From ea852dba7d1e6fd346832be91f835fdb8711f477 Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Mon, 5 Feb 2024 13:52:52 +0100 Subject: [PATCH 01/13] WIP --- p9-presentation/sections/_introduction.qmd | 62 +++++++++++++++++- p9-presentation/static/introduction/msl.jpg | Bin 0 -> 192807 bytes .../static/introduction/viking-lander.jpg | Bin 0 -> 333062 bytes 3 files changed, 61 insertions(+), 1 deletion(-) create mode 100644 p9-presentation/static/introduction/msl.jpg create mode 100644 p9-presentation/static/introduction/viking-lander.jpg diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index 79dc7208..0de98dce 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -1 +1,61 @@ -Presenter: Patrick Frostholm Østergaard. \ No newline at end of file +Presenter: Patrick Frostholm Østergaard. + +## The Early Days of Mars Exploration {.smaller transition="slide-in fade-out"} + + +::::: {.columns style="display: flex !important; height: 90%;"} + +:::: {.column width="50%"} + +::: {.fragment .fade-up fragment-index=1} +- 1970s: Viking missions + - *"Is there life on Mars?"* +::: + +::: {.fragment .fade-up} +- Inconclusive results... +::: + +:::: + +:::: {.column width="50%" style="display: flex; justify-content: center; align-items: center;"} + +::: {.fragment .fade-up fragment-index=1} +![Viking Lander](static/introduction/viking-lander.jpg) +::: + +:::: + +::::: + +::: {.notes} +In the 1970s, the Viking missions were the first to land on Mars. +Goal: Is there life on Mars? + +Experiments showed positive results, but the results were inconclusive and were not repeated due to budget constraints. +::: + +## The Early Days of Mars Exploration {.smaller transition="fade-in slide-out"} +- 1990s: Philosophical shift + - *"Did Mars ever have the conditions to support life as we know it?"* + +::: {.fragment .fade-up} +- August 2012: Mars Science Laboratory (MSL) mission + - Curiosity rover + +![](static/introduction/msl.jpg){height=50% width=50% fig-align="center"} +::: + + +::: {.notes} +1990s: Philosophical shift in the scientific community. +The focus shifted from "Is there life on Mars?" to "Did Mars ever have the conditions to support life as we know it?" + +August 2012: The Mars Science Laboratory (MSL) mission lands the Curiosity rover on Mars. +::: + +## Problem Definition + +## Related Work + +## Data Overview \ No newline at end of file diff --git a/p9-presentation/static/introduction/msl.jpg b/p9-presentation/static/introduction/msl.jpg new file mode 100644 index 0000000000000000000000000000000000000000..3c3d6609b758d6ccf388bb8673fad504b120f72a GIT binary patch literal 192807 zcmb6BcU)5c`v#65KpeOg4fj@5G&P*5A?CtLoZ+Y(4cuuqw1u<8l{2%{gNmVLsi|36 znToR%N2Lub&CJZo)qd6QN$=11@q0YJ|9uaq)8Q-*a6hm6x~}`izmtDoLsArHH)jZe zKtOxJ1N!?Ma)i(*0RaIN8Z6Lgw4jifun?FeMMXu#q$Oo!q$Q=LW#yGO$YK@cq@{5h zI7Jl|H8nNa4Vv1Ts@lq`YO4H;Ai$|Yfr7+mLO$tIHP)L*@|A9uL5h7qGBp@xLfi`szl-2YM 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zhC-)^#H@?5cOj(DM))0wWwflFn|6)iJEciZpGkOD1FG!C6Sb4$Z8BCgqH0IU?^Evw zbnY&$D)YV zB-KqvN7DHRWA7kVdxAzcfW1$=LS=!P<8>gjypY3;*&9rIjq0}H<@Hy{iM<|>(49GT zuMO6TN`!EBd41+Q(HwWPcQW!Q<$G3qhK;rJ)IbIme4H2ZZAVlwlKqnYRm6hA{%j?D_ybd~Xt9mvFxR9WyZLZDmiMnSGZ^-jMc z-+7Bgc_C0enO=sC!krNGE6U+Hi>2uVhkBOOglI+~fj{?y5Xr}RsDdj~w#?Kw$+;+F zoBtRjs{h=sOLcm~PBND=d-^$+Sj{k%KxPWI z!_ka)yN~6Dol>!5R%M-}g*jn( Date: Mon, 5 Feb 2024 14:07:06 +0100 Subject: [PATCH 02/13] Update _introduction.qmd --- p9-presentation/sections/_introduction.qmd | 49 +++++++++++++--------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index 0de98dce..25bfc430 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -3,7 +3,7 @@ Presenter: Patrick Frostholm Østergaard. ## The Early Days of Mars Exploration {.smaller transition="slide-in fade-out"} -::::: {.columns style="display: flex !important; height: 90%;"} +::::: {.columns} :::: {.column width="50%"} @@ -12,16 +12,35 @@ Presenter: Patrick Frostholm Østergaard. - *"Is there life on Mars?"* ::: -::: {.fragment .fade-up} -- Inconclusive results... +::: {.fragment .fade-up fragment-index=2} +- Positive but inconclusive results... ::: +::: {.fragment .fade-up fragment-index=3} +- 1990s: Philosophical shift + - *"Did Mars ever have the conditions to support life as we know it?"* +::: + +::: {.fragment .fade-up fragment-index=4} +- August 2012: Mars Science Laboratory (MSL) mission + - Curiosity rover +::: + +::: {.fragment .fade-up fragment-index=5} +- ChemCam: **L**aser-**i**nduced **b**reakdown **s**pectroscopy (*LIBS*) instrument +::: + + :::: -:::: {.column width="50%" style="display: flex; justify-content: center; align-items: center;"} +:::: {.column width="50%"} ::: {.fragment .fade-up fragment-index=1} -![Viking Lander](static/introduction/viking-lander.jpg) +![Viking Lander](static/introduction/viking-lander.jpg){width="60%" height="60%"} +::: + +::: {.fragment .fade-up fragment-index=4} +![Mars Science Laboratory](static/introduction/msl.jpg){width="60%" height="60%"} ::: :::: @@ -33,27 +52,19 @@ In the 1970s, the Viking missions were the first to land on Mars. Goal: Is there life on Mars? Experiments showed positive results, but the results were inconclusive and were not repeated due to budget constraints. -::: - -## The Early Days of Mars Exploration {.smaller transition="fade-in slide-out"} -- 1990s: Philosophical shift - - *"Did Mars ever have the conditions to support life as we know it?"* - -::: {.fragment .fade-up} -- August 2012: Mars Science Laboratory (MSL) mission - - Curiosity rover -![](static/introduction/msl.jpg){height=50% width=50% fig-align="center"} -::: - - -::: {.notes} 1990s: Philosophical shift in the scientific community. The focus shifted from "Is there life on Mars?" to "Did Mars ever have the conditions to support life as we know it?" August 2012: The Mars Science Laboratory (MSL) mission lands the Curiosity rover on Mars. +Rover is equipped with a suite of instruments to study the Martian climate and geology to search for organic material. + +One of the instruments is the Chemistry and Camera (ChemCam) instrument, a laser-induced breakdown spectroscopy (LIBS) instrument that can analyze the elemental composition of rocks and soil from a distance. +ChemCam is used to study the composition of the Martian surface and to search for organic material. +This requires the development of machine learning algorithms to analyze the data, which is the focus of this thesis. ::: + ## Problem Definition ## Related Work From 088dd1dd4265cb4a147e960bfdde4628828b5f43 Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Mon, 5 Feb 2024 14:18:46 +0100 Subject: [PATCH 03/13] Update _introduction.qmd --- p9-presentation/sections/_introduction.qmd | 23 ++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index 25bfc430..833e2757 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -13,16 +13,17 @@ Presenter: Patrick Frostholm Østergaard. ::: ::: {.fragment .fade-up fragment-index=2} -- Positive but inconclusive results... +- 1990s: Philosophical shift + - *"Did Mars ever have the conditions to support life as we know it?"