# Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends

Roger C. Wiens, Alyre J. Blazon-Brown, Noureddine Melikechi, Jens

Frydenvang, Erwin Dehouck, Samuel M. Clegg, Dot Delapp, Ryan B.

Anderson, Agnes Cousin, Sylvestre Maurice

► To cite this version:

Roger C. Wiens, Alyre J. Blazon-Brown, Noureddine Melikechi, Jens Frydenvang, Erwin Dehouck, et al. Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends. Spectrochimica Acta Part B: Atomic Spectroscopy, 2021, 182, 10.1016/j.sab.2021.106247. insu-03672412

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Distributed under a Creative Commons Attribution 4.0 International License
LA-UR-21-21097 (Accepted Manuscript)

Improving ChemCam LIBS long-distance elemental compositions using
empirical abundance trends

Wiens, Roger Craig; Blazon-Brown, Alyre Jean; Melikechi, N.;
Frydenvang, Jens; Dehouck, Erwin; Clegg, Samuel M.; Delapp, Dorothea
Marcia; Anderson, R. B.; Forni, Olivier; Cousin, Agnes; Gasnault,
Olivier; Maurice, Sylvestre

Provided by the author(s) and the Los Alamos National Laboratory (2021-07-12).

**To be published in:** Spectrochimica Acta Part B: Atomic Spectroscopy

**DOI to publisher's version:** 10.1016/j.sab.2021.106247

**Permalink to record:** http://permalink.lanl.gov/object/view?what=info:lanl-repo/lareport/LA-UR-21-21097

**Disclaimer:**

Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by Triad National Security, LLC for the National Nuclear Security Administration of U.S. Department of Energy under contract 89233218CNA000001. By approving this article, the publisher recognizes that the U.S. Government retains nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or to allow others to do so, for U.S. Government purposes. Los Alamos National Laboratory requests that the publisher identify this article as work performed under the auspices of the U.S. Department of Energy. Los Alamos National Laboratory strongly supports academic freedom and a researcher's right to publish; as an institution, however, the Laboratory does not endorse the viewpoint of a publication or guarantee its technical correctness.
Improving ChemCam LIBS Long-Distance Elemental Compositions Using
Empirical Abundance Trends

Roger C. Wiens*<sup>a</sup>, Alyre J. Blazon-Brown<sup>a,b</sup>, Noureddine Melikechi<sup>b</sup>, Jens Frydenvang<sup>c</sup>, Erwin Dehouck<sup>d</sup>, Samuel M. Clegg<sup>a</sup>, Dot Delapp<sup>a</sup>, Ryan B. Anderson<sup>c</sup>, Agnes Cousin<sup>f</sup>, and Sylvestre Maurice<sup>f</sup>

<sup>a</sup>Los Alamos National Laboratory, Los Alamos, New Mexico, USA

<sup>b</sup>Kennedy College of Sciences, University of Massachusetts, Lowell, Massachusetts, USA

<sup>c</sup>University of Copenhagen, Copenhagen, Denmark

<sup>d</sup>Université de Lyon, Univ. Lyon 1, ENSL, CNRS, LGL-TPE, F-69622, Villeurbanne, France

<sup>e</sup>U.S. Geological Survey, Flagstaff, Arizona, USA

<sup>f</sup>Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse,
CNRS, CNES, Toulouse, France

*Corresponding author: rwiens@lanl.gov

Highlights

* ChemCam provides elemental compositions to ~7 m distance on Mars using LIBS

* Elemental abundances of Si, Al, Na, K show systematic distance trends

* Trends reflect changes in laser beam properties affecting plasma temperature

* A simple empirical method to correct for distance trends is proposed

* Any remote LIBS systems can use this technique, adding validity to distant targets

Submitted to Spectrochimica Acta B
February, 2021

Submitted in revised form May, 2021
LA-UR-21-21097
## Abstract

The ChemCam instrument on the *Curiosity* rover provides chemical compositions of Martian rocks and soils using remote laser-induced breakdown spectroscopy (LIBS). The elemental calibration is stable as a function of distance for Ti, Fe, Mg, and Ca. The calibration shows small, systematically increasing abundance trends as a function of distance for Al, Na, K, and to some extent, Si. The distance effect is known to be due to a dependence with distance on the relative strengths of atomic transition lines. Emission lines representing transitions from relatively low energy levels remain intense at longer distances while emission lines representing transitions from higher energy levels decrease in intensity more rapidly as a function of distance. The multivariate algorithms used to determine elemental compositions rely on a large number of emission lines in many cases, so rather than trying to correct all emission lines, a study was made of the predicted compositions as a function of distance, in order to determine an empirical correction. Abundance trends can be well approximated by a linear trend with distance within the ranges of abundances and distances observed up to ~6 m. Data from 11 distinct geological members and data groups of the Murray formation in Gale crater, Mars, were used to form the model, selecting the members and data groups yielding the best statistics. The model was tested using data from several targets observed from two different distances, and using data from the Kimberley formation, the composition of which is significantly different from the Murray formation, showing that the model works on other compositions beyond those used to build the model. For long-distance observations up to ~6 m, corrections can be made back to an equivalent composition at the median distance of ChemCam observations (2.6 m). The model has been validated up to 6.2 m, although ChemCam is able to observe bedrock targets to > 7 m, and iron meteorites to distances of > 9 m.

## Keywords

* ChemCam
* LIBS
* Mars
* *Curiosity* rover
* Stand-off elemental analyses
# 1. Introduction

Laser-induced breakdown spectroscopy (LIBS) is a type of atomic emission spectroscopy in which a focused laser pulse is used to ablate materials, forming a plasma containing atoms in electronically excited states. These atoms emit photons at wavelengths that are characteristic of their atomic transitions (e.g., [1]). These transitions result in patterns of emission peaks representing the major, minor, and trace elements present in the targets. The ChemCam instrument [2, 3] on the *Curiosity* Mars rover uses LIBS to determine the elemental compositions of rocks and soils within ~7 m of the instrument.

The database from ChemCam is large, at > 25,000 observation points on Mars over the course of nearly nine years to date (2021). LIBS-derived elemental abundances are provided from these observations. This large database has facilitated analyses of the overall quality of the data in addition to providing unprecedented coverage of compositions on Mars' surface. One check of the quality of the data is the consistency of its elemental compositions as a function of the distance from the instrument to the sample. The *Curiosity* rover has traversed over homogeneous, fine-grained bedrock at various times during the mission. Early in its use, the ChemCam team observed that elemental abundances obtained for targets at longer distances are not exactly the same as abundances obtained for short-distance targets on the same homogeneous bedrock. A distance effect is perhaps not surprising, as diffraction and other slight optical aberrations affect the laser beam with increasing distance [2], resulting in larger ablation pit diameters and lower power densities at longer distances. The lower power densities are believed to result in lower plasma temperatures, changing the nature of the spectra slightly.

The fundamentals of LIBS distance effects on ChemCam were studied in a pair of papers [4, 5] in which the relative strengths of individual emission lines were assessed as a function of distance. These studies utilized the Mars air-fall dust that coats all surfaces as a material of uniform composition on which to investigate the effects of distance. They used the spectra from only the first shot of each laser burst (typically consisting of 30 shots) performed at each observation point, adopting a screening technique to cull samples with excessive influence from the underlying materials, e.g., where the dust was too thin. Emission lines originating from lower energy levels were observed to be less affected by distance than those emission lines originating from higher energy levels (the higher levels are less excited during laser-induced breakdown at longer distances). As a general rule, ionic lines were thus more strongly affected than neutral emission lines. More recently, the impact of altering the focal spot size on LIBS spectra was studied under ambient Earth laboratory and simulated Martian atmospheres for targets of aluminum, copper, and steel [6]. These authors reported that atomic and ionic emissions show different intensity peak distributions with varying lens-to-sample distances. Using empirical methods, the earlier ChemCam studies [4, 5] were able to correct for distance effects relatively accurately for individual emission lines. The method of distance correction was not adopted for the overall ChemCam major-element calibrations because they employ multivariate analyses (MVA) of the entire spectrum, and thus would have required empirical data on distance trends for each of a large number of emission lines. In the current work, we investigate a simpler empirical correction applied directly to the predicted elemental abundances.
The accumulation of large data sets in homogeneous, fine-grained bedrock has provided an alternative to using first shots on dust. An advantage of using homogeneous, fine-grained bedrock is that, over the course of time, different compositions can be studied to see if their trends with distance behave the same. Here we investigate observations from 11 different geological classes of the Murray formation on the lower slopes of Mt. Sharp in Gale crater, Mars [7, 8]. After selecting the best data and developing a tentative empirical method for correction, we check it by looking at data trends from the Kimberley formation, which was investigated shortly before arriving at the base of Mt. Sharp [9]. We also test the model on several individual targets that were observed from two different distances. Data from targets observed at the longest distances (to > 9 m) are then investigated to study the limits of this model and of the observational capabilities of ChemCam.

This study utilizes data collected on Mars rather than laboratory data for two primary reasons: (1) it is the Mars data that we want to correct. Deriving the correction using data collected by the instrument on Mars eliminates any unexpected effects due to different instrumental or experimental conditions. (2) Far more Mars data are available. The extensive Mars data set is more than sufficient to derive an empirical correction.

## 2. Data Set and Analysis Methods

ChemCam is a remote LIBS and imaging instrument that uses a 110 mm aperture Schmidt-Cassegrain telescope to project and focus the laser beam onto targets on the surface of Mars ~2 m (directly down to the ground) to ~7 m away [2]. The ChemCam telescope collects some of the plasma light and directs it to three optical spectrometers. These are ultraviolet (UV; 240-340 nm), violet (VIO; 385-465 nm), and visible and near infrared (VNIR; 470-850 nm) spectrometers read by charge-coupled devices (CCDs) without shutters or intensifiers [3]. The laser energy (~12 mJ) used to create the LIBS spectra has stayed the same for all surface targets since Mars solar day (Sol) 401 in late 2013. Prior to that, targets at distances < 3 m used a lower laser energy. This study ignores the earlier data from lower-energy pulses and uses a single laser energy. Figure 1 provides a comparison of pre-processed spectra in the UV range for two Murray formation targets located in the same general area, but at significantly different observation distances (6.6 and 2.3 m). Fig. 1B shows that the spectrum of the more distant target generally follows that of the closer target, but with an intensity that is reduced by a factor of ~30. Examples of various specific emission peaks plotted for targets at different distances are presented in the aforementioned papers on this subject [4, 5].

The ChemCam team routinely reports compositions for eight major elements (Si, Ti, Al, Fe, Mg, Ca, Na, K) and four trace elements (Li, Rb, Sr, Ba). To obtain these compositions, the spectra are pre-processed to subtract background light, remove the continuum, and correct for instrument optical response and flux as the inverse square of the distance. The normalized spectra are used in a MVA comparison against a library of spectra obtained in the laboratory under Mars-like conditions to yield major-element abundances [10-12]. Trace elements use the same pre-processing, but then use univariate calibrations against the spectral library (e.g., [13]). Resulting elemental compositions are reported and archived in NASA's Planetary Data System (PDS) for targets at all distances as long as the total emission is above a threshold and the compositions fall within the calibrated range (e.g., compositions of iron meteorites are not reported in the PDS).
Due to the known distance effect, most publications have used only data for targets at distances < 3.5 m.

The distance correction model presented in this work uses, for development and validation, materials that are broadly homogeneous at all distances. In other words, the targets used for this study are generally homogeneous at the scale sampled by the laser at its surface operating distances of 2.22 to ~7 m from the instrument. Martian sediments generally consist of a significant fraction of unaltered igneous grains along with variable amounts of clay minerals and x-ray amorphous material (e.g., [14, 15]). The primary igneous minerals differ significantly in their compositions between calcium and alkali alumino-silicates (feldspars), and iron and magnesium silicates (olivine and pyroxene). In order for bedrock consisting of these grains of diverse compositions to yield a homogeneous composition on the scale of ChemCam's observations, grain sizes need to be significantly smaller than the beam size (350-550 µm [2]), so that a representative ensemble of grains is sampled with each observation [16, 17]. Observations made early in the mission indicated that grain sizes represented by mudstone to very fine sandstone (e.g., < 125 µm [18]) provide results that are generally comparable to the precision of the instrument. A measure of ChemCam's precision comes from multiple observations of the calibration targets on board the rover [19]. Standard deviations of 480 targets in the Sheepbed mudstone in Yellowknife Bay (YKB) [20] also provide a measure of precision that is in overall agreement with calibration target standard deviations [19]. As will be shown later, the composition trends with distance are challenging to disentangle from the scatter that is inherent to the instrument (its precision) and, more significantly, variations due to sediment grain heterogeneity at the scale of the laser beam, so careful selection of the data set was needed.

