METAL CONTENTS WITH SIX-WINDOW VNIR SPECTROSCOPY UNDER VENUS CONDITIONS. M. D. Dyar1, J. Helbert2, T. Boucher3, D. Wendler2, I. Walter4, T. Widemann5, E. Marcq6, A. Maturilli2, S. Ferrari7,1, M. D’Amore2, N. Müller8, and S. Smrekar8, 1Planet. Sci. Inst., 1700 East Fort Lowell, Tucson, AZ 85719 USA (mdyar@mtholyoke.edu); 2Inst. Planet. Res., DLR, Rutherfordstrasse 2, 12489 Berlin, Germany; 3Col. of Inform. and Computer Sci., Univ. of Massachu-setts Amherst, Amherst, MA, 01003, USA; 4Inst. Optical Sensorsystems, DLR, Rutherfordstrasse 2, 12489 Berlin, Germany, 5LESIA; 6LATMOS; 7Dept. of Earth and Environmental Sciences, Univ. of Pavia, Via Ferrata 1 - 27100 Pavia, Italy; 8Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena CA, 91109.  Introduction: Although orbital VNIR spectrosco-py of Venus is hampered by the presence of its thick, CO2-rich atmosphere, recent experience shows that observations are possible through transparent windows in the CO2 spectrum near 1 µm (Figure 1). Ground observers have successfully used these windows dur-ing the flyby of the Galileo mission at Jupiter [2]. The VIRTIS instrument on the ESA mission Venus Express (VEX) was the first instrument to routinely map the surface of Venus using the near-infrared windows from orbit [3-6]. The Venus Emissivity Mapper (VEM) was developed for the VERITAS mission to study the sur-face of Venus through six different windows at 0.86, 0.91, 0.99, 1.02, 1.11, and 1.18 µm. This project ex-plores the rich scientific insights that can be gained using those six windows. Two specific issues are ad-dressed here: the ability of VEM-window data to dis-tinguish among key rock types on Venus, and the ca-pability of VEM-window data to evaluate redox state and transition metal contents of Venus surface rocks. Data: This project uses VNIR data collected in the Planetary Spectroscopy Laboratory (PSL) at the Ger-man Aerospace Center DLR in Berlin under the direc-tion of Jörn Helbert [8-13]. Samples studied include a basalt from Lanzarote Island, Spain; a weathered bas-alt/basaltic andesite from Holyoke, Massachusetts; an amphibolite sample from Labrador (Canada); a trachy-basalt from PEL collections (locality unknown); rhyo-lites from Lofoten Islands, Norway, and Seiser Alm, Italy; and rhyolitic glass from Newberry Volcano in Oregon (USA). Compositions were determined by x-ray fluorescence (XRF) as described in [14] or by EMPA using standard methods. Fe3+/Fe2+ ratios were measured using Mössbauer spectroscopy in the Miner-al Spectroscopy Laboratory at Mount Holyoke College [15] using standard methods. Assessing redox state and transition metal con-tents: The region of the visible spectrum where the CO2 windows occur conveniently lies in a critical spectral region between most Fe3+ and Fe2+ features in silicates. Mössbauer data on the samples studied were used to convert the total Fe contents into wt% Fe3+ and wt% Fe2+. These values are shown with the laboratory data in Figure 2 below. Two trends are apparent. First, emissivities measured at the higher bands at 0.99, 1.02, 1.11, and 1.18 µm are indicative of the transition metal contents of the rocks, except for the amphibolite. This stems from the presence of Fe2+ and other transition metal absorptions near this spectral region.  Further analysis of these data is needed to incorporate all the transition metals that may contribute to this spectral region. Second, the wavelength region below 0.91 µm is related to the oxidation state of Fe in the rocks. The Fe oxide, magnetite, which is 67% Fe3+, has the largest negative slope of any sample measured. Oxidized bas-alt/basaltic andesite and all the felsic rocks also have negative slopes, while the fresh basalts can be clearly distinguished on the basis of their positive slopes.  Figure 1. Highlands (red) and lowlands (blue) surface radiance spectra of Venus, with VEM filter locations su-perimposed [1].  Figure 2. Emissivity data from PEL with wt. Fe2+ and wt.