* ::: ::: {.fragment .fade-up fragment-index=3} -- 1990s: Philosophical shift - - *"Did Mars ever have the conditions to support life as we know it?"* +- 2004: Mars Exploration Rover mission + - Discovered evidence of water ::: ::: {.fragment .fade-up fragment-index=4} -- August 2012: Mars Science Laboratory (MSL) mission +- August 2012: Mars Science Laboratory - Curiosity rover ::: @@ -53,17 +54,19 @@ Goal: Is there life on Mars? Experiments showed positive results, but the results were inconclusive and were not repeated due to budget constraints. -1990s: Philosophical shift in the scientific community. -The focus shifted from "Is there life on Mars?" to "Did Mars ever have the conditions to support life as we know it?" +1990s: Philosophical shift within NASA: "Is there life on Mars?" to "Did Mars ever have the conditions to support life as we know it?" +In 2004, the Mars Exploration Rover mission landed the rovers Spirit and Opportunity on Mars, which quickly discovered evidence of water on Mars. -August 2012: The Mars Science Laboratory (MSL) mission lands the Curiosity rover on Mars. -Rover is equipped with a suite of instruments to study the Martian climate and geology to search for organic material. +A few years later in 2012, the Mars Science Laboratory (MSL) mission landed the Curiosity rover on Mars. +Rover is equipped with a suite of instruments to study the Martian climate and geology and to search for organic material. One of the instruments is the Chemistry and Camera (ChemCam) instrument, a laser-induced breakdown spectroscopy (LIBS) instrument that can analyze the elemental composition of rocks and soil from a distance. ChemCam is used to study the composition of the Martian surface and to search for organic material. -This requires the development of machine learning algorithms to analyze the data, which is the focus of this thesis. -::: +This requires the development of machine learning models to analyze the data, which is the focus of this presentation. +NASA has made most of the ChemCam calibration data publicly available, and they have also published a few papers on the machine learning models they have developed for ChemCam. +Our goal is to replicate and improve upon their work. +::: ## Problem Definition From c78321287421fa9b3297b3abf2e87173883c29b5 Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Mon, 5 Feb 2024 14:23:41 +0100 Subject: [PATCH 04/13] Update _introduction.qmd --- p9-presentation/sections/_introduction.qmd | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index 833e2757..67de76a2 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -1,7 +1,7 @@ Presenter: Patrick Frostholm Østergaard. -## The Early Days of Mars Exploration {.smaller transition="slide-in fade-out"} +## A Brief History {.smaller} ::::: {.columns} @@ -31,7 +31,6 @@ Presenter: Patrick Frostholm Østergaard. - ChemCam: **L**aser-**i**nduced **b**reakdown **s**pectroscopy (*LIBS*) instrument ::: - :::: :::: {.column width="50%"} @@ -68,7 +67,9 @@ NASA has made most of the ChemCam calibration data publicly available, and they Our goal is to replicate and improve upon their work. ::: -## Problem Definition + +## Problem Definition {.smaller} +*"In this work, we aim to solve the problem of identifying issues within the components of the current Multivariate Oxide Composition (MOC) model that limit its predictive accuracy and robustness. Following this, we will propose improvements to the model’s components that addresses these issues, thereby enhancing its overall accuracy and robustness."* ## Related Work From 742c3fcaa836e99d70c040231ce82b7e0eed2ba2 Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Mon, 5 Feb 2024 15:23:51 +0100 Subject: [PATCH 05/13] WIP --- p9-presentation/sections/_introduction.qmd | 59 ++++++++++++++++-- p9-presentation/static/introduction/msl.jpg | Bin 192807 -> 229827 bytes .../static/introduction/pipeline.png | Bin 0 -> 32398 bytes 3 files changed, 55 insertions(+), 4 deletions(-) create mode 100644 p9-presentation/static/introduction/pipeline.png diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index 67de76a2..af24741a 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -40,7 +40,7 @@ Presenter: Patrick Frostholm Østergaard. ::: ::: {.fragment .fade-up fragment-index=4} -![Mars Science Laboratory](static/introduction/msl.jpg){width="60%" height="60%"} +![Mars Science Laboratory Curiosity Rover](static/introduction/msl.jpg){width="75%" height="75%"} ::: :::: @@ -64,13 +64,64 @@ ChemCam is used to study the composition of the Martian surface and to search fo This requires the development of machine learning models to analyze the data, which is the focus of this presentation. NASA has made most of the ChemCam calibration data publicly available, and they have also published a few papers on the machine learning models they have developed for ChemCam. -Our goal is to replicate and improve upon their work. +We have worked with one of the authors of these papers, Jens Frydenvang, who asserted that the models are far from perfect and that there is room for improvement. +Following this, we have decided to replicate and improve upon their work. ::: ## Problem Definition {.smaller} -*"In this work, we aim to solve the problem of identifying issues within the components of the current Multivariate Oxide Composition (MOC) model that limit its predictive accuracy and robustness. Following this, we will propose improvements to the model’s components that addresses these issues, thereby enhancing its overall accuracy and robustness."* + +::::: {.columns style='display: flex !important; height: 70%;'} + +::: {.column width="50%"} + +::: {.fragment .fade-up fragment-index=1} +- Current model: **M**ultivariate **O**xide **C**omposition (*MOC*) +::: + +::: {.fragment .fade-up} +- Predicts composition from **C**lean, **c**alibrated **s**pectra (*CCS*) +::: + +::: {.fragment .fade-up} +- **Goal**: Identify components contributing most to error $E(M)$ as defined by RMSE +::: + +::: {.fragment .fade-up} +- **Method**: Replicate MOC model and perform experiments +::: + +::: + +::: {.column width="50%" style='display: flex; justify-content: center; align-items: center;'} +::: {.fragment .fade-up fragment-index=1} +![](static/introduction/pipeline.png){width="65%" height="65%"} +::: +::: + +:::: + +::: {.fragment .fade-up} +::: {style="border: 2px solid black; padding: 0px 15px;"} +**Problem**: Given a series of experiments and the resulting models, identify the components that acontribute the most to the overall error $E(M)$. +::: +::: + +::: {.notes} +The model currently used by the ChemCam team is the Multivariate Oxide Composition (MOC) model, illustrated in the figure on the right. +The MOC model is trained on a calibration dataset of clean, calibrated spectra (CCS), which I will give an overview of in a moment. +When a new spectrum is collected on Mars, it is first preprocessed into the CCS format and then fed into the MOC model to predict the composition of the sample. +Christian will elaborate on how exactly the MOC model works later in the