The *Curiosity* rover encountered two major regions dominated by mudstones to fine sandstones. The first was YKB from Sol 125 to 326 in 2013 [21]. As two different laser energies were used this early in the mission (see above), YKB will not be used as a bulk data set in this study. The second was the Murray formation (e.g., [7, 8]), starting around Sol 755, in 2014. *Curiosity* is still in the Murray formation at this writing (spring 2021). A third location where a significant amount of data were collected on fine-grained sediments was the Windjana drill site in the Kimberley formation (sols 608-628). This was the rover's second drill site, and ChemCam had time during this drill campaign to observe many bedrock targets [9]. From the rover's location, ChemCam observed two different geological members, differentiated by stratigraphy. The rover was positioned on one member, while the other member was several meters away. At the time, compositional differences were reported for these two members [9]; we will present the data here in the context of distance trends and will discuss the interpretation in the process.

Figure 2 shows the traverse of the rover, highlighting the different locations along the traverse. YKB is at the far northeastern extreme of the rover traverse. The Kimberley location is highlighted midway to the base of Mt. Sharp. Farther south, the Murray formation comprises the entire second half of the rover's traverse. Descriptions of the compositions of its members have been published [8, 23]. To investigate changes in predicted composition with distance, we treated observation points from each member of the Murray formation separately to only compare observation points from a similar lithology. Within some members, we observed some large-scale variations in geochemistry that would confound our investigation of any distance trends. Specifically, we discarded observation points from the contact between the Sutton Island and
Blunts Point members (Fig. 2B) where a substantial enrichment in MgO and MnO was observed [24, 8], and observation points from the upper part of the Pahrump member observed to be strongly enriched in silica (e.g., [25]). Furthermore, while they are technically a part of the Jura member, observation points from the Glen Torridon area south of Vera Rubin Ridge (VRR) are referenced here as either Glen Torridon rubbly, Glen Torridon pebbles, or Glen Torridon coherent to reflect three geochemical and textural groups observed in this area [26]. While the subdivisions broadly refer to separate Murray formation members, because there is some difference with the latter three data groups, we will refer to these as *classes* of observation points. All data were culled to remove any additional observation points that do not represent bedrock, such as those that hit Ca-sulfate veins, which occur throughout the Murray formation. The resulting data are given in the Supplementary Material. Frydenvang et al. [8] published a similar table of Murray-formation data but restricted their table to observations at distances of < 3.5 m. Data are also given in the Supplementary Material for the Kimberley formation; the latter corresponds to the 20 targets from the Dillinger and Mt. Remarkable members in Table 1 of Le Deit et al. [9]. Despite the data-selection efforts, the remaining data are definitely not fully homogeneous by ChemCam's standards since the abundances of a given element in a given class at a given distance vary more than the instrument precision.

Simple linear fits were performed for trends of abundances vs. distance using the MATLAB *fit* function, poly type 1, which yielded the slope, intercept, 95% confidence levels of both, and coefficients of variation (R²). The fits were also checked using a bi-square regression function in MATLAB, which reduces the influence of outliers, and the results were essentially identical to the simpler linear fit. A comparison is shown in the Supplemental Material.

In the following sections we present the trends using the Murray formation classes first, and then use the Kimberley data as a confirmation of the hypothesis, later extending it to several targets observed from more than one distance.

# 3. Results

## 3.1. Murray Formation

Figure 3 shows the reported chemical abundances for two major elements, Al and Ca, as oxides (Al₂O₃ and CaO), as functions of distance. The remaining major elements (Si, Ti, Fe, Mg, Na, and K) and the sum of the major elements are all plotted as functions of distance in the Supplemental Material. The two elements presented in Fig. 3 are representative of two groups: one showing increasing reported abundances with distance (Al₂O₃) and one showing effectively no trends with distance (CaO). The data are shown separately for each of the identified classes (Fig. 2B). Individual observation points are plotted; a raster of between 5 and 20 points is performed for each target, so at least several points generally appear at each distance in Fig. 3. In all cases, the minimum distance is ~2.22 m, a little greater than the minimum distance to the ground, as the optical configuration of ChemCam has a keep-out zone between 1.9 and 2.22 m [2]. Different maximum distances are observed in the various Murray formation classes plotted in Fig. 3.

The slopes plotted for the different Murray classes in Fig. 3 and the Supplemental Material are generally similar within a given element. Positive slopes are generally observed for SiO₂, Al₂O₃,
216 Na₂O, K₂O, and the sum of the major elements. Conversely, for TiO₂, FeOₜ, and CaO, some
classes have slight positive slopes and some have slight negative slopes (on average these are all
217 statistically insignificant), while MgO shows either no slope or slightly negative slope
218 (Supplemental Material). (For iron, the T subscript indicates that it represents the abundance of
219 total iron in the target, of any oxidation state, converted to the wt. % that it would have if it was
220 all in the form of FeO.)
221

222
223 To aid in determining which of these trends are significant, Table 1 provides the statistics of the
224 linear trends. Element oxides Al₂O₃, Na₂O, K₂O, and SiO₂ show R² values and upper and lower
225 95th percentile bands that indicate some potentially reliable positive slopes. All of the other
226 elements, including MgO, have maximum R² values of ≤ 0.10 (Table 1). Based on these
227 statistics, distance trends will be considered for the four elements mentioned above (Figure 3,
228 Supplemental Material, and Table 1), but not hereafter for the other elements. Distance trends for
229 the sum of the oxides (Table 1 and Supplemental Material) reflect the trends of the entire suite of
230 elements. Given that four elements show positive trends and the rest do not show significant
231 trends, it is not surprising that the sum of the oxides shows a positive trend. As this is a sum of
232 other quantities, the focus below will be on the individual elements.
233

234 The statistics in Table 1 and visual observation of Figure 3 show why separation into distinct
235 classes is necessary, as there is much more scatter of the data in some classes than in others. A
236 linear fit of the entire data set together gives an R² for Al₂O₃ of 0.34, while the classes with the
237 least scatter give R² values of nearly double that value (Table 1).
238

239 One question is whether there might be a temporal trend in the data, for example, if some part of
240 the instrument was changing over time. After all, the data presented here from the Murray
241 formation were taken over five Earth years, from 2014 to 2019. Figure 4 shows the slopes for
242 each class in the order in which the data were taken, for the four elements that tend to have
243 positive slopes. There does not appear to be any trend with time.
244

245 Table 1 gives 95% upper and lower bounds, as well as R² values. Classes with the highest R²
246 values yield similar slopes for each element (Figure 5). One other quantity was investigated,
247 which was the centroid, the difference between the minimum and median distance of each class.
248 It was found that the slopes did not converge for the classes with the longest centroids
249 (Supplemental Material), and the centroids did not correlate with R². This suggests that some
250 other quantity than the distribution of points with distance had the strongest effect on the quality
251 of the results. At least part of this effect could result from the grain-size variations among the
252 different classes, in which larger grains result in greater reported heterogeneity. Regardless of the
253 cause, the scatter is significantly larger than the precision of the instrument (Fig. 3). A study was
254 devoted specifically to heterogeneity within rasters as a proxy for grain size in the Murray
255 formation [27]. In that study, some of the greatest heterogeneity variations occurred near the
256 upper end of Sutton Island. Pahrump Hills, Hartmann's Valley, and Karasburg all show a range
257 of grain sizes [27], while Blunts Point consists entirely of the smallest-scale metric in the study.
258 That study did not extend to the later-sampled Pettegrove Point, Jura, Glen Torridon pebbles,
259 Glen Torridon rubbly, and Glen Torridon coherent classes, although the team observed that these
260 were generally fine grained, particularly the Glen Torridon rubbly class (e.g., [26]). For all four
261 elements of interest, Blunts Point, Glen Torridon rubbly, and Glen Torridon pebbles have the
highest R² values (Table 1), and we select them as representing a uniform composition with small grain sizes. The general agreement of these three classes is confirmed visually in Fig. 5, and their characteristics are summarized in Table 2. These results will be used in the model described in Section 4.2.

## 3.2. Kimberley Formation
The Kimberley formation results are shown in Fig. 6, along with linear fits of the data as in Fig. 3. The bedrock in this location has a very different composition than the Murray formation, particularly for several of the elements that show the strongest distance effects. Specifically, K₂O is significantly higher, while SiO₂, Al₂O₃, and Na₂O are all lower. These observations at the Windjana drill site cross from the Dillinger member at close distances to the Mt. Remarkable member starting 5 m away [9]. The data from the latter member consist of two targets (one observed twice) comprising 18 observation points. Interpretation of the ChemCam results initially indicated that the remotely observed Mt. Remarkable member differs in composition from the Dillinger member, namely, having higher SiO₂, Al₂O₃, K₂O, and Na₂O [9]. Here we see that the Mt. Remarkable compositions (observations at > 5 m in Fig. 6) appear to be a continuation of the distance trend extending from targets observed at shorter distances in the Dillinger formation.

## 3.3. Targets Observed from Two Different Distances, and Targets Observed at > 6 m
Two more sets of data are used to understand the effect of distance on the major-element compositions. One consists of several targets that were observed from two different distances, the rover having moved 2 to 4 meters between observations in all three cases. The means and standard deviations of the 5-10 observation points of each target at each distance are given in Tables 3 and 4. The three bedrock targets are from different locations along the traverse. Bonnet Plume is at YKB (Fig. 2), with both observations performed before Sol 200. Even though it was early in the mission, the nearer observation was at a long enough distance that normal laser energy was used. Hallaig was observed in the Glen Torridon coherent class on sols 2413 and 2416. Glen Lyon was targeted three times near the Glen Etive drill hole in Glen Torridon. The rover was re-positioned between observations on sols 2481 and 2533; it was not moved for the third analysis, on Sol 2540. The focus was not good for the Sol 2533 data (autofocus diagnostic curve did not show a peak, and images were slightly blurry), so the spectral data from that observation were omitted.

Two soil targets were included in this data set. Soil has been encountered at various points along the rover traverse. In general, fine-grained soil tends to be uniform in composition across all of Mars, but studies have also shown that weathering of rocks and pebbles contributes to soils locally, tending to dominate the coarser grains (e.g., [28, 29]). In a number of locations, soil drifts were found to be armored by coarse grains to fine pebbles across the entire drift (e.g., [28] or to show mechanical sorting, with olivine grains concentrated at the crests of the ripples [30, 31]. It was important to avoid these situations to obtain a widely representative sample. Additionally, there was generally no need to target soils at long distances, since the same material is usually also found closer to the instrument. On Sol 2242, the ChemCam team attempted to observe an iron meteorite at 4.2 m. ChemCam missed the target and shot the surrounding soil, making Noss the longest-distance homogeneous soil observation, along with one other target, Mount Eduni, near the beginning of the mission (Sol 312). It is necessary to pair
308 the long-distance observation with a soil target observed at close distance. Since at the beginning
309 of the mission, nearby targets used a lower laser energy, it is not possible to pair Mount Eduni
310 with a nearby close-distance target, so we go with Noss, pairing it with soil target Loch Monar,
311 observed at 2.3 m on Sol 2291 (locations of both are indicated in Fig. 2). Data from these soil
targets are also presented in Tables 3 and 4.
312
313

314 Data from the longest-distance targets of the mission, between 6 and 9.3 m, are given in Table 5.
315 The locations of these targets are indicated in Fig. 2. LIBS was not attempted for any bedrock
targets beyond 7.5 m, as the signal would likely be too weak based on the other bedrock
316 observations made at long distances. A total atomic emission of 5x10<sup>13</sup> photons emitted at the
317 target, summed from all channels, is the cut-off for acceptable data for validation, and some of
318 the data from bedrock in Table 5 are close to or below that limit. The longest-distance targets
319 consist of iron meteorites, which were found to provide useful results in terms of the Fe-Ni ratios
320 (e.g., [32]), which are important for classifying the types of iron meteorites. Major-element
321 abundances are not determined for iron meteorites because the compositions and target
322 properties are well outside the range of the data used to train the ChemCam calibration models.
323
324

325 4. Discussion

326 4.1. Characteristics of Distance Trends

327 Distance trends are observed to arise in the elemental compositions provided by the MVA
328 algorithms used by ChemCam. These trends are due to physical changes in the laser-induced
329 plasma that are reflected in changes in the spectra. As the laser beam is focused at longer
330 distances, the Airy disk representing the tightest beam at best focus is larger [2]. As a result,
331 photon (and energy) density is lower. Earlier studies of ChemCam spectra as a function of
332 distance [4, 5] have shown that ionized emission lines tend to decrease in intensity faster with
333 distance than neutral emission lines. In general, atomic emission lines having higher upper
334 transition energy levels require higher photon densities to excite these energy levels. The
335 increased sensitivity of higher-energy transitions manifests itself in the Boltzmann plot (e.g.,
336 [33]), in which the plasma temperature T (actually 1/kT, where k is the Boltzmann constant) is
337 determined from the slope of the plot of energy level vs. the log of the intensity. As the plasma
338 temperature decreases with lower photon density at longer distances, the intensities of peaks
339 representing higher energy transitions decrease more rapidly than those of low-energy peaks.
340 Measurement of plasma temperatures is not technically feasible with ChemCam due to its lack of
341 a shutter (exposure times several ms), with the result that the requirement of local
342 thermodynamic equilibrium is not strictly met. Despite this computational disadvantage, it seems
343 highly likely that plasma temperatures are lower for longer-distance observations (cf. [10]).
344

345 ChemCam uses weighted averages of the results of two MVA algorithms to determine the
346 elemental compositions: partial least squares (PLS), and independent component analysis (ICA)
347 regression [11]. For the four elements that show significant distance trends, the ICA and PLS
348 results are either weighted equally (SiO₂, Al₂O₃) or weighted toward ICA (Na₂O by 60% and
349 K₂O by 75%; [11]). All four of these elements tend to have relatively simple ICA loadings and
350 PLS regression coefficients focused on the strongest emission peaks of the elements of interest.
351 Loadings are vectors which, when multiplied by the respective channels of the spectrum, give the
352 ICA score. Composition is determined using a calibration curve that relates ICA score to wt. %
of the element. For PLS, elemental abundance is obtained by multiplying the intensities of each spectral channel by the regression coefficient for that channel and summing the products. If both the regression coefficient and the intensity of the spectrum corresponding to an emission line are strongly positive, the result is a high abundance for that element.

