Skip to main content
U.S. flag

An official website of the United States government

Ryan Bradley Anderson (Former Employee)

Science and Products

USGS

Terrestrial Remote Sensing Data Ingestion with PyHAT (Python Hyperspectral Analysis Tool)

This work will make it easier to work with multiple terrestrial data sets in PyHAT, a USGS tool that enables machine learning analysis of spectral datasets
By
Community for Data Integration (CDI)
Terrestrial Remote Sensing Data Ingestion with PyHAT (Python Hyperspectral Analysis Tool)

Terrestrial Remote Sensing Data Ingestion with PyHAT (Python Hyperspectral Analysis Tool)

This work will make it easier to work with multiple terrestrial data sets in PyHAT, a USGS tool that enables machine learning analysis of spectral datasets
Learn More
Thumbnail graphic for the PyHAT software package

Python Hyperspectral Analysis Tool (PyHAT)

The Python Hyperspectral Analysis Tool (PyHAT) provides access to data processing, analysis, and machine learning capabilities for spectroscopic applications. It includes a GUI so you can get straight to analyzing data without writing any code. Or, if you are comfortable writing code, PyHAT can be imported just like any other Python package.
By
Astrogeology Science Center
Python Hyperspectral Analysis Tool (PyHAT)

Python Hyperspectral Analysis Tool (PyHAT)

The Python Hyperspectral Analysis Tool (PyHAT) provides access to data processing, analysis, and machine learning capabilities for spectroscopic applications. It includes a GUI so you can get straight to analyzing data without writing any code. Or, if you are comfortable writing code, PyHAT can be imported just like any other Python package.
Learn More

Digital Terrain Models for Mars Sinuous Ridges and Alluvial Fans Projects Digital Terrain Models for Mars Sinuous Ridges and Alluvial Fans Projects

Metadata on this main landing page applies to all DTMs listed under Child Items, apart from USGS-made DTMs [HRSC DTM_75 (centered at -28.8N, 78.6E)_USGS and CTM DTM over "Crater L" (centered at -23.1N, 74.3E)_USGS]. Metadata associated with USGS-made DTMs are located with DTM files under their respective Child Item.
By
Astrogeology Science Center
Python Hyperspectral Analysis Tool (PyHAT) Logo
Python Hyperspectral Analysis Tool (PyHAT) Logo
Python Hyperspectral Analysis Tool (PyHAT) Logo
Python Hyperspectral Analysis Tool (PyHAT) Logo

Python Hyperspectral Analysis Tool (PyHAT) Logo

Python Hyperspectral Analysis Tool (PyHAT) Logo

This a version of the logo for the Python Hyperspectral Analysis Tool (PyHAT). It is intended for use in info boxes on the USGS website. The spectrum in the graphic is a laser induced breakdown spectroscopy spectrum, plotted on a logarithmic y axis to emphasize weaker emission peaks.

By
Astrogeology Science Center
Python Hyperspectral Analysis Tool (PyHAT) Logo

Python Hyperspectral Analysis Tool (PyHAT) Logo

Python Hyperspectral Analysis Tool (PyHAT) Logo

This a version of the logo for the Python Hyperspectral Analysis Tool (PyHAT). It is intended for use in info boxes on the USGS website. The spectrum in the graphic is a laser induced breakdown spectroscopy spectrum, plotted on a logarithmic y axis to emphasize weaker emission peaks.

By
Astrogeology Science Center
screenshot of an example data table in PyHAT format
Python Hyperspectral Analysis Tool (PyHAT) Data Format Example
Python Hyperspectral Analysis Tool (PyHAT) Data Format Example
screenshot of an example data table in PyHAT format

Python Hyperspectral Analysis Tool (PyHAT) Data Format Example

Python Hyperspectral Analysis Tool (PyHAT) Data Format Example

Screenshot showing the simple data format used by the Python Hyperspectral Analysis Tool (PyHAT). Spectra are stored in rows of the table, along with their associated metadata and compositional information.

By
Astrogeology Science Center
screenshot of an example data table in PyHAT format

Python Hyperspectral Analysis Tool (PyHAT) Data Format Example

Python Hyperspectral Analysis Tool (PyHAT) Data Format Example

Screenshot showing the simple data format used by the Python Hyperspectral Analysis Tool (PyHAT). Spectra are stored in rows of the table, along with their associated metadata and compositional information.