presentation. + +Our goal is to replicate the MOC model and perform experiments to identify the components that contribute the most to the overall error $E(M)$, as defined by the root mean squared error (RMSE). +We do this through a series of experiments, which Ivik will explain in more detail later in the presentation. +By doing this, we hope to gain a better understanding of the MOC model and to identify areas where it can be improved. + +This is the focus of our pre-thesis project, and we will present our findings in this presentation and talk about our plans for the thesis project next semester. +::: + ## Related Work -## Data Overview \ No newline at end of file +## Data Overview +- 8 major oxides: $\text{SiO}_2$, $\text{TiO}_2$, $\text{Al}_2\text{O}_3$, $\text{Fe}_2\text{O}_3$, $\text{MgO}$, $\text{CaO}$, $\text{Na}_2\text{O}$, $\text{K}_2\text{O}$ \ No newline at end of file diff --git a/p9-presentation/static/introduction/msl.jpg b/p9-presentation/static/introduction/msl.jpg index 3c3d6609b758d6ccf388bb8673fad504b120f72a..d96c26d5e7a999194fe4f1083d0686f15f31ea7c 100644 GIT binary patch literal 229827 zcmbTd2Q*ym+b=vbj1e`&Ac&rj(R+z9dS@`Ygy_ACUWSkmT}bqaK59gd2#E+HY7jl5 zmmqrQ+n(qBzvuhbde`~ZIeS}dd*A))*ZsS%#;@i;H`SHZltExH7^Di^psU{?S|xu+ zI}k`s4FMtq{jcllH=etLkB^5WAD^2y?_(QxD_dS`cUM0D#~yqFy!?D1DOrDy$JWla zKFn6O_Kt4StlKSZtjvx!(yT@zTKrlb3bqc8s)1g%`hnVL>p*8~2^&^f8D=SeNq<)l zS6iRQ%>J$}Zr+mq(yae9E(ttezvg3Q{-=nKvox#Rb+63ES}0}(cQ0FJ5nfRqYXM;q zW-$p~0Z}1g2?=gyL4E;oK7MgN0U;g%QAuHbNkKv8|GHR#-n?w=B=r=P|En+HKWWzg znv|cPAFrPfue+B$pMZpf1RuX3pP(QQP=d!hz|H5eKaZO?+kaY6wDq?3a`fUuq|e@c7%=-K|SVf>G!z0m<4wtRZF-tN9$ z*0w-#w*OQH*6x4b(RD#!G?F@AjsQ^}yC}L_`?}h?`KT#MvjSguZ5(YR`9*~J`4yDp zgas7Dltcss;Aw6X4_H0Rtcak%J+4AbbiaC5r&TEmk?*$1o~EPd0f>vfi5- zxRBMSAwqTquiw-WDSvlp?h5OB=Zh#>zZ3O|O#M8JHn?Z=5@?g|y1VP^zq`Ed1LzX? zCPV=SL9Q<_1PTQKUEzVi5DH2>)?0%3@_K9nR4j6jt)N1>o?Z&c`j`mzPeWIeAQC*F z5(ORwNEWopwJZ%)Ort`umlC@|@}Fmd84)-5IpI`jA`D7~lbVwegJR~KW`dV}+JIC7 z?|AB<$Wk~2d;J~p|9M1?LGslxhNaI&joE>RJ}UX}U4%y}Q4v`o;!AA35>O3q8M6v! 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+0100 Subject: [PATCH 06/13] Data overview --- p9-presentation/sections/_introduction.qmd | 102 +++++++++++++++++- .../static/introduction/folder-structure.png | Bin 0 -> 31582 bytes .../static/introduction/oxide_corr.png | Bin 0 -> 65770 bytes .../static/introduction/spectrum.png | Bin 0 -> 165959 bytes 4 files changed, 99 insertions(+), 3 deletions(-) create mode 100644 p9-presentation/static/introduction/folder-structure.png create mode 100644 p9-presentation/static/introduction/oxide_corr.png create mode 100644 p9-presentation/static/introduction/spectrum.png diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index af24741a..d76c9b36 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -109,19 +109,115 @@ Following this, we have decided to replicate and improve upon their work. ::: {.notes} The model currently used by the ChemCam team is the Multivariate Oxide Composition (MOC) model, illustrated in the figure on the right. + The MOC model is trained on a calibration dataset of clean, calibrated spectra (CCS), which I will give an overview of in a moment. When a new spectrum is collected on Mars, it is first preprocessed into the CCS format and then fed into the MOC model to predict the composition of the sample. Christian will elaborate on how exactly the MOC model works later in the presentation. Our goal is to replicate the MOC model and perform experiments to identify the components that contribute the most to the overall error $E(M)$, as defined by the root mean squared error (RMSE). We do this through a series of experiments, which Ivik will explain in more detail later in the presentation. -By doing this, we hope to gain a better understanding of the MOC model and to identify areas where it can be improved. +By doing this, we hope to gain a better understanding of the MOC model and to identify areas where it can be improved. This is the focus of our pre-thesis project, and we will present our findings in this presentation and talk about our plans for the thesis project next semester. ::: ## Related Work -## Data Overview -- 8 major oxides: $\text{SiO}_2$, $\text{TiO}_2$, $\text{Al}_2\text{O}_3$, $\text{Fe}_2\text{O}_3$, $\text{MgO}$, $\text{CaO}$, $\text{Na}_2\text{O}$, $\text{K}_2\text{O}$ \ No newline at end of file +## Calibration Data {.smaller} + +::::: {.columns} + +::: {.column width="50%"} + +::: {.fragment .fade-up fragment-index=1} +- 408 pressed powder samples +- 5 locations per sample +- 50 shots location +::: + +::: {.fragment .fade-up fragment-index=2} +- CCS: 3 spectrometers +::: +::: {.fragment .fade-up} +- Masked regions: + - 240.811 — 246.635 nm + - 338.457 — 340.797 nm + - 382.138 — 387.859 nm + - 473.184 — 492.427 nm + - 849 — 905.574 nm +::: + +::: + +::: {.column width="50%"} + +::: {.fragment .fade-up fragment-index=1} +![](static/introduction/folder-structure.png){width="35%" height="35%" fig-align="center"} +::: + +::: {.fragment .fade-up fragment-index=2} +![](static/introduction/spectrum.png) +::: + +::: + +:::: + +::: {.notes} +The calibration dataset consists of 408 pressed powder samples. +Each sample was shot at 5 different locations, and 50 shots were taken at each location. + +The data is grouped into folders where each folder corresponds to a sample. +Each folder contains a .csv file for each location, and each .csv file contains the spectra for the 3 spectrometers. +This is what we refer to as the CCS data. + +An example of a spectrum is shown in the figure on the right. +The x-axis represents the wavelength, and the y-axis represents the intensity of the light at each wavelength. + +As you can see, there are some regions of the spectrum that are masked out. +These regions are at the edges of each of the spectrometers' ranges and are not used in the analysis because they are noisy and not very informative. +We will talk more about the preprocessing of the CCS data later in the presentation. +::: + +## Composition Data {.smaller} +- 8 major oxides: $\text{SiO}_2$, $\text{TiO}_2$, $\text{Al}_2\text{O}_3$, $\text{Fe}_2\text{O}_3$, $\text{MgO}$, $\text{CaO}$, $\text{Na}_2\text{O}$, $\text{K}_2\text{O}$ + +- For each sample, we know the composition weight percentage (wt. %) + +::: {.fragment .fade-up} +- Composition data is used to train the MOC model + +::: {style="font-size: 0.5em;"} +| Target | Spectrum Name | Sample Name | SiO2 | TiO2 | Al2O3 | FeOT | MnO | MgO | CaO | Na2O | K2O | MOC total | +|--------|---------------|-------------|------|------|-------|------|-----|-----|-----|------|-----|-----------| +| AGV2 | AGV2 | AGV2 | 59.3 | 1.05 | 16.91 | 6.02 | 0.099 | 1.79 | 5.2 | 4.19 | 2.88 | 97.44 | +| BCR-2 | BCR2 | BCR2 | 54.1 | 2.26 | 13.5 | 12.42 | 0.2 | 3.59 | 7.12 | 3.16 | 1.79 | 98.14 | +| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | +| TB | --- | --- | 60.23 | 0.93 | 20.64 | 11.6387 | 0.052 | 1.93 | 0.000031 | 1.32 | 3.87 | 100.610731 | +| TB2 | --- | --- | 60.4 | 0.93 | 20.5 | 11.6536 | 0.047 | 1.86 | 0.2 | 1.29 | 3.86 | 100.7406 | +::: +::: + +::: {.fragment .fade-up} +![](static/introduction/oxide_corr.png){width="60%" height="60%" fig-align="center"} +::: + +::: {.notes} +The composition data is our ground truth. +It contains 8 major oxides: $\text{SiO}_2$, $\text{TiO}_2$, $\text{Al}_2\text{O}_3$, $\text{Fe}_2\text{O}_3$, $\text{MgO}$, $\text{CaO}$, $\text{Na}_2\text{O}$, and $\text{K}_2\text{O}$. +For each sample, we know the composition weight percentage (wt. %) of each of these oxides. + +This data is used to train the MOC model. +This table here shows an excerpt of the composition data for a few samples. +You can see that for each sample, we have the target, the spectrum name, the sample name, and the weight percentage of each of the oxides. +The last column is the total weight percentage of the oxides, which should in theory sum to 100%. +However, as you can see, the sum is not always exactly 100%, which is due to the presence of other elements in the samples that are not considered major oxides. +In the last row, you can see that the sum is actually greater than 100%, which is a physical impossibility. + +This figure here shows the correlation between the different oxides as a heatmap. +The correlation is calculated using the Pearson correlation coefficient, and you can see that there are some strong correlations between some of the oxides. +For example, $\text{SiO}_2$ and $\text{CaO}$ are strongly negatively correlated, indicating that when the weight percentage of one of these oxides increases, the weight percentage of the other decreases. + +This is the data that we use to train our models, which Christian will talk about next. +::: \ No newline at end of file diff --git a/p9-presentation/static/introduction/folder-structure.png b/p9-presentation/static/introduction/folder-structure.png new file mode 100644 index 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p9-presentation/static/references.bib | 113 +++++++++++++++++++++ 2 files changed, 164 insertions(+), 1 deletion(-) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index d76c9b36..d2ca2ccc 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -122,7 +122,56 @@ This is the focus of our pre-thesis project, and we will present our findings in ::: -## Related Work +## Related Work {.smaller} +:::: {.fragment .fade-up} +- **@takahashi_quantitative_2017**: ANNs for LIBS data non-linearities. +::: + +::: {.fragment .fade-up} +- **@lepore_quantitative_2022**: Full spectrum instead of sub-models often reduces RMSEP, enhancing geochemical accuracy. +::: + +::: {.fragment .fade-up} +- **@bai_application_2023**: Elastic net regression for Mars LIBS data and Norm 3 optimal normalization technique. +::: + +::: {.fragment .fade-up} +- **@dyar_effect_2021**: Larger LIBS training sets improve geochemical quantification accuracy. +::: + +::: {.fragment .fade-up} +- **@castorena_deep_2021**: Developed a real-time, efficient deep spectral CNN for LIBS. +::: + +::: {.fragment .fade-up} +- **@chen_xgboost_2016** presented XGBoost, a gradient boosting system. +- **@andersonPostlandingMajorElement2022** found GBR performed well for SuperCam calibration data. +::: + +::: {.notes} +I will briefly summarize the key aspects of our related work section from our report. + +First, @takahashi_quantitative_2017 applied artificial neural networks (ANNs) to LIBS data and found that ANNs showed potential to overcome these issues because ANNs are able to learn the non-linear relationship between the LIBS spectra and the composition of the sample. +This has inspired one of our experiments, as we compared the performance the MOC model with an ANN model. + +@lepore_quantitative_2022 examined how dividing LIBS spectra into sub-models impacts the prediction of major element compositions. +Their findings suggest that using the entire spectrum instead of sub-models often reduces the error and enhances geochemical accuracy. +This is interesting considering that sub-models were introduced to the MOC model to improve its performance, which contradicts the findings of this paper. + +@bai_application_2023 explored elastic net regression for Mars LIBS data and found it quite efficient, however perhaps more interestingly, they found that the Norm 3 normalization technique was the optimal normalization technique for their context. +This is one of the two normalization techniques used to preprocess the CCS data, and this find is something we considered when performing our experiments. + +@dyar_effect_2021 found that accuracy in geochemical quantification with LIBS improves as the training set size increases. +This is expected and tells us that we should aim to use as much data as possible when training our models. + +@castorena_deep_2021 developed a real-time, efficient deep spectral CNN for LIBS. +While we did not have time to experiment with CNNs this semester, this is something we might consider in the future. + +Finally, @chen_xgboost_2016 presented XGBoost, which is a gradient boosting system that has become popular among data scientists for its performance in diverse machine learning applications. +Interestingly, @andersonPostlandingMajorElement2022 found that Gradient Boosting Regression (GBR) performed well in predicting major oxides for SuperCam, which is ChemCam's successor. +This is why we chose to include XGBoost in our experiments, as it is a popular and effective implementation of GBR. +::: + ## Calibration Data {.smaller} @@ -180,6 +229,7 @@ These regions are at the edges of each of the spectrometers' ranges and are not We will talk more about the preprocessing of the CCS data later in the presentation. ::: + ## Composition Data {.smaller} - 8 major oxides: $\text{SiO}_2$, $\text{TiO}_2$, $\text{Al}_2\text{O}_3$, $\text{Fe}_2\text{O}_3$, $\text{MgO}$, $\text{CaO}$, $\text{Na}_2\text{O}$, $\text{K}_2\text{O}$ diff --git a/p9-presentation/static/references.bib b/p9-presentation/static/references.bib index e69de29b..42d2e353 100644 --- a/p9-presentation/static/references.bib +++ b/p9-presentation/static/references.bib @@ -0,0 +1,113 @@ +@article{castorena_deep_2021, + title = {Deep spectral CNN for laser induced breakdown spectroscopy}, + volume = {178}, + issn = {05848547}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0584854721000720}, + doi = {10.1016/j.sab.2021.106125}, + abstract = {This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral {CNN} is trained, it can accomplish either task through a single feedforward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, 'Curiosity'.