The link between loadings and emission lines is most straightforward in the case of K, which has only two strong emission lines, given in Table 6. These two emission lines have the lowest upper energy levels of the major emission lines of the major elements, and for K, both MVA algorithms target these two emission lines almost exclusively.

Table 6 shows upper energy levels in the range of 1.6 to 11.8 eV for the four elements showing positive distance trends. In a terrestrial environment, a number of the emission lines with low upper energy levels suffer from self-adsorption [1]. However, in Mars' thin atmosphere, the plasma expands rapidly, avoiding this effect. Plots of a number of emission lines in Table 6 are shown in [4, 5] for targets at different distances, with no changes in full-width at half maximum (FWHM), indicating that self-adsorption is not a problem in this environment.

For geological materials observed on Mars (e.g., [8]) and for the spectral range of ChemCam [3], the spectra are dominated by iron emission lines in the UV spectrometer and iron and calcium in the VIO spectrometer. Iron plays less of a role in the VNIR spectral range, where peaks tend to be dominated more by Ca and a trio of strong, closely spaced oxygen emission lines at 777 nm. As part of the data processing, the spectra are normalized to the total emission, either within each given spectrometer (for Al, Na, and K) or to the entire spectrum (Si) [11]. It is this normalization, combined with the physics described above, that causes the distance trends. Relative to the other emission lines, the upper energy levels of Si, Al, Na, and K are low. For example, for the VNIR spectral range, relevant especially for K and Na, the strong oxygen triplet at 777 nm has an upper energy level of 10.74 eV. Besides the O triplet and the alkali elements, the VNIR range is often dominated by Ca emission lines, which range from 3.9 to 10.0 eV with the exception of one lower-energy Ca emission line at 657.5 nm, which is weak in the LIBS spectrum. Other peaks in this range also tend to have high upper transition energies, such as the H Balmer emission line at 656.46 nm, which is 12.1 eV. These energy levels are all higher than the K and Na energy levels in Table 6. The result is that for this spectral range, as laser photon density decreases at increasing target distances, the alkali emission lines persist while the other emission lines decrease faster [4, 5], resulting in increasing predicted Na and K abundances with distance.

The UV and VIO spectral ranges, which tend to be more important for Al and Si, are dominated by Fe emission lines, especially on Mars, which typically has much higher crustal Fe abundances than the Earth (e.g., [34]). In the UV range, the NIST database [35] lists nearly 5500 Fe I and II emission lines. Not all of these are observed in a ChemCam LIBS spectrum. The strongest emission lines tend to have upper transition energy levels between 7 and 14 eV. In the VIO range, the strongest Fe emission lines tend to have upper transition energy levels between 4.5 and 7 eV. Overall, the situation for Si and Al is essentially the same as for the alkalis, such that the emission lines of these elements (Table 6) also persist with distance while the other emission lines grow weaker as the laser photon density decreases [4, 5].
The linear trends shown in Fig. 3 are accompanied by significant scatter. As mentioned earlier, this scatter represents both inhomogeneities in the bedrock and the natural scatter of the stand-off LIBS technique, represented by the precision error bars. Given the scatter, and a limited distance range over which this phenomenon is observed, we find that a simple linear trend is the most appropriate fit to the data. Only qualitative arguments will be used in this discussion.

The factors in question are (a) the change in laser beam spot size with distance which induces a change in laser photon density as a function of distance, (b) the rate of response of the emission peaks to changes in photon density, and (c) the treatment of the data. Relative increases in the normalized intensity of an emission line with a low upper transition energy level, such as the K emission lines, could be in a linear regime as long as the area under the K emission peaks is low relative to the total emission to which it is normalized. This is likely to be true if the abundance of the element in question does not dominate the overall composition, since there are generally hundreds of emission lines in the spectral range being normalized, with only two of them being K emission lines. If the abundance of a given element were to approach 100% in some target, given the normalization, the curve as a function of distance might approach an asymptotic (constant) value instead of increasing linearly with distance. This does not happen for any of the four elements considered here. Even for Si, with the abundance of SiO₂ typically in the range of 50 wt. % in silicate-dominated rocks, its emission lines are relatively few compared to transition-metal elements such as Fe, which have a much larger number of emission lines, so the distance trends for SiO₂ likely stay linear until the oxide starts to approach close to 100 wt. %. In summary, the trends with distance seem to be reasonably modeled by linear equations in these abundance ranges, at least given the uncertainties in the data.

## 4.2. A Linear Distance-Correction Model

Assuming that the data can be approximated by linear trends, a single slope is not expected for all situations for a given element except in the case where there is no trend with distance (Fig. 7a). Instead, given the normalization, the slope depends on the abundance. The predicted abundance increases with distance as a function of the difference in energy levels and also as a function of its abundance at a given (e.g., nominal) distance, as illustrated in Fig. 7b and 7c. If the abundance of a given element is near zero, the predicted abundance increases slowly with distance in an absolute sense. On the other hand, in the case of a relatively high abundance at a normal distance, a much larger abundance will be predicted at longer distances. Over the range of distances explored here, the effect approximates the linear trend rotating about a point on the x-axis (Fig. 7c). The location of the axis of rotation (e.g., the position given by the x-intercept) does not have a physical meaning in terms of distance, but it is related to the behavior of that element as a function of distance. This behavior is determined by the ratio of transition energy levels of the element of interest relative to the energy levels of the peaks that dominate across the spectral region used for normalization. An elemental trend with an x-intercept that is farther from ChemCam's analytical region (e.g., Fig. 7b) increases more slowly with distance than a trend having an x-intercept closer to this region (Fig. 7c). With this concept, we propose that any composition obtained from the MVA algorithms for observation points at long distances (the applicable range of which is discussed below) can be corrected back along a line toward the respective x-intercept of that element.
The x-intercepts for each of the four elements (Al, Na, K, and Si) are defined in this study by the mean and standard deviations of the abundances from the Murray formation members. Because of the large spread in quality of the fits, we use the data corresponding to only the three highest R² values, given in Table 2. The Murray formation classes with the three highest R² values have similar slopes and x-intercepts. The slope for each element varies around its mean values by ±4% for Al₂O₃ and K₂O, by ±10% for Na₂O, and by ±13% for SiO₂. The x-intercepts have relatively tight ranges, with standard deviations in the range of 0.32 to 0.56 for Al₂O₃, Na₂O, and K₂O. The case of SiO₂ is different from the other three oxides due to the fact that the typical abundance of SiO₂ is much higher and the slope is very low as a fraction of that abundance. The result is that the x-intercept of SiO₂ is a much larger negative number than the others, similar to Fig. 7b. It also has a much larger standard deviation, at 5.69.

To correct the abundance Aᵢ of a given element observed at distance dᵢ, the mean x-intercept, dₓ is taken from Table 2 and applied to a desired final distance, dᵣ. In general, dᵣ will be the median distance for ChemCam observations, but other distances can be tested, as will be done in Section 4.3.2. The corrected final abundance, Aᵣ, is found from the equation

$$A_f = A_i (d_f - d_x) / (d_i - d_x) \quad [1]$$

The uncertainty can be determined from the standard deviation of the x-intercepts given in Table 2, combining it with the uncertainty of the long-distance observation.

## 4.3. Tests of the Distance Model

Having proposed an empirical model based on the best data from the Murray formation, several tests of the model are presented here.

### 4.3.1. Kimberley Formation as a Test of the Distance Model Built on Murray Data

The elemental compositions of the Kimberley formation are quite different from those of the Murray formation, so the former makes a good test of the broader applicability of the model. In this case, the x-intercepts of data provided by trends in the Kimberley formation, given in Table 2, are checked against the x-intercepts determined from the Murray formation (Table 2). Although the compositions are different, and hence different trends are produced as a function of distance, the x-intercepts should be in agreement.

Figure 8 shows the linear trends of the Murray formation members having the three highest R² values, and over-plots the linear trends of the Kimberley formation, which has x-intercepts of -25.56, -2.75, -3.09, and -0.49, respectively, for SiO₂, Al₂O₃, Na₂O, and K₂O (compare with Murray values and standard deviations in Table 2). Although we can only be confident of the model within the distance range over which data exist (Fig. 8, solid lines), extrapolation is needed to determine the x-intercept, which is key to the model. For Al₂O₃, Na₂O, and K₂O, the Kimberley x-intercepts (Table 7) fall in the middle of the x-intercepts defined by the three Murray classes (Table 2). Silicon is different in that the x-intercept of the Kimberley is not within the range of Murray intercepts given in Table 2. Although the Kimberley trend gives a lower absolute-value x-intercept, it is still within two standard deviations of the Murray observations. As mentioned earlier, SiO₂ has the lowest R² values of the four elements (Tables 1 and 2), so perhaps this scatter is not surprising. Overall, given the consistency of three of the four
elements between the Murray formation and the disparate Kimberley compositions, we take
these results (Table 7 and Fig. 8) as at least partial validation of the model.

### 4.3.2. Targets Observed at Two Different Distances

Another test of the distance-trend model can be made using individual targets that were each
observed at two different distances. In each case, the composition obtained at the longer distance
is corrected back using Eq. 1 to give a predicted composition at the nearer distance, for direct
comparison to the measurement made at that distance. The results, along with both sets of
observations, are given in Table 8. The error for the corrections is the error of the prediction
(derived from the standard deviation of the x-intercept in Table 2) root sum squared with the
standard deviation of the measurement at the longer distance. For Hallaig and Glen Lyon, the
predicted results at the near distance match the measurement at that distance almost exactly. In
the case of Bonnet Plume, the nominal differences are slightly greater but are still within the
error. Potassium, in particular, looks suspicious, as the observations at long and shorter distances
give nearly the same nominal value, appearing not to show a distance trend. However, the
nominal value of K₂O at the longer distance, at 0.59 wt. %, is quite low, so the correction is
expected to be small, only -0.24 wt. %. Due to the scatter of the data, which gives an uncertainty
of ±0.17 wt. % for the long-distance observation and ±0.14 wt. % at the shorter distance, the
correction (also with an uncertainty of ±0.17 wt. %) is within error of the nearer-distance
observation. The situation with Bonnet Plume's Na₂O and SiO₂ are somewhat similar to that of
K₂O in that the model seems to make over-corrections relative to the nominal values at the
shorter distance, but all the results are within the error of the measurements.

The results of the soil correction are also given in Table 8. The corrected SiO₂ abundance matches the SiO₂ abundance of the closer target almost exactly. However, the target Noss has a relatively large standard deviation for SiO₂. The errors are much smaller for the other three elements, which are good tests of the model. The soil comparison is also important in that, like Bonnet Plume and the Kimberley targets, its composition differs significantly from the Murray formation on which the model is based. It also shows that the correction is unaffected by matrix effects associated with soil relative to rock (e.g., [36]). We conclude that, at least for the relatively few comparisons made here, the empirical distance correction appears to be consistent with the results.

### 4.4. General Application: The Case of the Mt. Remarkable Member, Kimberley Formation

The ChemCam observations in the Mt. Remarkable member of the Kimberley formation—the longest distance observations at the Windjana drill site, all > 5 m [9]—were originally interpreted to signal a different composition for that member, a conclusion that now seems unnecessary based on the distance trends shown in Fig. 6 and the fact that the Kimberley trends have essentially the same x-intercepts for Al, Na, and K as the Murray formation (Fig. 7 and Tables 2 and 7). Here we provide corrected abundances for the Mt. Remarkable observations. Correction is made back to the median distance of the 4930 bedrock observations in the Murray formation used in [8], which is 2.6 m. We use the distance correction model that is based on Murray data (Table 2 and Eq. 1). Figure 9 shows the corrected values of the three Mt. Remarkable member targets in comparison with the mean and standard deviation of the Dillinger
member [9], which is nearer. The corrected values are consistent with the Dillinger member in all cases. The corrections for Al₂O₃ and Na₂O seem to overshoot the nominal Dillinger abundances just slightly. This may be because a few of the Dillinger targets are themselves at longer distances. One Dillinger target at 4.7 m was removed for simplicity. Over one third of the other 17 Dillinger targets are beyond 3.5 m, ranging to 3.9 m, with a mean of 3.13 m [9]. Thus, the mean values of the Dillinger member could be corrected down slightly. The corrected Mt. Remarkable abundances of K₂O (~3 wt. %) are still high for Mars and consistent with the observations by other instruments in the Dillinger member, with interesting implications for that location [9].