By
Astrogeology Science Center
CRISM mineral map image, with Red = Olivine, Green = High-Ca Pyroxene, and Blue = Low-Ca pyroxene
Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars
Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars
CRISM mineral map image, with Red = Olivine, Green = High-Ca Pyroxene, and Blue = Low-Ca pyroxene

Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars

Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars

This figure shows an example mineral parameter map image generated using PyHAT. The area in this Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) image is Jezero crater, the landing site of NASA's Mars Perseverance rover.

By
Astrogeology Science Center, Astrogeology Science Center
CRISM mineral map image, with Red = Olivine, Green = High-Ca Pyroxene, and Blue = Low-Ca pyroxene

Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars

Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars

This figure shows an example mineral parameter map image generated using PyHAT. The area in this Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) image is Jezero crater, the landing site of NASA's Mars Perseverance rover.

By
Astrogeology Science Center, Astrogeology Science Center
Graph of PCA scores, color coded by Fe2O3T content, and the loading vectors used to calculate the scores.
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example
Graph of PCA scores, color coded by Fe2O3T content, and the loading vectors used to calculate the scores.

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum.

By
Astrogeology Science Center
Graph of PCA scores, color coded by Fe2O3T content, and the loading vectors used to calculate the scores.

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum.

By
Astrogeology Science Center
Plot of PCA scores and loadings. The points in the scores plot are color coded based on the cluster assigned by k-means
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example
Plot of PCA scores and loadings. The points in the scores plot are color coded based on the cluster assigned by k-means

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum.

By
Astrogeology Science Center
Plot of PCA scores and loadings. The points in the scores plot are color coded based on the cluster assigned by k-means

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum.

By
Astrogeology Science Center
Scatter plot showing PCA scores as points. Several points are marked in red as outliers.
Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example
Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example
Scatter plot showing PCA scores as points. Several points are marked in red as outliers.

Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example

Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example

This figure shows an example of outlier identification using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum. Dimensionality was then reduced using principal components analysis (PCA).

By
Astrogeology Science Center
Scatter plot showing PCA scores as points. Several points are marked in red as outliers.

Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example

Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example

This figure shows an example of outlier identification using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum. Dimensionality was then reduced using principal components analysis (PCA).

By
Astrogeology Science Center
Plot showing the training set and cross validation error vs number of components for a PLS model predicting CaO
Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example
Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example
Plot showing the training set and cross validation error vs number of components for a PLS model predicting CaO

Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example

Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example

This figure shows the results of cross-validating a Partial Least Squares (PLS) model to predict the abundance of CaO in geologic targets using PyHAT. Cross validation is necessary to optimize the parameters of a regression algorithm to avoid overfitting.

By
Astrogeology Science Center
Plot showing the training set and cross validation error vs number of components for a PLS model predicting CaO

Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example

Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example

This figure shows the results of cross-validating a Partial Least Squares (PLS) model to predict the abundance of CaO in geologic targets using PyHAT. Cross validation is necessary to optimize the parameters of a regression algorithm to avoid overfitting.

By
Astrogeology Science Center
Plot showing the LIBS spectrum of basalt, with colored lines approximating the baseline using different algorithms.
Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example
Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example
Plot showing the LIBS spectrum of basalt, with colored lines approximating the baseline using different algorithms.

Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example

Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example

This figure shows an example spectrum plot generated using PyHAT. The black line is a laser induced breakdown spectroscopy (LIBS) spectrum of a basalt sample. The colored lines show the baseline estimated using several different algorithms. 

By
Astrogeology Science Center
Plot showing the LIBS spectrum of basalt, with colored lines approximating the baseline using different algorithms.

Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example

Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example

This figure shows an example spectrum plot generated using PyHAT. The black line is a laser induced breakdown spectroscopy (LIBS) spectrum of a basalt sample. The colored lines show the baseline estimated using several different algorithms. 

By
Astrogeology Science Center
Scatter plot comparing predicted vs actual CaO content for a set of spectra of geologic targets using two regression models
Python Hyperspectral Analysis Tool (PyHAT) Regression Example
Python Hyperspectral Analysis Tool (PyHAT) Regression Example
Scatter plot comparing predicted vs actual CaO content for a set of spectra of geologic targets using two regression models

Python Hyperspectral Analysis Tool (PyHAT) Regression Example

Python Hyperspectral Analysis Tool (PyHAT) Regression Example

This figure compares the results of two regression models to predict the abundance of CaO in geologic standards based on their laser induced breakdown spectroscopy (LIBS) spectra using PyHAT. The horizontal axis is the independently measured CaO abundance, the vertical axis is the abundance predicted by the models.