}, + pages = {106125}, + journaltitle = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + shortjournal = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + author = {Castorena, Juan and Oyen, Diane and Ollila, Ann and Legett, Carey and Lanza, Nina}, + urldate = {2023-10-30}, + date = {2021-04}, + langid = {english} +} + +@article{bai_application_2023, + title = {Application of elastic net in quantitative analysis of major elements using Martian laser-induced breakdown spectroscopy datasets}, + volume = {199}, + issn = {05848547}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0584854722002312}, + doi = {10.1016/j.sab.2022.106587}, + abstract = {Multiple sets of laser-induced breakdown spectroscopy ({LIBS}) instruments, including {ChemCam} (Curiosity), {SuperCam} (Perseverance), and the Mars Surface Composition Detector ({MarSCoDe}, Zhurong), are working currently on the surface of Mars, a booming scene of {LIBS} technology application to planetary exploration. One of the primary challenges faced by those {LIBS} instruments is accurate and quantitative chemical composition determination of unexplored geological targets. Elastic net is a linear regression model that combines ridge regression and lasso model, which inherits the sparseness of lasso and the stability of ridge. In this work, we investigated the spectral features selected by elastic net model, the model performance and application to over 23,000 {LIBS} points. Selected features exhibit significant emission lines that are attributed to a certain single element and a few lines attributed to other related elements. These features enhance the interpretability of the model and weaken the matrix effect in {LIBS} quantitative analysis. In comparison, the results of elastic net are comparable and may be slightly better in some cases than other common linear regression models. For igneous calibration targets (norite, picrite, and shergottite), the predictions of elastic net are closer to the values measured in Earth laboratory than the final multivariate oxide composition values predicted by {ChemCam} team. Finally, we predicted the oxide abundances of 8 major elements from Mars {LIBS} spectra of over 23,000 points using elastic net. The predicted values of the elastic net models are highly correlated with those from the {ChemCam} team model. These results indicate that elastic net is viable for quantitative analysis Mars {LIBS} spectra. We propose that elastic net is an important candidate model for the quantitative analysis of the Zhurong Mar {SCoDe} {LIBS} spectra.}, + pages = {106587}, + journaltitle = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + shortjournal = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + author = {Bai, Hongchun and Liu, Ping and Fu, Xiaohui and Qiao, Le and Liu, Changqing and Xin, Yanqi and Ling, Zongcheng}, + urldate = {2023-11-07}, + date = {2023-01}, + langid = {english} +} + +@article{dyar_effect_2021, + title = {Effect of data set size on geochemical quantification accuracy with laser-induced breakdown spectroscopy}, + volume = {177}, + issn = {05848547}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0584854721000094}, + doi = {10.1016/j.sab.2021.106073}, + abstract = {Laser-induced breakdown spectroscopy ({LIBS}) data acquired from 2959 geochemical standards allow the effects of training set size on {LIBS} accuracy in geochemical analyses to be evaluated. In addition, {LIBS} prediction accuracies are quantified for 65 elements based on a typical benchtop instrument. Analyses used two equivalent randomly selected subsets of the full data set to compare prediction accuracies of partial least squares models using 75, 50, 25, 10, 5, 2.5, 1, and 0.5\% of the total data set for training and the remainder for testing. The number of components, a measure of complexity, in the {PLS} models was shown to increase with the size of the training set. Based on root mean square errors on unseen test data, our results show that the larger the training set, the better (lower) the prediction accuracy will be on unseen data. Calibration (training set) size was shown to have a first-order effect on prediction accuracy relative to spectral resolution and detector sensitivity. Different methods of assessing model accuracy using root mean square error ({RMSE}) are compared, including the error of the calibration ({RMSE}-C), the error of cross-validation ({RMSE}-{CV}), and the error of prediction ({RMSE}-P). Use of {RMSE}-C is inappropriate because the samples being predicted are those on which the model was trained. In data sets that are sufficiently large, use of test data ({RMSE}-P) provides the best measure of prediction accuracy, while {RMSE}-{CV} is useful only to provide an estimate of subsequent model performance. Increasing the number of crossvalidation folds for our large dataset yields surprisingly comparable {RMSE}-{CV} values for models with five or more (up to 100) folds, but this result is likely not applicable to smaller data sets and needs further evaluation.}, + pages = {106073}, + journaltitle = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + shortjournal = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + author = {Dyar, M. Darby and Ytsma, Cai R.}, + urldate = {2023-11-07}, + date = {2021-03}, + langid = {english} +} + +@article{lepore_quantitative_2022, + title = {Quantitative prediction accuracies derived from laser-induced breakdown spectra using optimized multivariate submodels}, + volume = {191}, + issn = {05848547}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0584854722000520}, + doi = {10.1016/j.sab.2022.106408}, + abstract = {The accuracy of laser-induced breakdown spectroscopy ({LIBS}) methods for analyzing geological samples is improved when calibration standards and unknown targets are compositionally similar. A recent study suggests that customized submodels can be used to optimize calibration datasets to achieve more accurate predictions [1]. In practice, this is difficult to implement because the errors inherent in the methods used for sorting unknown targets by composition may affect how successfully this matching can occur. Moreover, creation of submodels intrinsically reduces the size of the dataset on which the model is trained, which has been shown to reduce prediction accuracy. This paper uses {LIBS} spectra of 2990 unique rock powder standards to compare the accuracy of 1) submodels generated for each element over its geochemical range, 2) submodels created using {SiO}2 content only, 3) submodels created using the ratio of Si({II})/Si(I) emission lines to group spectra by a proxy for approximate plasma temperature, and 4) models created using all data. Results indicate that prediction accuracies are not always improved by creating submodels because subdividing a dataset to optimize calibrations will always result in a smaller database available for each submodel, and the reduced training set size negatively affects accuracy. Customized {LIBS} standards for specific applications might overcome this problem in cases where the matrix is similar and the expected concentration range is known. But in a majority of geochemical applications, submodel approaches are only useful in improving prediction accuracies when the initial database is itself extensive enough to support large, robust submodel calibration suites.