An important factor to address is the choice of distance to use as the reference point for the correction. In the above example, the idea of using the median distance, where most of the data lie, seems appropriate. It would not be recommended to correct to some distance outside of the range of Mars surface observations, as this would be an extrapolation into a regime where the behavior is unknown. The shortest distance of observations on the Mars surface is ~2.22 m. There is no clear reason to correct to such a distance. In terms of the difference it would make, using the mean slopes in the Murray formation (Table 3), the abundances of SiO₂, Al₂O₃, Na₂O, and K₂O would be reduced by 0.53, 0.83, 0.17, and 0.20 wt. %, respectively. These differences are small relative to the mean accuracies for these elements, which for the Murray bedrock observations are ±5.1, 3.5, 0.62, and 0.96 wt. %, respectively.

More analyses, including dedicated laboratory measurements at varying distances, may be needed before attempting to correct all data—e.g., even targets observed at < 3 m—to a single distance as a way to improve the quality of the data overall. We do recommend using the corrections for observations in the 3.0 to ~6.0 m range, as exemplified here. Section 4.6 will discuss targets beyond 6.0 m.

## 4.5. Trace Elements: Li, Rb, Sr, Ba

ChemCam also regularly provides data on four trace elements: Li, Rb, Sr, and Ba. LIBS generally has low detection limits for the alkali and alkali-earth elements, and so these four elements are detectable above background in many of the Mars targets. Calibration of these elemental abundances is fundamentally different from that of the major elements. As described in Section 2, the major elements are calibrated using MVA. This calibration is not feasible for trace elements because their peaks are much less intense in most spectra, so the trace elements are calibrated using individual emission peaks (univariate calibration). The single emission peaks used for these elements are 670.96, 780.2, 421.67, and 614.3 nm for Li, Rb, Sr, and Ba, respectively [13, 37-39], and their upper energy transition levels are 1.85, 1.59, 2.94, and 2.72 eV, respectively. These energy levels are similar to the lowest ones in Table 6, and are much lower than those of most emission lines. These elements are thus expected to exhibit trends with distance. Data from the Murray formation generally include abundances for Li and Rb, while for most Murray observations, the abundances of Sr and Ba were below the limits of quantification. The Li and Rb data were plotted as functions of distance and fit with linear trends as in Fig. 3. However, in spite of the fact that the ChemCam precision for at least Li is excellent, at approximately ±6 ppm, the data show significant scatter, being relatively close to the limit of quantification. The R² values never exceed 0.13 for Li and 0.06 for Rb, leaving us unable to develop a model for these elements.
581
582
### 4.6. Targets Observed at the Longest Distances
583 Here we review the longest-distance targets observed by ChemCam LIBS and address whether the correction model can be applied at these distances. Table 5 shows ChemCam targets observed at distances > 6 m. There are 11 Mars targets beyond this distance for which at least some of the observation points were validated, e.g., the focus was good, noise is relatively low, continuum removal is good, the signal is deemed sufficient (> $5 \times 10^{13}$ photons of atomic emission peaks were generated at the target), and the abundances are within the acceptable range for calibration. The table gives the mean compositions for the validated points for each target. The mean shot-to-shot precisions are given in the Supplemental Materials. In general, these precisions are factors of 2-3 larger than the mean shot-to-shot standard deviations of all ChemCam observations, which is not surprising for these distances. Descriptions and images of the targets are also provided in the Supplemental Material. In total, these targets represent >50 observation points. The bottom four targets in Table 5 are iron meteorites that are outside the range of compositions for which the MOC calibration was designed. These will be discussed near the end of this section.

597
598 The observations in Table 5 cannot be compared directly with other observations to test the model at these distances. The exception is Bonnet Plume, which has already been discussed in Section 4.3.2, among the targets observed at two distances. Targets Mary River and Husky Creek are part of the Cratered Unit, a geologic unit that was not visited by the rover, with the exception of these long-distance observations [40]. Targets in the Sol 339 to ~500 range include eroded conglomerates of varying compositions. For example, the composition of the target Red Wine appears very different, with almost no iron. Target Mell is part of the wall rock at the head of Moonlight Valley at Dingo Gap [7]. It was not observed at any closer distance. Target Last Chance Canyon consists of only one observation point, as it was observed at a time when ChemCam had to use a time-consuming manual focus technique [41]. Target Eorsa is a float rock, so it is not clear if it should be similar to the Murray formation or if it originated elsewhere. Target Erongo (a portion of its spectrum is shown in Fig. 1) was selected by the AEGIS onboard software [42] in the area near Namib dune. It should represent Murray formation bedrock, so it may be the only target in this table for which the composition should be well known. It was not included in the Murray bedrock database represented by the statistics in Table 1 due to its long distance (6.57 m).

614
615 The longest bedrock target observation in the mission is Mell, observed on Sol 530 at a distance of 7.2 m (Supplementary Material) as a 10-point raster. The first several points returned reasonable spectra. However, the autofocus underperformed after the fifth point, so the rest of the raster was out of focus. Looking at the four points that were validated, the $SiO_2$ values are surprisingly low, in the mid-30s wt. % (Table 5). $Al_2O_3$ is ~10 wt. %, $Na_2O$ is ~2 wt. %, and $K_2O$ is < 1 wt. %. None of the other long-distance targets (up to 6.61 m) show such low $SiO_2$, and since there are no other targets at this distance, it is not clear if anomalously low $SiO_2$ is typical of observations at > 7 m. Portions of the averaged spectrum are shown in the Supplemental Material.

624
625 One other feature that does not seem to fit the Murray distance trends is the oxide sum (Table 1 and Supplemental Material). While a full treatment of the oxide sum was not given here, the sum
has a relatively strong trend with distance among the Murray data. Extrapolation of this trend to > 6 m would suggest oxide sums well over 100 wt. %, which are not seen in the data in Table 5.
A final characteristic that appears to be a relatively common feature of most of the observations at distances > 6 m is anomalously high FeO<sub>T</sub>. It is seen on all targets in Table 5 except Bonnet Plume, Red Wine (already mentioned), and the single point on Last Chance Canyon. The mean FeO<sub>T</sub> of all of the 4930 points in the Murray Formation bedrock [8] is 19.1 wt. %. Other geologic formations generally had similar or lower FeO<sub>T</sub>, although some observations in the Kimberley formation (but not at Windjana) showed FeO<sub>T</sub> > 20 wt. % [9]. However, the fact that most targets in Table 6 show higher FeO<sub>T</sub>, and that it is especially enriched in the longest target, Mell, leaves us to tentatively conclude that the longest observations seem to be anomalously high in FeO<sub>T</sub>. If so, this and the oxide sums represent phenomena not observed at distances < 6 m. The behavior of iron abundances at long distances could be related to the fact that iron meteorites can be observed at much longer distances, as mentioned next. In any case, the distance correction model presented above should be limited to the distances over which it has been validated, that is, within ~6 m.

The final entries in Table 5 are iron meteorites. The ChemCam team noticed that iron meteorites can be reliably observed at longer distances than typical silicate rocks, as the laser beam couples better to the iron-nickel of the meteorites than to the silicates comprising the bedrock. Stated another way, laser plasmas are observed to longer distances on iron-rich surfaces. ChemCam has thus made a number of observations of iron meteorites to distances as far as 9.1 m [32, 43]. Portions of the spectrum of the longest-distance target are shown in the Supplemental Material. The compositions of these meteorites fall outside of the range of the ChemCam calibration model, so distance effects cannot be checked on these in the same way as the silicate bedrock.
Laboratory work has been done to understand the Ni/Fe ratios, which allows classification of the meteorites [42]. In this way, the meteorite observations are relatively important even to the longest distance observed.

# 5. Conclusions

ChemCam is the first instrument of its kind, designed to provide qualitative and quantitative elemental abundances at a submillimeter scale and at a range of distances using LIBS observations made through a telescope. Here we have explored the elemental quantification as a function of distance, exclusively using the extensive Mars database. A number of the quantified elements are stable with respect to distance to at least 6 m, while four elements—Al, Na, K, and to some extent, Si—show systematic trends with distance. We hypothesize that these trends occur because the strongest emission lines of these elements in the spectral range of ChemCam have lower upper-energy transition levels than the bulk of the emission lines. As the laser beam is projected to increasing distances, its photon density decreases, affecting these transitions. Emission lines with lower upper-energy transition levels are less strongly affected by the decreasing laser irradiance than the rest of the emission lines. Following normalization to the total emission, this results in higher predicted abundances for these elements at longer distances. Accordingly, most published studies have only used ChemCam observations within 3.5 m of the instrument. This is not usually a problem due to the sparsity of long-distance observations. As of Sol 2837, just 3013 observations comprising 13% of the validated ChemCam observations (~200
unique targets) are at distances > 3.5 m. However, at times, features at longer distances need to be investigated, as evidenced by this 13% of the observations. The Mt. Remarkable member of the Kimberley formation is one such example.

Observations from the generally fine grained Murray formation [8] provided a data set to define an empirical correction model for the four elements of interest, for use in correcting long-distance observations by ChemCam, provided that they are within the distance range used to build the model. The model tested well against data of significantly different compositions in the Kimberley formation and for targets that were each observed at two different distances. The latter includes Mars soil, which also has a significantly different composition from the Murray formation data set used to build the model. The results obtained in this work are generally consistent with earlier distance-correction work [4, 5]. However, a direct comparison is not generally possible, as the earlier work corrected individual emission peaks, and it was tested on a less mature MVA model [10] to provide elemental abundances. Due to its computational requirements, the earlier work was not ready to apply directly to the Mars data set. Given the limitations of the earlier work, our results provide a more utilitarian and consistent correction for ChemCam data.

The method developed here will be useful, not only for ChemCam, but also for future remote LIBS instruments, such as SuperCam [44-46] MarsCode [47], and terrestrial instruments. For example, it should be feasible to construct distance trends using laboratory data that can then be used to obtain a similar empirical distance correction. Given that all of the strong emission lines for potassium--and to some extent the other three elements—are low energy, it seems that all calibrations that normalize the spectra to the total emission will have this feature, thus an empirical correction of this nature will be needed. Fortunately, the corrections presented here are easy to implement.

We are grateful to the many people involved in building and operating the *Curiosity* rover and the ChemCam instrument to obtain these data. Helpful comments by L. Le Deit, G. Caravaca, O. Gasnault, and O. Forni are gratefully acknowledged. Support from additional people at LANL and elsewhere is also acknowledged: BLB, SEJ, SDG. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

This work was supported at LANL and the U.S. Geological Survey by the NASA Mars Exploration Program, which also supported the operation of the *Curiosity* rover. Work at UML and LANL was supported by NASA grant number NNX15AP84A. Work in France was supported by CNRS. Work in Denmark was supported by the Carlsberg Foundation.
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851 Malin, D. Blake, R. Gellert, P. Mahaffy, R. Wiens, S. Maurice, J.A. Grant, S. Wilson, R.
852 Anderson, L. Beegle, R. Arvidson, B. Hallet, R. Sletten, M. Rice, J. Bell, J. Griffes, B. Ehlmann,
853 T. Bristow, M. Palucis, W.E. Dietrich, G. Dromart, J. Eigenbrode, A. Fraeman, C. Hardgrove, K.
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855 McCloskey, M. Meyer, M. Minitti, D. Oehler, A. Okon, H. Newsom, T. Parker, S. Rowland, S.
856 Squyres, A. Steele, E. Stolper, R. Summons, A. Treiman, R. Williams, A. Yingst, the MSL
857 Science Team, A habitable fluvio-lacustrine environment at Yellowknife Bay, Gale crater, Mars.
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859
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874
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877 Mangold, A. Cousin, R.B. Anderson, V. Payré, W. Rapin, D. Vaniman, R.V. Morris, D. Blake,
878 S. Gupta, V. Sautter, P.-Y. Meslin, C. Bedford, P. Edwards, M. Rice, K.M. Kinch, R. Milliken,
879 R. Gellert, L. Thompson, C.B. Clark, K.S. Edgett, D. Sumner, A. Fraeman, M.B. Madsen, I.
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882 10.1002/2017GL073323.

883
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885 V.K. Fox, K.A. Bennett, S. Maurice, R.C. Wiens, MSL/ChemCam at Glen Torridon:
886 Geochemistry of the orbitally-identified clay-bearing unit of Gale crater, Ninth Int. Conference
887 on Mars (2019) 6125.

888
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890 Gupta, S. Gwizd, E. Heydari, S. Maurice, M. Nachon, H. Newson, J. Schieber, K. Stack-Morgan,
891 N. Stein, R.C. Wiens, Grain size variations in the Murray formation: Stratigraphic evidence for
892 changing depositional environments in Gale crater, Mars. J. Geophys. Res. Planets (2020)
893 doi:10.1029/2019JE006230.