% Fe3+ indicated as determined by combining Mössbauer and EMPA data. 3014.pdfLunar and Planetary Science XLVIII (2017)Based on these initial laboratory data, an oxidation state metric can easily be developed to distinguish among various Venus rock types. Discriminating among rock types: Beyond simp-ly determining chemical characteristics as discussed above, a key capability needed for understanding Ve-nus is distinguishing between basalt plains on Venus and other various igneous rock types (specifically ba-saltic andesite, andesite, dacite, and high SiO2 rock types such as rhyolites and granites). To assess this issue, we used averages of our lab data (from Figure 2) and interpolated between them using FeO contents to obtain the model spectra for basaltic andesite, andesite, and dacite shown in Figure 3. We repeated these analyses using two different levels of error: the 4% VERITAS requirement and the currrent best estimates of errors following Phase A analyses. The values used for the latter were 0.7%, 0.7%, 0.4%, 0.3%, 0.7%, and 1.2% for the 0.86, 0.91, 0.99, 1.02, 1.11, and 1.18 µm bands, respectively. In each case, we then created 100 synthetic spectra within a random distribution of those errors. For the 4% error spectra, we also parameterized each of the spectra by calculating the slope between each pair of band (15 combinations) as well as the band ratios between each pair of bands (again, 15 pos-sibilities). This provided a total of 36 associated with each 4% error model (Figure 4). This full parameteri-zation step was not necessary for the CBE spectra be-cause 100% accuracy in classification was achieved using only the six channels of emissivity data (Table 1). Next, we created a binary classifier to assess how accurately each of intermediate rock type could be distinguished from the basaltic plains of Venus. We used a regularized maximum entropy classifier, begin-ning by randomly holding out 20% of the data while the other 80% were used to train the model. The model was then used to predict the classification of the re-maining 20%. We repeated 100 randomized trials for each model, with results shown in Table 1. Table 1. Accuracy of Binary Classifier Trained to  Distinguish between Rock Types  n  Basalt / felsic Basalt /dacite Basalt / andesite Basalt / BA CBE errors 6 ?̅?𝑥 100.0 100.0 100.0 100.0 s2 0.0 0.0 0.0 0.0 4% errors 36 ?̅?𝑥 94.6 88.5 80.4 65.4 s2 0.6 0.9 1 1.2 6 ?̅?𝑥 93.7 86.4 80.2 60.1 s2 0.6 0.9 0.9 1.3 n = number of components in model, ?̅?𝑥 = mean and             s2 = standard deviation of 100 trials. CBE errors as given in text.  This analysis shows that the highest accuracy in discriminating binary rock types is found (unsurprisingly) between mafic and felsic rocks. The most difficult distinction is between baslt and its closest compositional neighbor, basaltic andesite. However, basalt spectra can easily be distinguished from basaltic andesite at CBE error levels. References: [1] Helbert J. et al. (2013) IR Remote Sens. Instrum. XXI, 8867, doi: 10.1117/12.2025582. [2] Carlson R. W. et al. (1991) Science, 253, 1541-1548. [3] Mueller N. et al. (2008) JGR 113, 1–21. [4] Helbert J. et al. (2008) GRL, 35, L11201. [5] Hashimo-to G. L. et al. (2008) JGR, 113, E00B24. [6] Piccioni G. et al. (2007) ESA Special Pub. 1295. [7] Helbert J. et al. (2013) EPSL, 369-370, 233-238. [8] Maturilli A. and Helbert, J. (2006), PSS, 54, 1057-1064. [9] Ma-turilli A. et al. (2008) PSS, 56, 420-425. [10 ] Helbert J. and Maturilli A. (2009) EPSL,  285, 347-354. [11 ] Maturilli A. and Helbert J. (2014) J. Appl. Remote Sens., 8, 084985. [12 ] Maturilli A. et al. (2014) EPSL, 398, 58-65. [13] Maturilli et al. (2016) LPS XLVIII, Abstract #1402. [14] reference to wherever the compo-sitional data are reported. [15] Dyar M. D. et al. (2016) LPS XLVIII, Abstract #2205.  Figure 4. Parameterization of the information in the six emissivity bands collected by VEM into 36 values that con-tain predictive information, of which 21 are independent. The 36 values are then used in models to discriminate among rock types. Figure 3. Spectra of synthetic rock types given as dashed lines between real average lab data, representing intermedi-ate igneous rock types including basaltic andesite (6.71 wt.% FeO), andesite (4.47 wt.% FeO), and dacite (2.24 wt.% FeO). These FeO values were interpolated between the real average rock FeO compositions of samples run in the laboratory at DLR.  3014.pdfLunar and Planetary Science XLVIII (2017)

Probing Rock Type, Fe Redox State, and Transition Metal Contents with Six-Window VNIR Spectroscopy Under Venus Conditions

Probing Rock Type, Fe Redox State, and Transition Metal Contents with Six-Window VNIR Spectroscopy Under Venus Conditions

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