}, + pages = {106408}, + journaltitle = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + shortjournal = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + author = {Lepore, Kate H. and Ytsma, Caroline R. and Dyar, M. Darby}, + urldate = {2023-11-07}, + date = {2022-05}, + langid = {english} +} + +@article{takahashi_quantitative_2017, + title = {Quantitative methods for compensation of matrix effects and self-absorption in Laser Induced Breakdown Spectroscopy signals of solids}, + volume = {138}, + issn = {05848547}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0584854716303299}, + doi = {10.1016/j.sab.2017.09.010}, + abstract = {This paper reviews methods to compensate for matrix effects and self-absorption during quantitative analysis of compositions of solids measured using Laser Induced Breakdown Spectroscopy ({LIBS}) and their applications to in-situ analysis. Methods to reduce matrix and self-absorption effects on calibration curves are first introduced. The conditions where calibration curves are applicable to quantification of compositions of solid samples and their limitations are discussed. While calibration-free {LIBS} ({CF}-{LIBS}), which corrects matrix effects theoretically based on the Boltzmann distribution law and Saha equation, has been applied in a number of studies, requirements need to be satisfied for the calculation of chemical compositions to be valid. Also, peaks of all elements contained in the target need to be detected, which is a bottleneck for in-situ analysis of unknown materials. Multivariate analysis techniques are gaining momentum in {LIBS} analysis. Among the available techniques, principal component regression ({PCR}) analysis and partial least squares ({PLS}) regression analysis, which can extract related information to compositions from all spectral data, are widely established methods and have been applied to various fields including in-situ applications in air and for planetary explorations. Artificial neural networks ({ANNs}), where non-linear effects can be modelled, have also been investigated as a quantitative method and their applications are introduced. The ability to make quantitative estimates based on {LIBS} signals is seen as a key element for the technique to gain wider acceptance as an analytical method, especially in in-situ applications. In order to accelerate this process, it is recommended that the accuracy should be described using common figures of merit which express the overall normalised accuracy, such as the normalised root mean square errors ({NRMSEs}), when comparing the accuracy obtained from different setups and analytical methods.}, + pages = {31--42}, + journaltitle = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + shortjournal = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + author = {Takahashi, Tomoko and Thornton, Blair}, + urldate = {2023-11-07}, + date = {2017-12}, + langid = {english} +} + +@inproceedings{chen_xgboost_2016, + location = {San Francisco California {USA}}, + title = {{XGBoost}: A Scalable Tree Boosting System}, + isbn = {978-1-4503-4232-2}, + url = {https://dl.acm.org/doi/10.1145/2939672.2939785}, + doi = {10.1145/2939672.2939785}, + shorttitle = {{XGBoost}}, + abstract = {Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable endto-end tree boosting system called {XGBoost}, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, {XGBoost} scales beyond billions of examples using far fewer resources than existing systems.}, + eventtitle = {{KDD} '16: The 22nd {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining}, + pages = {785--794}, + booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining}, + publisher = {{ACM}}, + author = {Chen, Tianqi and Guestrin, Carlos}, + urldate = {2023-11-13}, + date = {2016-08-13}, + langid = {english} +} + +@article{andersonPostlandingMajorElement2022, + title = {Post-Landing Major Element Quantification Using {{SuperCam}} Laser Induced Breakdown Spectroscopy}, + author = {Anderson, Ryan B. and Forni, Olivier and Cousin, Agnes and Wiens, Roger C. and Clegg, Samuel M. and Frydenvang, Jens and Gabriel, Travis S. J. and Ollila, Ann and Schröder, Susanne and Beyssac, Olivier and Gibbons, Erin and Vogt, David S. and Clavé, Elise and Manrique, Jose-Antonio and Legett, Carey and Pilleri, Paolo and Newell, Raymond T. and Sarrao, Joseph and Maurice, Sylvestre and Arana, Gorka and Benzerara, Karim and Bernardi, Pernelle and Bernard, Sylvain and Bousquet, Bruno and Brown, Adrian J. and Alvarez-Llamas, César and Chide, Baptiste and Cloutis, Edward and Comellas, Jade and Connell, Stephanie and Dehouck, Erwin and Delapp, Dorothea M. and Essunfeld, Ari and Fabre, Cecile and Fouchet, Thierry and Garcia-Florentino, Cristina and García-Gómez, Laura and Gasda, Patrick and Gasnault, Olivier and Hausrath, Elisabeth M. and Lanza, Nina L. and Laserna, Javier and Lasue, Jeremie and Lopez, Guillermo and Madariaga, Juan Manuel and Mandon, Lucia and Mangold, Nicolas and Meslin, Pierre-Yves and Nelson, Anthony E. and Newsom, Horton and Reyes-Newell, Adriana L. and Robinson, Scott and Rull, Fernando and Sharma, Shiv and Simon, Justin I. and Sobron, Pablo and Fernandez, Imanol Torre and Udry, Arya and Venhaus, Dawn and McLennan, Scott M. and Morris, Richard V. and Ehlmann, Bethany}, + date = {2022-02-01}, + journaltitle = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + shortjournal = {Spectrochimica Acta Part B: Atomic Spectroscopy}, + volume = {188}, + pages = {106347}, + issn = {0584-8547}, + doi = {10.1016/j.sab.2021.106347}, + url = {https://www.sciencedirect.com/science/article/pii/S0584854721003049}, + urldate = {2023-05-15}, + abstract = {The SuperCam instrument on the Perseverance Mars 2020 rover uses a pulsed 1064~nm laser to ablate targets at a distance and conduct laser induced breakdown spectroscopy (LIBS) by analyzing the light from the resulting plasma. SuperCam LIBS spectra are preprocessed to remove ambient light, noise, and the continuum signal present in LIBS observations. Prior to quantification, spectra are masked to remove noisier spectrometer regions and spectra are normalized to minimize signal fluctuations and effects of target distance. In some cases, the spectra are also standardized or binned prior to quantification. To determine quantitative elemental compositions of diverse geologic materials at Jezero crater, Mars, we use a suite of 1198 laboratory spectra of 334 well-characterized reference samples. The samples were selected to span a wide range of compositions and include typical silicate rocks, pure minerals (e.g., silicates, sulfates, carbonates, oxides), more unusual compositions (e.g., Mn ore and sodalite), and replicates of the sintered SuperCam calibration targets (SCCTs) onboard the rover. For each major element (SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O), the database was subdivided into five “folds” with similar distributions of the element of interest. One fold was held out as an independent test set, and the remaining four folds were used to optimize multivariate regression models relating the spectrum to the composition. We considered a variety of models, and selected several for further investigation for each element, based primarily on the root mean squared error of prediction (RMSEP) on the test set, when analyzed at 3~m. In cases with several models of comparable performance at 3~m, we incorporated the SCCT performance at different distances to choose the preferred model. Shortly after landing on Mars and collecting initial spectra of geologic targets, we selected one model per element. Subsequently, with additional data from geologic targets, some models were revised to ensure results that are more consistent with geochemical constraints. The calibration discussed here is a snapshot of an ongoing effort to deliver the most accurate chemical compositions with SuperCam LIBS.