894
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896 Lasue, S. Maurice, V. Sautter, S. Le Mouelic, R.C. Wiens, C. Fabre, W. Goetz, D. Bish, N.
897 Mangold, B. Ehlmann, N. Lanza, A.-M. Harri, R. Anderson, E. Rampe, T.H. McConnochie, P.
898 Pinet, D. Blaney, R. Léveillé, D. Archer, B. Barraclough, S. Bender, D. Blake, J.G. Blank, N.
899 Bridges, B.C. Clark, L. DeFlores, D. Delapp, G. Dromart, M.D. Dyar, M. Fisk, B. Gondet, J.
900 Grotzinger, K. Herkenhoff, J. Johnson, J.-L. Lacour, Y. Langevin, L. Leshin, E. Lewin, M.B.
901 Madsen, N. Melikechi, A. Mezzacappa, M.A. Mishna, J.E. Moores, H. Newsom, A. Ollila, R.
902 Perez, N. Renno, J.-B. Sirven, R. Tokar, M. de la Torre, L. d'Uston, D. Vaniman, A. Yingst, and
903 the MSL Science Team, Soil diversity and hydration as observed by ChemCam at Gale crater,
904 Mars. Science 341 (2014) doi: 10.1126/science.1238670.

905
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907 Forni, R. Tokar, J. Lasue, D. Vaniman, A. Ollila, S. Schroeder, V. Sautter, D. Blaney, S. Le
908 Mouelic, M. Nachon, G. Dromart, H. Newsom, S. Maurice, M.D. Dyar, N. Lanza, B. Clark, S.
909 Clegg, W. Goetz, and the MSL Science Team, Compositions of coarse and fine particles in
910 martian soils at Gale: A window into the production of soils. Icarus 249 (2014) 22-42.
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914 Williams, N. Stein, S. Schroeder, W. Rapin, V. Sautter, V. Payré, S. Maurice, R.C. Wiens, P.
915 Pinet, Geochemistry of the Bagnold Dune Field as observed by ChemCam, and comparison with
916 other aeolian deposits at Gale crater. J. Geophys. Res. Planets 122 (2017)
917 doi:10.1002/2017JE005261.

918
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921 Wellington, and R.C. Wiens, Visible/near-infrared spectral diversity from in situ observations of
922 the Bagnold Dune Field sands in Gale crater, Mars. J. Geophys. Res. Planets, 122 (2017) 2655-
923 2684, doi:10.1002/2016JE005187.

924
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926 iron meteorite detections by the Mars Science Laboratory *Curiosity* rover. 51st Lunar and
927 Planetary Science Conference (2020) 1136.

928
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931 complex route to quantitative analysis. In *Laser-Induced Breakdown Spectroscopy (LIBS)* (A.W.
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941 R.C. Wiens, S. Maurice, W. Rapin, J.F. Fronton, Laser-induced breakdown spectroscopy (LIBS)
942 characterization of Martian soil analogs: Implications for ChemCam data at Gale crater, Mars.
943 Icarus, in press (2021).

944 [37] C. Fabre, S. Maurice, A. Cousin, R.C. Wiens, O. Forni, V. Sautter, D. Guillaume, Onboard
945 calibration igneous targets for the Mars Science Laboratory *Curiosity* rover and the Chemistry
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950 V. Sautter, S. Maurice, S.M. Clegg, O. Gasnault, O. Forni, R. Tokar, E. Lewin, M.D. Dyar, J.
951 Lasue, R. Anderson, S.M. McLennan, J. Bridges, D. Vaniman, N. Lanza, C. Fabre, N. Melikechi,
952 G.M. Perrett, J.L. Campbell, P.L. King, B. Barraclough, D. Delapp, S. Johnstone, P.-Y. Meslin,
953 A. Rosen-Gooding, J. Williams, the MSL Science Team, Trace element geochemistry (Li, Ba,
954 Sr, and Rb) using *Curiosity's* ChemCam: Early results for Gale crater from Bradbury Landing
955 Site to Rocknest. J. Geophys. Res., 119 (2014) 255-285, doi:10.1002/2013JE004517.

957 [39] A. Cousin, O. Forni, P.-Y. Meslin, S. Clegg, A. Ollila, O. Gasnault, S. Maurice, R.C. Wiens,
958 the ChemCam Team, New quantification of Ba and Sr in ChemCam LIBS data and implications
959 for geological interpretations. Lunar Planet. Sci. 51 (2020) 2160.

961 [40] R.B. Anderson, J.C. Bridges, A. Williams, L. Edgar, A. Ollila, J. Williams, M. Nachon, N.
962 Mangold, J. Schieber, S. Gupta, G. Dromart, R.C. Wiens, S. Le Mouelic, O. Forni, N. Lanza, A.
963 Mezzacappa, V. Sautter, M. Fisk, D. Blaney, B. Clark, S. Clegg, O. Gasnault, J. Lasue, R.
964 Leveillé, E. Lewin, K.W. Lewis, S. Maurice, S.P. Schwenzer, D. Vaniman, ChemCam results
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969 D. Delapp, C. Donny, S. Johnstone, C. Little, E. Lorigny, R. McInroy, S. Maurice, N. Mittal, B.
970 Pavri, R. Perez, R.C. Wiens, C. Yana, Restoration of the autofocus capability of the ChemCam
971 instrument onboard the *Curiosity* rover. SpaceOps 2016 Conference, (AIAA 2016-2539) (2016)
972 doi:10.2514/6.2016-2539.

974 [42] R. Francis, T. Estlin, G. Doran, S. Johnstone, D. Gaines, V. Verma, M. Burl, J. Frydenvang,
975 S. Montano, R.C. Wiens, S. Schaffer, O. Gasnault, L. Deflores, D. Blaney, B. Bornstein, AEGIS
976 autonomous targeting for ChemCam on Mars Science Laboratory: Deployment and results of
977 initial science team use. *Science Robotics* 2 (2017) eaan4582.

979 [43] P.-Y. Meslin, D. Wellington, R.C. Wiens, J.R. Johnson, J. Van Beek, O. Gasnault, V.
980 Sautter, I. Maroger, J. Lasue, P. Beck, J.C. Bridges, B. Cohen, J.W. Ashley, A.G. Fairen, H.
981 Newsom, A. Cousin, O. Forni, F. Calef, W. Rapin, S. Maurice, B. Chide, S. Schroeder, W.
982 Goetz, N. Mangold, T. Gabriel, N. Lanza, P. Pinet, Diversity and areal density of iron-nickel
983 meteorites analyzed by ChemCam in Gale crater. 50th Lunar and Planetary Science Conference
984 (2019) 3179.

986
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[46] R.C. Wiens, S. Maurice, S.H. Robinson, A.E. Nelson, P. Cais, P. Bernardi, R.T. Newell, S.M. Clegg, S.K. Sharma, S. Storms, J. Deming, D. Beckman, A.M. Ollila, O. Gasnault, E. Auden, R.B. Anderson, Y. André, S.M. Angel, G. Arana, P. Beck, J. Becker, K. Benzerara, S. Bernard, O. Beyssac, L. Borges, B. Bousquet, K. Boyd, M. Caffrey, J. Carlson, K. Castro, J. Celis, B. Chide, K. Clark, E. Cloutis, E.C. Cordoba, A. Cousin, M. Dale, L. Deflores, D. Delapp, M. Deleuze, M. Dirmyer, C. Donny, G. Dromart, M.G. Duran, M. Egan, J. Ervin, C. Fabre, A. Fau, W.W. Fischer, O. Forni, T. Fouchet, R. Fresquez, J. Frydenvang, D. Gasway, I. Gontijo, J. Grotzinger, X. Jacob, S. Jacquinod, J.R. Johnson, R.A. Klisiewicz, J. Lake, N. Lanza, J. Laserna, J. Lasue, S. Le Mouelic, C. Legett IV, R. Leveille, E. Lewin, R. Lorenz, E. Lorigny, S.P. Love, B. Lucero, J.M. Madariaga, M. Madsen, S. Madsen, N. Mangold, J.A. Manrique, J.P. Martinez, J. Martinez-Frias, K.P. McCabe, T.H. McConnochie, J.M. McGlown, S.M. McLennan, N. Melikechi, P.-Y. Meslin, J.M. Michel, D. Mimoun, A. Misra, G. Montagnac, F. Montmessin, V. Mousset, N. Murdoch, H. Newsom, L.A. Ott, Z.R. Ousnamer, L. Pares, Y. Parot, R. Pawluczyk,
1033 C.G. Peterson, P. Pilleri, P. Pinet, G. Pont, F. Poulet, C. Provost, B. Quertier, H. Quinn, W.

1034 Rapin, J.-M. Reess, A.H. Regan, A.L. Reyes-Newell, P.J. Romano, C. Royer, F. Rull, B.F.

1035 Sandoval, J.H. Sarrao, V. Sautter, M.J. Schoppers, S. Schroeder, D. Seitz, T. Shepherd, P.

1036 Sobron, B. Dubois, V. Sridhar, M.J. Toplis, I. Torre-Fdez, I.A. Trettel, M. Underwood, A.

1037 Valdez, J. Valdez, D. Venhaus, P. Willis, The SuperCam instrument suite on the NASA Mars

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1039 https://doi.org/10.1007/s11214-020-00777-5.

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1042 planetary science, in *Laser-Induced Breakdown Spectroscopy, 2nd Edition*. J.P. Singh and S.N.

1043 Thakur, eds. Elsevier (2020) pp. 441-471.
<table><caption>Table 1. Statistics of linear fits with distance.</caption><thead><tr><th>Class</th><th>SiO<sub>2</sub></th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcept</th></tr></thead><tbody><tr><td>Pahrump</td><td></td><td>237</td><td>0.04</td><td>1.29</td><td>0.42</td><td>2.15</td><td>-37.7</td></tr><tr><td>Hartmanns Valley</td><td></td><td>467</td><td>0.02</td><td>0.88</td><td>0.32</td><td>1.44</td><td>-56.9</td></tr><tr><td>Karasburg</td><td></td><td>265</td><td>0.06</td><td>1.19</td><td>0.62</td><td>1.75</td><td>-42.2</td></tr><tr><td>Sutton Island</td><td></td><td>1276</td><td>0.16</td><td>2.09</td><td>1.82</td><td>2.36</td><td>-22.9</td></tr><tr><td>Blunts Point</td><td></td><td>304</td><td>0.25</td><td>1.63</td><td>1.31</td><td>1.95</td><td>-30.8</td></tr><tr><td>VRR - Pettegrove Point</td><td></td><td>974</td><td>0.09</td><td>1.38</td><td>1.11</td><td>1.65</td><td>-36.5</td></tr><tr><td>VRR - Jura (gray)</td><td></td><td>555</td><td>0.01</td><td>-0.70</td><td>-1.30</td><td>-0.09</td><td>80.3</td></tr><tr><td>VRR - Jura</td><td></td><td>556</td><td>0.08</td><td>1.66</td><td>1.20</td><td>2.13</td><td>-30.2</td></tr><tr><td>Glen Torridon - Pebbles</td><td></td><td>311</td><td>0.18</td><td>1.27</td><td>0.97</td><td>1.58</td><td>-42.2</td></tr><tr><td>Glen Torridon - Coherent</td><td></td><td>362</td><td>0.09</td><td>1.35</td><td>0.90</td><td>1.80</td><td>-37.6</td></tr><tr><td>Glen Torridon - Rubbly</td><td></td><td>412</td><td>0.25</td><td>1.46</td><td>1.22</td><td>1.70</td><td>-36.1</td></tr></tbody></table><table><thead><tr><th>Class</th><th>TiO<sub>2</sub></th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcept</th></tr></thead><tbody><tr><td>Pahrump</td><td></td><td>237</td><td>0.00</td><td>-0.03</td><td>-0.07</td><td>0.02</td><td>44.4</td></tr><tr><td>Hartmanns Valley</td><td></td><td>467</td><td>0.01</td><td>0.03</td><td>0.01</td><td>0.05</td><td>-31.2</td></tr><tr><td>Karasburg</td><td></td><td>265</td><td>0.01</td><td>0.01</td><td>0.00</td><td>0.03</td><td>-74.2</td></tr><tr><td>Sutton Island</td><td></td><td>1276</td><td>0.03</td><td>0.03</td><td>0.02</td><td>0.04</td><td>-30.5</td></tr><tr><td>Blunts Point</td><td></td><td>304</td><td>0.02</td><td>0.02</td><td>0.00</td><td>0.04</td><td>-47.4</td></tr><tr><td>VRR - Pettegrove Point</td><td></td><td>974</td><td>0.00</td><td>0.00</td><td>-0.01</td><td>0.02</td><td>-916.2</td></tr><tr><td>VRR - Jura (gray)</td><td></td><td>555</td><td>0.00</td><td>-0.04</td><td>-0.09</td><td>0.01</td><td>31.8</td></tr><tr><td>VRR - Jura</td><td></td><td>556</td><td>0.01</td><td>0.04</td><td>0.01</td><td>0.06</td><td>-25.7</td></tr><tr><td>Glen Torridon - Pebbles</td><td></td><td>311</td><td>0.03</td><td>0.04</td><td>0.01</td><td>0.06</td><td>-24.7</td></tr><tr><td>Glen Torridon - Coherent</td><td></td><td>362</td><td>0.00</td><td>0.03</td><td>-0.03</td><td>0.09</td><td>-36.7</td></tr><tr><td>Glen Torridon - Rubbly</td><td></td><td>412</td><td>0.07</td><td>0.04</td><td>0.03</td><td>0.05</td><td>-21.4</td></tr></tbody></table><table><thead><tr><th>Class</th><th>Al<sub>2</sub>O<sub>3</sub></th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcept</th></tr></thead><tbody><tr><td>Pahrump</td><td></td><td>237</td><td>0.31</td><td>2.23</td><td>1.80</td><td>2.66</td><td>-3.6</td></tr><tr><td>Hartmanns Valley</td><td></td><td>467</td><td>0.29</td><td>2.24</td><td>1.92</td><td>2.56</td><td>-2.8</td></tr><tr><td>Karasburg</td><td></td><td>265</td><td>0.40</td><td>1.76</td><td>1.50</td><td>2.03</td><td>-3.8</td></tr><tr><td>Sutton Island</td><td></td><td>1276</td><td>0.39</td><td>2.22</td><td>2.07</td><td>2.38</td><td>-2.8</td></tr><tr><td>Blunts Point</td><td></td><td>304</td><td>0.53</td><td>2.24</td><td>2.00</td><td>2.47</td><td>-2.9</td></tr><tr><td>VRR - Pettegrove Point</td><td></td><td>974</td><td>0.43</td><td>2.64</td><td>2.45</td><td>2.83</td><td>-1.9</td></tr><tr><td>VRR - Jura (gray)</td><td></td><td>555</td><td>0.19</td><td>1.70</td><td>1.41</td><td>2.00</td><td>-4.5</td></tr><tr><td>VRR - Jura</td><td></td><td>556</td><td>0.36</td><td>2.14</td><td>1.90</td><td>2.38</td><td>-2.6</td></tr><tr><td>Glen Torridon - Pebbles</td><td></td><td>311</td><td>0.58</td><td>2.36</td><td>2.14</td><td>2.59</td><td>-2.3</td></tr><tr><td>Glen Torridon - Coherent</td><td></td><td>362</td><td>0.26</td><td>1.98</td><td>1.63</td><td>2.32</td><td>-3.4</td></tr><tr><td>Glen Torridon - Rubbly</td><td></td><td>412</td><td>0.65</td><td>2.15</td><td>2.00</td><td>2.30</td><td>-2.8</td></tr></tbody></table><table><thead><tr><th>Class</th><th>FeO<sub>r</sub></th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcept</th></tr></thead><tbody><tr><td>Pahrump</td><td></td><td>237</td><td>0.01</td><td>-0.48</td><td>-1.06</td><td>0.10</td><td>39.7</td></tr><tr><td>Hartmanns Valley</td><td></td><td>467</td><td>0.01</td><td>0.40</td><td>0.09</td><td>0.71</td><td>-46.0</td></tr><tr><td>Karasburg</td><td></td><td>265</td><td>0.05</td><td>-0.50</td><td>-0.78</td><td>-0.23</td><td>41.6</td></tr><tr><td>Sutton Island</td><td></td><td>1276</td><td>0.00</td><td>-0.02</td><td>-0.17</td><td>0.12</td><td>860.5</td></tr><tr><td>Blunts Point</td><td></td><td>304</td><td>0.02</td><td>-0.30</td><td>-0.52</td><td>-0.08</td><td>69.3</td></tr><tr><td>VRR - Pettegrove Point</td><td></td><td>974</td><td>0.00</td><td>-0.08</td><td>-0.25</td><td>0.09</td><td>253.3</td></tr><tr><td>VRR - Jura (gray)</td><td></td><td>555</td><td>0.04</td><td>0.88</td><td>0.50</td><td>1.26</td><td>-19.3</td></tr><tr><td>VRR - Jura</td><td></td><td>556</td><td>0.03</td><td>0.63</td><td>0.31</td><td>0.95</td><td>-28.6</td></tr><tr><td>Glen Torridon - Pebbles</td><td></td><td>311</td><td>0.00</td><td>0.09</td><td>-0.16</td><td>0.34</td><td>-217.3</td></tr><tr><td>Glen Torridon - Coherent</td><td></td><td>362</td><td>0.00</td><td>0.01</td><td>-0.29</td><td>0.31</td><td>-1963.9</td></tr><tr><td>Glen Torridon - Rubbly</td><td></td><td>412</td><td>0.00</td><td>0.07</td><td>-0.10</td><td>0.25</td><td>-259.7</td></tr></tbody></table><table><thead><tr><th>Class</th><th>MgO</th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcept</th></tr></thead><tbody><tr><td>Pahrump</td><td></td><td>237</td><td>0.01</td><td>0.35</td><td>-0.02</td><td>0.72</td><td>-8.9</td></tr><tr><td>Hartmanns Valley</td><td></td><td>467</td><td>0.01</td><td>-0.21</td><td>-0.47</td><td>0.05</td><td>26.9</td></tr><tr><td>Karasburg</td><td></td><td>265</td><td>0.01</td><td>-0.32</td><td>-0.70</td><td>0.07</td><td>19.9</td></tr><tr><td>Sutton Island</td><td></td><td>1276</td><td>0.03</td><td>-0.66</td><td>-0.89</td><td>-0.43</td><td>12.2</td></tr><tr><td>Blunts Point</td><td></td><td>304</td><td>0.02</td><td>-0.31</td><td>-0.59</td><td>-0.03</td><td>18.0</td></tr><tr><td>VRR - Pettegrove Point</td><td></td><td>974</td><td>0.06</td><td>-0.40</td><td>-0.50</td><td>-0.30</td><td>15.0</td></tr><tr><td>VRR - Jura (gray)</td><td></td><td>555</td><td>0.06</td><td>-0.72</td><td>-0.96</td><td>-0.47</td><td>8.3</td></tr><tr><td>VRR - Jura</td><td></td><td>556</td><td>0.02</td><td>-0.31</td><td>-0.47</td><td>-0.14</td><td>19.0</td></tr><tr><td>Glen Torridon - Pebbles</td><td></td><td>311</td><td>0.03</td><td>-0.24</td><td>-0.40</td><td>-0.08</td><td>22.5</td></tr><tr><td>Glen Torridon - Coherent</td><td></td><td>362</td><td>0.00</td><td>-0.13</td><td>-0.36</td><td>0.10</td><td>58.7</td></tr><tr><td>Glen Torridon - Rubbly</td><td></td><td>412</td><td>0.03</td><td>-0.27</td><td>-0.41</td><td>-0.12</td><td>22.9</td></tr></tbody></table>

N = number of observation points; UB = upper bound; LB = lower bound, Intcpt = intercept.
Table 1, continued.

<table><thead><tr><th>Class CaO</th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcpt</th></tr></thead><tbody><tr><td>Pahrump</td><td>237</td><td>0.00</td><td>-0.14</td><td>-0.44</td><td>0.17</td><td>35.6</td></tr><tr><td>Hartmanns Valley</td><td>467</td><td>0.00</td><td>-0.01</td><td>-0.18</td><td>0.16</td><td>375.2</td></tr><tr><td>Karasburg</td><td>265</td><td>0.00</td><td>-0.02</td><td>-0.22</td><td>0.17</td><td>102.4</td></tr><tr><td>Sutton Island</td><td>1276</td><td>0.00</td><td>0.02</td><td>-0.05</td><td>0.09</td><td>-88.9</td></tr><tr><td>Blunts Point</td><td>304</td><td>0.01</td><td>0.08</td><td>-0.02</td><td>0.19</td><td>-20.8</td></tr><tr><td>VRR - Pettegrove Point</td><td>974</td><td>0.01</td><td>0.12</td><td>0.02</td><td>0.22</td><td>-13.3</td></tr><tr><td>VRR - Jura (gray)</td><td>555</td><td>0.01</td><td>-0.13</td><td>-0.27</td><td>0.02</td><td>19.8</td></tr><tr><td>VRR - Jura</td><td>556</td><td>0.02</td><td>0.21</td><td>0.07</td><td>0.35</td><td>-6.7</td></tr><tr><td>Glen Torridon - Pebbles</td><td>311</td><td>0.03</td><td>0.12</td><td>0.04</td><td>0.20</td><td>-6.9</td></tr><tr><td>Glen Torridon - Coherent</td><td>362</td><td>0.02</td><td>0.20</td><td>0.05</td><td>0.35</td><td>-6.9</td></tr><tr><td>Glen Torridon - Rubbly</td><td>412</td><td>0.00</td><td>-0.02</td><td>-0.07</td><td>0.04</td><td>82.2</td></tr></tbody></table>

<table><thead><tr><th>Class Na<sub>2</sub>O</th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcpt</th></tr></thead><tbody><tr><td>Pahrump</td><td>237</td><td>0.19</td><td>0.37</td><td>0.27</td><td>0.47</td><td>-5.7</td></tr><tr><td>Hartmanns Valley</td><td>467</td><td>0.16</td><td>0.37</td><td>0.29</td><td>0.45</td><td>-5.1</td></tr><tr><td>Karasburg</td><td>265</td><td>0.19</td><td>0.35</td><td>0.26</td><td>0.43</td><td>-4.9</td></tr><tr><td>Sutton Island</td><td>1276</td><td>0.29</td><td>0.43</td><td>0.39</td><td>0.46</td><td>-3.4</td></tr><tr><td>Blunts Point</td><td>304</td><td>0.46</td><td>0.50</td><td>0.44</td><td>0.57</td><td>-2.7</td></tr><tr><td>VRR - Pettegrove Point</td><td>974</td><td>0.32</td><td>0.56</td><td>0.51</td><td>0.61</td><td>-2.5</td></tr><tr><td>VRR - Jura (gray)</td><td>555</td><td>0.10</td><td>0.33</td><td>0.25</td><td>0.42</td><td>-6.1</td></tr><tr><td>VRR - Jura</td><td>556</td><td>0.14</td><td>0.35</td><td>0.28</td><td>0.42</td><td>-4.8</td></tr><tr><td>Glen Torridon - Pebbles</td><td>311</td><td>0.49</td><td>0.42</td><td>0.37</td><td>0.47</td><td>-3.6</td></tr><tr><td>Glen Torridon - Coherent</td><td>362</td><td>0.25</td><td>0.48</td><td>0.39</td><td>0.57</td><td>-2.9</td></tr><tr><td>Glen Torridon - Rubbly</td><td>412</td><td>0.62</td><td>0.48</td><td>0.45</td><td>0.52</td><td>-2.7</td></tr></tbody></table>

<table><thead><tr><th>Class K<sub>2</sub>O</th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcpt</th></tr></thead><tbody><tr><td>Pahrump</td><td>237</td><td>0.24</td><td>0.30</td><td>0.23</td><td>0.36</td><td>-0.5</td></tr><tr><td>Hartmanns Valley</td><td>467</td><td>0.06</td><td>0.21</td><td>0.14</td><td>0.28</td><td>-3.9</td></tr><tr><td>Karasburg</td><td>265</td><td>0.09</td><td>0.23</td><td>0.15</td><td>0.32</td><td>-2.6</td></tr><tr><td>Sutton Island</td><td>1276</td><td>0.22</td><td>0.44</td><td>0.40</td><td>0.49</td><td>0.1</td></tr><tr><td>Blunts Point</td><td>304</td><td>0.49</td><td>0.55</td><td>0.49</td><td>0.62</td><td>0.0</td></tr><tr><td>VRR - Pettegrove Point</td><td>974</td><td>0.15</td><td>0.40</td><td>0.34</td><td>0.46</td><td>-0.9</td></tr><tr><td>VRR - Jura (gray)</td><td>555</td><td>0.16</td><td>0.45</td><td>0.36</td><td>0.53</td><td>-0.4</td></tr><tr><td>VRR - Jura</td><td>556</td><td>0.08</td><td>0.36</td><td>0.25</td><td>0.46</td><td>-1.4</td></tr><tr><td>Glen Torridon - Pebbles</td><td>311</td><td>0.34</td><td>0.53</td><td>0.44</td><td>0.61</td><td>-1.1</td></tr><tr><td>Glen Torridon - Coherent</td><td>362</td><td>0.10</td><td>0.36</td><td>0.25</td><td>0.47</td><td>-0.5</td></tr><tr><td>Glen Torridon - Rubbly</td><td>412</td><td>0.55</td><td>0.57</td><td>0.52</td><td>0.62</td><td>-0.4</td></tr></tbody></table>