}, + langid = {english} +} \ No newline at end of file From 0b6cc4eb8d5d5782895157496308ab2c1cdeafea Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Mon, 5 Feb 2024 16:47:31 +0100 Subject: [PATCH 08/13] Update _introduction.qmd --- p9-presentation/sections/_introduction.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index d2ca2ccc..43e7ef49 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -103,7 +103,7 @@ Following this, we have decided to replicate and improve upon their work. ::: {.fragment .fade-up} ::: {style="border: 2px solid black; padding: 0px 15px;"} -**Problem**: Given a series of experiments and the resulting models, identify the components that acontribute the most to the overall error $E(M)$. +**Problem**: Given a series of experiments and the resulting models, identify the components that contribute the most to the overall error $E(M)$. ::: ::: From 08adeb035fb26688f518df009882309e8d057109 Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Mon, 5 Feb 2024 16:55:41 +0100 Subject: [PATCH 09/13] Update _introduction.qmd --- p9-presentation/sections/_introduction.qmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index 43e7ef49..d09f1108 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -35,12 +35,12 @@ Presenter: Patrick Frostholm Østergaard. :::: {.column width="50%"} -::: {.fragment .fade-up fragment-index=1} -![Viking Lander](static/introduction/viking-lander.jpg){width="60%" height="60%"} +::: {.fragment .fade-up fragment-index=1 style="border-radius: 10px;"} +![Viking Lander](static/introduction/viking-lander.jpg){width="60%" height="60%" .rounded-fig} ::: ::: {.fragment .fade-up fragment-index=4} -![Mars Science Laboratory Curiosity Rover](static/introduction/msl.jpg){width="75%" height="75%"} +![Mars Science Laboratory Curiosity Rover](static/introduction/msl.jpg){width="75%" height="75%" .rounded-fig} ::: :::: From c8f750d5f9da19ab304ce9af1977d5d43c55ed8b Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Mon, 5 Feb 2024 16:55:46 +0100 Subject: [PATCH 10/13] Update styles.css --- p9-presentation/styles.css | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/p9-presentation/styles.css b/p9-presentation/styles.css index 94d6fa48..6e0ba3bb 100644 --- a/p9-presentation/styles.css +++ b/p9-presentation/styles.css @@ -6,3 +6,13 @@ .reveal-full-page { --r-background-color: #fefefe; } + +figcaption { + margin: auto; + text-align: center; + font-style: italic; +} + +.rounded-fig { + border-radius: 5px; +} From 5e508e3a0242b4049b305ea474140317951b812d Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Tue, 6 Feb 2024 17:20:00 +0100 Subject: [PATCH 11/13] Fixes --- p9-presentation/sections/_introduction.qmd | 28 +++++++++++----------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index d09f1108..5cd4fd9b 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -50,10 +50,10 @@ Presenter: Patrick Frostholm Østergaard. ::: {.notes} In the 1970s, the Viking missions were the first to land on Mars. Goal: Is there life on Mars? - Experiments showed positive results, but the results were inconclusive and were not repeated due to budget constraints. 1990s: Philosophical shift within NASA: "Is there life on Mars?" to "Did Mars ever have the conditions to support life as we know it?" + In 2004, the Mars Exploration Rover mission landed the rovers Spirit and Opportunity on Mars, which quickly discovered evidence of water on Mars. A few years later in 2012, the Mars Science Laboratory (MSL) mission landed the Curiosity rover on Mars. @@ -62,10 +62,9 @@ Rover is equipped with a suite of instruments to study the Martian climate and g One of the instruments is the Chemistry and Camera (ChemCam) instrument, a laser-induced breakdown spectroscopy (LIBS) instrument that can analyze the elemental composition of rocks and soil from a distance. ChemCam is used to study the composition of the Martian surface and to search for organic material. This requires the development of machine learning models to analyze the data, which is the focus of this presentation. - NASA has made most of the ChemCam calibration data publicly available, and they have also published a few papers on the machine learning models they have developed for ChemCam. We have worked with one of the authors of these papers, Jens Frydenvang, who asserted that the models are far from perfect and that there is room for improvement. -Following this, we have decided to replicate and improve upon their work. +Following this, we have decided to replicate and attempt to improve upon their work. ::: @@ -114,7 +113,8 @@ The MOC model is trained on a calibration dataset of clean, calibrated spectra ( When a new spectrum is collected on Mars, it is first preprocessed into the CCS format and then fed into the MOC model to predict the composition of the sample. Christian will elaborate on how exactly the MOC model works later in the presentation. -Our goal is to replicate the MOC model and perform experiments to identify the components that contribute the most to the overall error $E(M)$, as defined by the root mean squared error (RMSE). +Our goal was to replicate the MOC model and perform experiments to identify the components that contribute the most to the overall error $E(M)$, as defined by the root mean squared error (RMSE). + We do this through a series of experiments, which Ivik will explain in more detail later in the presentation. By doing this, we hope to gain a better understanding of the MOC model and to identify areas where it can be improved. @@ -151,7 +151,7 @@ This is the focus of our pre-thesis project, and we will present our findings in ::: {.notes} I will briefly summarize the key aspects of our related work section from our report. -First, @takahashi_quantitative_2017 applied artificial neural networks (ANNs) to LIBS data and found that ANNs showed potential to overcome these issues because ANNs are able to learn the non-linear relationship between the LIBS spectra and the composition of the sample. +First, @takahashi_quantitative_2017 applied artificial neural networks (ANNs) to LIBS data and found that ANNs showed potential since they are able to learn the non-linear relationship between the LIBS spectra and the composition of the sample. This has inspired one of our experiments, as we compared the performance the MOC model with an ANN model. @lepore_quantitative_2022 examined how dividing LIBS spectra into sub-models impacts the prediction of major element compositions. @@ -186,7 +186,8 @@ This is why we chose to include XGBoost in our experiments, as it is a popular a ::: ::: {.fragment .fade-up fragment-index=2} -- CCS: 3 spectrometers +- Grouped into folders: + - Each folder corresponds to a sample ::: ::: {.fragment .fade-up} - Masked regions: @@ -194,7 +195,7 @@ This is why we chose to include XGBoost in our experiments, as it is a popular a - 338.457 — 340.797 nm - 382.138 — 387.859 nm - 473.184 — 492.427 nm - - 849 — 905.574 nm + - 849.000 — 905.574 nm ::: ::: @@ -214,7 +215,8 @@ This is why we chose to include XGBoost in our experiments, as it is a popular a :::: ::: {.notes} -The calibration dataset consists of 408 pressed powder samples. +Now we will take a closer look at data that we have worked with this semester. +First, we have the calibration dataset, which consists of 408 pressed powder samples. Each sample was shot at 5 different locations, and 50 shots were taken at each location. The data is grouped into folders where each folder corresponds to a sample. @@ -223,10 +225,9 @@ This is what we refer to as the CCS data. An example of a spectrum is shown in the figure on the right. The x-axis represents the wavelength, and the y-axis represents the intensity of the light at each wavelength. - As you can see, there are some regions of the spectrum that are masked out. These regions are at the edges of each of the spectrometers' ranges and are not used in the analysis because they are noisy and not very informative. -We will talk more about the preprocessing of the CCS data later in the presentation. +This masking is part of the preprocessing of the CCS data, which is something we will talk more about later in the presentation. ::: @@ -254,13 +255,13 @@ We will talk more about the preprocessing of the CCS data later in the presentat ::: ::: {.notes} -The composition data is our ground truth. +Next, we have the composition data, which is our ground truth. It contains 8 major oxides: $\text{SiO}_2$, $\text{TiO}_2$, $\text{Al}_2\text{O}_3$, $\text{Fe}_2\text{O}_3$, $\text{MgO}$, $\text{CaO}$, $\text{Na}_2\text{O}$, and $\text{K}_2\text{O}$. -For each sample, we know the composition weight percentage (wt. %) of each of these oxides. +For each sample in the calibration dataset, we know the composition weight percentage of each of these oxides. This data is used to train the MOC model. This table here shows an excerpt of the composition data for a few samples. -You can see that for each sample, we have the target, the spectrum name, the sample name, and the weight percentage of each of the oxides. +You can see that we know the composition of each sample in terms of the weight percentage of each of the major oxides. The last column is the total weight percentage of the oxides, which should in theory sum to 100%. However, as you can see, the sum is not always exactly 100%, which is due to the presence of other elements in the samples that are not considered major oxides. In the last row, you can see that the sum is actually greater than 100%, which is a physical impossibility. @@ -268,6 +269,5 @@ In the last row, you can see that the sum is actually greater than 100%, which i This figure here shows the correlation between the different oxides as a heatmap. The correlation is calculated using the Pearson correlation coefficient, and you can see that there are some strong correlations between some of the oxides. For example, $\text{SiO}_2$ and $\text{CaO}$ are strongly negatively correlated, indicating that when the weight percentage of one of these oxides increases, the weight percentage of the other decreases. - This is the data that we use to train our models, which Christian will talk about next. ::: \ No newline at end of file From dd5241472ea2685b3124033e8ffd40624db76f90 Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Tue, 6 Feb 2024 17:21:30 +0100 Subject: [PATCH 12/13] Update _introduction.qmd --- p9-presentation/sections/_introduction.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index 5cd4fd9b..2a621ae0 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -60,7 +60,7 @@ A few years later in 2012, the Mars Science Laboratory (MSL) mission landed the Rover is equipped with a suite of instruments to study the Martian climate and geology and to search for organic material. One of the instruments is the Chemistry and Camera (ChemCam) instrument, a laser-induced breakdown spectroscopy (LIBS) instrument that can analyze the elemental composition of rocks and soil from a distance. -ChemCam is used to study the composition of the Martian surface and to search for organic material. +In very simple terms, the instrument shoots a laser at a rock, and the light emitted by the rock is then captured by spectrometers as a spectrum, which can be used to determine the composition of the rock. This requires the development of machine learning models to analyze the data, which is the focus of this presentation. NASA has made most of the ChemCam calibration data publicly available, and they have also published a few papers on the machine learning models they have developed for ChemCam. We have worked with one of the authors of these papers, Jens Frydenvang, who asserted that the models are far from perfect and that there is room for improvement. From 7edf693a38039fe18cfa04613f2444d1b0c5df2b Mon Sep 17 00:00:00 2001 From: Pattrigue Date: Tue, 6 Feb 2024 17:24:22 +0100 Subject: [PATCH 13/13] Update _introduction.qmd --- p9-presentation/sections/_introduction.qmd | 2 ++ 1 file changed, 2 insertions(+) diff --git a/p9-presentation/sections/_introduction.qmd b/p9-presentation/sections/_introduction.qmd index 2a621ae0..635dd640 100644 --- a/p9-presentation/sections/_introduction.qmd +++ b/p9-presentation/sections/_introduction.qmd @@ -62,6 +62,8 @@ Rover is equipped with a suite of instruments to study the Martian climate and g One of the instruments is the Chemistry and Camera (ChemCam) instrument, a laser-induced breakdown spectroscopy (LIBS) instrument that can analyze the elemental composition of rocks and soil from a distance. In very simple terms, the instrument shoots a laser at a rock, and the light emitted by the rock is then captured by spectrometers as a spectrum, which can be used to determine the composition of the rock. This requires the development of machine learning models to analyze the data, which is the focus of this presentation. +Specifically, this type of problem is a supervised learning, multivariate regression problem. +It is supervised because we have labeled data, and it is multivariate because we are predicting multiple outputs. NASA has made most of the ChemCam calibration data publicly available, and they have also published a few papers on the machine learning models they have developed for ChemCam. We have worked with one of the authors of these papers, Jens Frydenvang, who asserted that the models are far from perfect and that there is room for improvement. Following this, we have decided to replicate and attempt to improve upon their work.

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