<table><thead><tr><th>Class OXIDE SUM</th><th>N</th><th>R<sup>2</sup></th><th>Slope</th><th>95% UB</th><th>95% LB</th><th>X-Intcpt</th></tr></thead><tbody><tr><td>Pahrump</td><td>237</td><td>0.42</td><td>3.89</td><td>3.31</td><td>4.48</td><td>-22.4</td></tr><tr><td>Hartmanns Valley</td><td>467</td><td>0.33</td><td>3.92</td><td>3.41</td><td>4.43</td><td>-22.1</td></tr><tr><td>Karasburg</td><td>265</td><td>0.29</td><td>2.70</td><td>2.19</td><td>3.21</td><td>-33.1</td></tr><tr><td>Sutton Island</td><td>1276</td><td>0.51</td><td>4.55</td><td>4.31</td><td>4.80</td><td>-18.7</td></tr><tr><td>Blunts Point</td><td>304</td><td>0.68</td><td>4.42</td><td>4.08</td><td>4.77</td><td>-19.7</td></tr><tr><td>VRR - Pettegrove Point</td><td>974</td><td>0.40</td><td>4.61</td><td>4.26</td><td>4.97</td><td>-18.6</td></tr><tr><td>VRR - Jura (gray)</td><td>555</td><td>0.04</td><td>1.78</td><td>1.02</td><td>2.53</td><td>-52.2</td></tr><tr><td>VRR - Jura</td><td>556</td><td>0.45</td><td>5.09</td><td>4.62</td><td>5.55</td><td>-16.6</td></tr><tr><td>Glen Torridon - Pebbles</td><td>311</td><td>0.65</td><td>4.58</td><td>4.20</td><td>4.96</td><td>-19.1</td></tr><tr><td>Glen Torridon - Coherent</td><td>362</td><td>0.43</td><td>4.27</td><td>3.76</td><td>4.78</td><td>-20.4</td></tr><tr><td>Glen Torridon - Rubbly</td><td>412</td><td>0.76</td><td>4.50</td><td>4.25</td><td>4.75</td><td>-19.4</td></tr></tbody></table>

N = number of observation points; UB = upper bound; LB = lower bound, Intcpt = intercept.
1050

<table><caption>Table 2. R², slope, and x-intercept values for the Murray classes with the highest coefficients of variation.</caption><thead><tr><th rowspan="2">Class</th><th colspan="3">SiO<sub>2</sub></th><th colspan="3">Al<sub>2</sub>O<sub>3</sub></th><th colspan="3">Na<sub>2</sub>O</th><th colspan="3">K<sub>2</sub>O</th></tr><tr><th>R<sup>2</sup></th><th>Slope</th><th>x-intcpt</th><th>R<sup>2</sup></th><th>Slope</th><th>x-intcpt</th><th>R<sup>2</sup></th><th>Slope</th><th>x-intcpt</th><th>R<sup>2</sup></th><th>Slope</th><th>x-intcpt</th></tr></thead><tbody><tr><td>GT Rubbly</td><td>0.25</td><td>1.46</td><td>-36.13</td><td>0.65</td><td>2.15</td><td>-2.83</td><td>0.62</td><td>0.48</td><td>-2.65</td><td>0.55</td><td>0.57</td><td>-0.44</td></tr><tr><td>Blunts Point</td><td>0.25</td><td>1.63</td><td>-30.79</td><td>0.53</td><td>2.24</td><td>-2.91</td><td>0.46</td><td>0.50</td><td>-2.70</td><td>0.49</td><td>0.55</td><td>-0.01</td></tr><tr><td>GT Pebbles</td><td>0.18</td><td>1.27</td><td>-42.16</td><td>0.58</td><td>2.36</td><td>-2.33</td><td>0.49</td><td>0.42</td><td>-3.57</td><td>0.34</td><td>0.53</td><td>-1.11</td></tr><tr><td>MEAN</td><td></td><td>1.45</td><td>-36.36</td><td></td><td>2.25</td><td>-2.69</td><td></td><td>0.47</td><td>-2.97</td><td></td><td>0.55</td><td>-0.52</td></tr><tr><td>STD DEV</td><td></td><td>0.18</td><td>5.69</td><td></td><td>0.11</td><td>0.32</td><td></td><td>0.04</td><td>0.52</td><td></td><td>0.02</td><td>0.56</td></tr></tbody></table>

1051

Intcpt = intercept

1052

1053
<table><caption>Table 3. Mean abundances for targets observed at two different distances.</caption><thead><tr><th>Target</th><th>Dist. (m)</th><th>Sol</th><th># Pts.</th><th>SiO<sub>2</sub></th><th>TiO<sub>2</sub></th><th>Al<sub>2</sub>O<sub>3</sub></th><th>FeO<sub>T</sub></th><th>MgO</th><th>CaO</th><th>Na<sub>2</sub>O</th><th>K<sub>2</sub>O</th><th>Sum</th><th>Tot. Emission</th></tr></thead><tbody><tr><td>Bonnet Plume</td><td>6.2</td><td>171</td><td>6</td><td>48.6</td><td>1.7</td><td>16.3</td><td>17.3</td><td>7.0</td><td>7.7</td><td>3.3</td><td>0.6</td><td>102.5</td><td>1.6E+14</td></tr><tr><td>Bonnet Plume</td><td>3.5</td><td>149</td><td>9</td><td>47.9</td><td>1.0</td><td>10.8</td><td>19.7</td><td>9.0</td><td>5.6</td><td>3.0</td><td>0.6</td><td>97.5</td><td>5.5E+14</td></tr><tr><td>Hallaig</td><td>4.5</td><td>2413</td><td>5</td><td>57.2</td><td>1.0</td><td>15.8</td><td>19.3</td><td>5.7</td><td>1.4</td><td>3.5</td><td>2.6</td><td>106.4</td><td>2.7E+14</td></tr><tr><td>Hallaig</td><td>2.4</td><td>2416</td><td>10</td><td>54.1</td><td>0.9</td><td>10.6</td><td>19.4</td><td>6.2</td><td>1.7</td><td>2.3</td><td>1.3</td><td>96.5</td><td>3.9E+14</td></tr><tr><td>Glen Lyon</td><td>4.0</td><td>2540</td><td>9</td><td>57.7</td><td>0.9</td><td>14.3</td><td>19.7</td><td>5.3</td><td>1.3</td><td>3.0</td><td>3.1</td><td>105.3</td><td>2.5E+14</td></tr><tr><td>Glen Lyon</td><td>2.7</td><td>2481</td><td>9</td><td>56.0</td><td>1.0</td><td>11.5</td><td>19.6</td><td>6.0</td><td>1.3</td><td>2.4</td><td>2.1</td><td>100.0</td><td>3.9E+14</td></tr><tr><td>Soil (Noss)</td><td>4.2</td><td>2242</td><td>5</td><td>43.6</td><td>0.8</td><td>10.1</td><td>19.8</td><td>7.1</td><td>7.0</td><td>2.2</td><td>0.5</td><td>91.1</td><td>8.25E+13</td></tr><tr><td>Soil (Loch Monar)</td><td>2.3</td><td>2291</td><td>5</td><td>41.1</td><td>0.8</td><td>8.0</td><td>19.1</td><td>8.2</td><td>7.1</td><td>1.6</td><td>0.2</td><td>86.2</td><td>2.48E+14</td></tr></tbody></table>

Abundances are given in wt. %. Tot. Emission is the sum of photons emitted from the target within ChemCam's spectral range.

1057

1058

<table><caption>Table 4. Standard deviations for targets observed at two different distances.</caption><thead><tr><th>Target</th><th>Dist. (m)</th><th>SiO<sub>2</sub></th><th>TiO<sub>2</sub></th><th>Al<sub>2</sub>O<sub>3</sub></th><th>FeO<sub>T</sub></th><th>MgO</th><th>CaO</th><th>Na<sub>2</sub>O</th><th>K<sub>2</sub>O</th><th>Sum</th><th>Tot. Emission</th></tr></thead><tbody><tr><td>Bonnet Plume</td><td>6.2</td><td>1.7</td><td>1.9</td><td>2.0</td><td>1.7</td><td>1.0</td><td>0.7</td><td>0.4</td><td>0.2</td><td>3.2</td><td>9.3E+13</td></tr><tr><td>Bonnet Plume</td><td>3.5</td><td>1.8</td><td>0.1</td><td>1.5</td><td>0.6</td><td>1.7</td><td>1.2</td><td>0.4</td><td>0.1</td><td>2.5</td><td>2.3E+14</td></tr><tr><td>Hallaig</td><td>4.5</td><td>0.8</td><td>0.1</td><td>0.8</td><td>0.7</td><td>0.2</td><td>0.3</td><td>0.2</td><td>0.2</td><td>0.7</td><td>4.4E+13</td></tr><tr><td>Hallaig</td><td>2.4</td><td>1.2</td><td>0.1</td><td>0.4</td><td>1.3</td><td>0.2</td><td>0.5</td><td>0.2</td><td>0.2</td><td>0.8</td><td>7.7E+13</td></tr><tr><td>Glen Lyon</td><td>4.0</td><td>1.2</td><td>0.1</td><td>0.8</td><td>0.6</td><td>0.5</td><td>0.2</td><td>0.3</td><td>0.3</td><td>1.2</td><td>3.4E+13</td></tr><tr><td>Glen Lyon</td><td>2.7</td><td>0.4</td><td>0.1</td><td>0.7</td><td>0.3</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.1</td><td>1.1</td><td>2.6E+13</td></tr><tr><td>Soil (Noss)</td><td>4.2</td><td>1.9</td><td>0.1</td><td>0.8</td><td>0.5</td><td>0.5</td><td>0.3</td><td>0.2</td><td>0.1</td><td>3.3</td><td>1.9E+13</td></tr><tr><td>Soil (Loch Monar)</td><td>2.3</td><td>0.6</td><td>0.1</td><td>0.6</td><td>0.4</td><td>0.4</td><td>0.2</td><td>0.2</td><td>0.1</td><td>1.6</td><td>4.1E+13</td></tr></tbody></table>

1060

1061

1062

Abundances are given in wt. %. Tot. Emission is defined in Table 3.
1063

<table><caption>Table 5. ChemCam LIBS targets observed at distances > 6.0 m.</caption><thead><tr><th>Sol</th><th>Target</th><th>Dist. (m)</th><th>Total Emission</th><th># Pts</th><th>SiO<sub>2</sub></th><th>TiO<sub>2</sub></th><th>Al<sub>2</sub>O<sub>3</sub></th><th>FeO<sub>T</sub></th><th>MgO</th><th>CaO</th><th>Na<sub>2</sub>O</th><th>K<sub>2</sub>O</th><th>Sum</th></tr></thead><tbody><tr><td>171</td><td>Bonnet Plume</td><td>6.22</td><td>1.6E+14</td><td>6</td><td>48.6</td><td>1.7</td><td>16.3</td><td>17.3</td><td>7.0</td><td>7.7</td><td>3.3</td><td>0.6</td><td>102.5</td></tr><tr><td>283</td><td>Long Island</td><td>6.39</td><td>2.4E+14</td><td>1</td><td>41.8</td><td>0.9</td><td>11.9</td><td>21.7</td><td>6.2</td><td>8.7</td><td>2.7</td><td>0.4</td><td>94.4</td></tr><tr><td>312</td><td>Wishart</td><td>6.40</td><td>2.2E+14</td><td>4</td><td>45.5</td><td>1.0</td><td>11.6</td><td>23.4</td><td>7.9</td><td>4.8</td><td>2.7</td><td>0.8</td><td>97.6</td></tr><tr><td>316</td><td>Mary River</td><td>6.20</td><td>3.1E+14</td><td>9</td><td>44.0</td><td>1.2</td><td>15.5</td><td>23.2</td><td>6.7</td><td>5.8</td><td>3.6</td><td>1.9</td><td>101.9</td></tr><tr><td>322</td><td>Husky Creek</td><td>6.23</td><td>5.9E+13</td><td>1</td><td>43.8</td><td>3.6</td><td>11.1</td><td>24.3</td><td>3.0</td><td>3.2</td><td>3.6</td><td>2.0</td><td>94.8</td></tr><tr><td>339</td><td>Red Wine</td><td>6.61</td><td>8.2E+13</td><td>2</td><td>53.9</td><td>0.8</td><td>26.8</td><td>3.3</td><td>0.4</td><td>6.3</td><td>6.3</td><td>1.5</td><td>99.1</td></tr><tr><td>401</td><td>McKinnon</td><td>6.07</td><td>1.4E+14</td><td>5</td><td>46.6</td><td>0.9</td><td>14.1</td><td>22.3</td><td>3.9</td><td>4.7</td><td>3.9</td><td>2.0</td><td>98.2</td></tr><tr><td>530</td><td>Mell</td><td>7.45</td><td>6.6E+13</td><td>4</td><td>35.6</td><td>0.9</td><td>10.1</td><td>27.8</td><td>6.2</td><td>3.9</td><td>2.1</td><td>0.8</td><td>87.2</td></tr><tr><td>861</td><td>Last Chance Canyon</td><td>6.51</td><td>3.9E+14</td><td>1</td><td>49.0</td><td>0.9</td><td>19.7</td><td>16.6</td><td>3.3</td><td>5.8</td><td>5.4</td><td>1.6</td><td>102.3</td></tr><tr><td>1238</td><td>Erongo</td><td>6.57</td><td>2.2E+14</td><td>6</td><td>48.6</td><td>0.8</td><td>17.5</td><td>21.9</td><td>3.0</td><td>3.7</td><td>3.8</td><td>2.3</td><td>101.7</td></tr><tr><td>2361</td><td>Eorsa</td><td>6.46</td><td>2.4E+14</td><td>5</td><td>43.9</td><td>0.9</td><td>15.5</td><td>24.3</td><td>7.5</td><td>3.2</td><td>3.6</td><td>1.6</td><td>100.5</td></tr><tr><td>2172</td><td>Stoneyburn</td><td>8.30</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2367</td><td>Lumphanan</td><td>9.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2550</td><td>Monach Isles 2</td><td>8.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2559</td><td>Pladda Isle</td><td>7.0</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

1064

1065

1066

1067

Elemental abundances and their sums are in wt. %. Total emission is in photons emitted at the target. The last four targets are iron meteorites for which compositions are outside of the calibration range of ChemCam.
<table><caption>Table 6. Strongest emission lines for the four elements showing distance trends, ordered by E<sub>k</sub>.</caption><thead><tr><th rowspan="2">Element</th><th rowspan="2">State</th><th rowspan="2">λ (nm)</th><th rowspan="2">E<sub>k</sub> (eV)</th><th colspan="2">Major line for</th></tr><tr><th>PLS</th><th>ICA</th></tr></thead><tbody><tr><td>K</td><td>I</td><td>766.701</td><td>1.6</td><td>Y</td><td>Y</td></tr><tr><td>K</td><td>I</td><td>770.108</td><td>1.6</td><td>Y</td><td>Y</td></tr><tr><td>Na</td><td>I</td><td>589.158</td><td>2.1</td><td>Y</td><td>Y</td></tr><tr><td>Na</td><td>I</td><td>589.756</td><td>2.1</td><td>Y</td><td>Y</td></tr><tr><td>Al</td><td>I</td><td>394.512</td><td>3.1</td><td>Y</td><td>Y</td></tr><tr><td>Al</td><td>I</td><td>396.264</td><td>3.1</td><td>Y</td><td>Y</td></tr><tr><td>Na</td><td>I</td><td>819.704</td><td>3.6</td><td>Y</td><td>Y</td></tr><tr><td>Na</td><td>I</td><td>819.708</td><td>3.6</td><td>Y</td><td>Y</td></tr><tr><td>Al</td><td>I</td><td>308.305</td><td>4.0</td><td>Y</td><td>Y</td></tr><tr><td>Al</td><td>I</td><td>309.361</td><td>4.0</td><td>Y</td><td>Y</td></tr><tr><td>Si</td><td>I</td><td>251.996</td><td>4.9</td><td>Y</td><td>Y</td></tr><tr><td>Si</td><td>I</td><td>252.487</td><td>4.9</td><td>Y</td><td>Y</td></tr><tr><td>Si</td><td>I</td><td>252.927</td><td>4.9</td><td>Y</td><td>Y</td></tr><tr><td>Si</td><td>I</td><td>250.766</td><td>5.0</td><td>Y</td><td>Y</td></tr><tr><td>Si</td><td>I</td><td>251.508</td><td>5.0</td><td>Y</td><td>Y</td></tr><tr><td>Si</td><td>I</td><td>288.242</td><td>5.1</td><td>Y</td><td>Y</td></tr><tr><td>Si</td><td>II</td><td>634.885</td><td>10.1</td><td>Y</td><td>Y</td></tr><tr><td>Si</td><td>II</td><td>637.312</td><td>10.1</td><td>Y</td><td>Y</td></tr><tr><td>Al</td><td>II</td><td>281.701</td><td>11.8</td><td>Y</td><td>N</td></tr></tbody></table>

1069 E<sub>k</sub> gives the upper energy level of the transition

1070 Y indicates the emission line is used in the algorithm

1071 Data obtained from the NIST atomic emission database [35]

1072

1073

1074

1075
1076

1077

<table><caption>Table 7. Linear fit characteristics of data from the Kimberley Formation</caption><thead><tr><th>N = 231</th><th>Slope</th><th>R<sup>2</sup></th><th>95% UB</th><th>95% LB</th><th>X-Intcpt</th></tr></thead><tbody><tr><td>SiO<sub>2</sub></td><td>1.56</td><td>0.27</td><td>1.23</td><td>1.90</td><td>-25.6</td></tr><tr><td>TiO<sub>2</sub></td><td>0.03</td><td>0.03</td><td>0.01</td><td>0.05</td><td>-25.5</td></tr><tr><td>Al<sub>2</sub>O<sub>3</sub></td><td>1.46</td><td>0.55</td><td>1.29</td><td>1.63</td><td>-2.7</td></tr><tr><td>FeO<sub>T</sub></td><td>0.27</td><td>0.02</td><td>0.01</td><td>0.53</td><td>-67.1</td></tr><tr><td>MgO</td><td>-0.12</td><td>0.01</td><td>-0.31</td><td>0.08</td><td>80.5</td></tr><tr><td>CaO</td><td>0.35</td><td>0.10</td><td>0.22</td><td>0.49</td><td>-11.1</td></tr><tr><td>Na<sub>2</sub>O</td><td>0.24</td><td>0.12</td><td>0.16</td><td>0.32</td><td>-3.1</td></tr><tr><td>K<sub>2</sub>O</td><td>0.86</td><td>0.40</td><td>0.72</td><td>1.00</td><td>-0.5</td></tr><tr><td>Oxide Sum</td><td>4.66</td><td>0.72</td><td>4.28</td><td>5.04</td><td>-16.6</td></tr></tbody></table>

1078

1079

Abbreviations are the same as in Table 1.
<table><caption>Table 8. Reported and corrected abundances (in wt. %) for targets observed at two distances.</caption><thead><tr><th>Target</th><th>Dist. (m)</th><th>SiO<sub>2</sub></th><th>Al<sub>2</sub>O<sub>3</sub></th><th>Na<sub>2</sub>O</th><th>K<sub>2</sub>O</th></tr></thead><tbody><tr><td>Bonnet Plume</td><td>6.2</td><td>48.6±1.7</td><td>16.3±2.0</td><td>3.3±0.4</td><td>0.59±0.17</td></tr><tr><td>Bonnet Plume</td><td>3.5</td><td>47.9±1.8</td><td>10.8±1.5</td><td>3.0±0.4</td><td>0.56±0.14</td></tr><tr><td>Correction</td><td>6.2→3.5</td><td>45.5±1.8</td><td>11.3±2.0</td><td>2.3±0.4</td><td>0.35±0.17</td></tr><tr><td>Hallaig</td><td>4.5</td><td>57.2±0.8</td><td>15.8±0.8</td><td>3.5±0.2</td><td>2.6±0.2</td></tr><tr><td>Hallaig</td><td>2.4</td><td>54.1±1.2</td><td>11.0±0.4</td><td>2.3±0.2</td><td>1.3±0.2</td></tr><tr><td>Correction</td><td>4.5→2.4</td><td>54.1±0.9</td><td>11.0±0.8</td><td>2.5±0.2</td><td>1.5±0.2</td></tr><tr><td>Glen Lyon</td><td>4.0</td><td>57.7±1.2</td><td>14.3±0.8</td><td>3.0±0.3</td><td>3.1±0.3</td></tr><tr><td>Glen Lyon</td><td>2.7</td><td>56.0±0.4</td><td>11.5±0.7</td><td>2.4±0.2</td><td>2.1±0.1</td></tr><tr><td>Correction</td><td>4.0→2.7</td><td>55.9±1.2</td><td>11.6±0.8</td><td>2.5±0.3</td><td>2.2±0.3</td></tr><tr><td>Soil (Noss)</td><td>4.2</td><td>43.6±1.9</td><td>10.1±0.5</td><td>2.2±0.2</td><td>0.51±0.09</td></tr><tr><td>Soil (Loch Monar)</td><td>2.3</td><td>41.1±0.6</td><td>8.0±0.4</td><td>1.6±0.2</td><td>0.21±0.05</td></tr><tr><td>Correction</td><td>4.2→2.3</td><td>41.2±2.0</td><td>7.3±0.8</td><td>1.6±0.2</td><td>0.31±0.09</td></tr></tbody></table>

1081 Correction uses the data collected at the longer distance to provide an estimate for the abundances at the shorter distance.

1082 Errors are the standard deviations of the mean of the abundances of the individual observation points associated with the target; for the corrections, the error of the correction is convolved into the error of the original measurement.

1083
1084
1085
1086
1087
1088
1089
1090
1091

9. Figures

<figure><img src="image_2.png" /></figure>

1092

<figure><img src="image_3.png" /></figure>

1093
1094
1095
1096
1097
1098

<caption>Figure 1. Comparison of the ultraviolet spectral range of the LIBS spectra from two Murray formation targets at long and short distances. The raw data have been dark-subtracted and de-noised, and the continuum has been removed. The same spectral portions are shown in linear (A) and logarithmic (B) representation of the intensity in digital numbers (DN). Each spectral portion is the mean of all five observation points (with 30 laser shots each) on its respective target.</caption>
<figure><img src="image_4.png" /><figcaption>Figure 2. (A) Overhead view of the region of Gale crater traversed by the *Curiosity* rover through Sol 2640. A star marks the start of the traverse in 2012. Regions mentioned from early in the traverse are marked in blue. Targets mentioned in the text are marked in white with their name or letters denoting their name: BP = Bonnet Plume; LI = Long Island; W = Wishart; HC = Husky Creek; MR = Mary River. (B) Enlargement of the shaded region in (A) showing the locations of the Murray formation bedrock targets used for the distance correction analysis. Image is based on Fig. 1 of Frydenvang et al. [8] and on a mosaic of HiRISE images of Gale crater, Mars; see Calef and Parker [22] for details.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Distance (m)</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 3. Abundances of (A) Al₂O₃, as an example of an element with a distance trend, and (B) CaO, as an example of an element with no distance trend, as functions of distance for all ChemCam bedrock observation points in 11 classes of targets of the Murray formation. Linear least-squares regression fits and 95% confidence levels are also plotted, as are error bars indicating the accuracy (thin bars) and precision (thick bars) of the technique. Other elements are plotted in the Supplemental Material.</figcaption></figure>

1115
1116
1117
1118
1119
1120
1121
1122
1123
<figure><img src="image_7.png" /><figcaption>Figure 4. Slopes of abundance trends vs. distance for each Murray class, plotted in chronological order. Error bars represent the 95% confidence interval of the slopes.</figcaption></figure>

1124

1125

1126

1127

1128
<figure><img src="image_8.png" /><figcaption>Figure 5. Slopes of elemental abundance trends vs. distance, plotted as functions of R².</figcaption></figure>

1129
1130
1131
1132
1133
1134

1135
1136
1137
1138
1139
1140
<figure><img src="image_9.png" /><figcaption>Figure 6. Abundances vs. distance for bedrock observations at the Windjana location in the Kimberley formation. Accuracy for each observation is shown as a narrow vertical line; precision as a broad vertical line. A red line indicates the linear fit, with dashed lines showing the 95% confidence levels.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 7. Conceptual illustration of three elements, shown in separate panels A, B, and C, which have different distributions of upper energy levels for their strongest emission lines. Element A has moderate upper energy levels. At longer distances, the strongest emission peaks of Element A decrease in intensity at the same rate as most of the other peaks in the spectrum, with the result that there is no abundance trend with distance. This is true whether the element has a high abundance or a low abundance, as shown by the flat trends through red squares and blue circles. Element B has somewhat low upper energy levels for its strongest peaks, so its normalized peak areas increase with distance, resulting in positive slopes for the abundance trends as functions of distance. Element C has the lowest upper energy levels and shows strong trends with distance. In all three cases, an abundance of zero at the nominal distance will result in a slope of 0 and a line along the x-axis, indicated at the bottom of Panel C. For any elements with distance trends (e.g., Elements B and C in this illustration), the slope increases with increasing abundance. For element A, the x-intercept is at negative infinity, as the trends are parallel. For Element B, the x-intercept is far from the measured values, while for Element C, the x-intercept is quite near. These x-intercepts are related to the ratio of the upper energy levels for the strong emission lines of the element in question relative to the ensemble of upper energy levels of the peaks to which they are normalized.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure 8. Linear trends of the Murray formation classes with the three highest R² values, along with the linear trends from the Kimberley formation. Solid lines indicate the distance range over which data exist; dashed lines indicate extrapolations. X-intercepts are given in Tables 2 and 7.</figcaption></figure>

1174
1175
1176
1177
1178
1179
1180
1181
<figure><img src="image_12.png" /><figcaption>Figure 9. Uncorrected and corrected targets Mahony, Mahony_2, and San Sou from the Mt.</figcaption></figure>

1183

1184

1185 Remarkable member of the Kimberley formation, which were at > 5 m distance, in comparison

1187 with the mean and standard deviation of the Dillinger member.