By
Astrogeology Science Center, Astrogeology Science Center
Scatter plot comparing predicted vs actual CaO content for a set of spectra of geologic targets using two regression models

Python Hyperspectral Analysis Tool (PyHAT) Regression Example

Python Hyperspectral Analysis Tool (PyHAT) Regression Example

This figure compares the results of two regression models to predict the abundance of CaO in geologic standards based on their laser induced breakdown spectroscopy (LIBS) spectra using PyHAT. The horizontal axis is the independently measured CaO abundance, the vertical axis is the abundance predicted by the models.

By
Astrogeology Science Center, Astrogeology Science Center
Filter Total Items: 32

Python Hyperspectral Analysis Tool (PyHAT) user guide Python Hyperspectral Analysis Tool (PyHAT) user guide

This report is a user guide for the 0.1.2 release of the Python Hyperspectral Analysis Tool (PyHAT) and its graphical user interface (GUI). The GUI is intended to provide an intuitive front end to allow users to apply sophisticated preprocessing and analysis methods to spectroscopic data. Though the PyHAT package has been developed with a particular focus on laser-induced breakdown...
Authors
Ryan Anderson, Itiya Aneece, Travis Gabriel
By
Natural Hazards Mission Area, Astrogeology Science Center, Community for Data Integration (CDI)

Intense alteration on early Mars revealed by high-aluminum rocks at Jezero Crater Intense alteration on early Mars revealed by high-aluminum rocks at Jezero Crater

The NASA Perseverance rover discovered light-toned float rocks scattered across the surface of Jezero crater that are particularly rich in alumina ( ~ 35 wt% Al2O3) and depleted in other major elements (except silica). These unique float rocks have heterogeneous mineralogy ranging from kaolinite/halloysite-bearing in hydrated samples, to spinel-bearing in dehydrated samples also...
Authors
C. Royer, C.C. Bedford, J.R. Johnson, B.H.N. Horgan, A. Broz, O. Forni, S. Connell, R.C. Wiens, L. Mandon, B.S. Kathir, E.M. Hausrath, A. Udry, J.M. Madariaga, E. Dehouck, Ryan Anderson, P.S.A. Beck, O. Beyssac, É. Clavé, S.M. Clegg, E. Cloutis, T. Fouchet, Travis Gabriel, B.J. Garczynski, A. Klidaras, H.T. Manelski, L.E. Mayhew, J. Nunez, A.M. Ollila, S.E. Schröder, J.I. Simon, U. Wolf, K.M. Stack, A. Cousin, S. Maurice
By
Natural Hazards Mission Area, Astrogeology Science Center

Regolith of the crater floor units, Jezero crater, Mars: Textures, composition and implications for provenance Regolith of the crater floor units, Jezero crater, Mars: Textures, composition and implications for provenance

A multi-instrument study of the regolith of Jezero crater floor units by the Perseverance rover has identified three types of regolith: fine-grained, coarse-grained, and mixed-type. Mastcam-Z, WATSON, and SuperCam RMI were used to characterize regolith texture, particle size, and roundedness where possible. Mastcam-Z multispectral and SuperCam LIBS data were used to constrain the...
Authors
Alicia Vaughan, Michelle Minitti, Emily Cardarelli, Jeffrey Johnson, Linda Kah, Paolo Pilleri, Mellisa Rice, Mark Sephton, Briony Horgan, Roger Wiens, R. Yingst, Maria-Paz Zorzano Mier, Ryan Anderson, James F. III Bell, Adrian Brown, Edward Cloutis, Agnes Cousin, Kenneth E. Herkenhoff, Elisabeth Housrath, Alexander Hayes, Kjartan Kinch, Marco Merusi, Chase Million, Robert Sullivan, Sandra Siljestrom, Michael St. Clair
By
Natural Hazards Mission Area, Astrogeology Science Center

Spatial and temporal distribution of sinuous ridges in southeastern Terra Sabaea and the northern region of Hellas Planitia, Mars Spatial and temporal distribution of sinuous ridges in southeastern Terra Sabaea and the northern region of Hellas Planitia, Mars

Sinuous ridges are an important yet understudied component of Mars' hydrologic history. We have produced a map of sinuous ridges, valleys and channels, and tectonic ridges across southeastern Terra Sabaea and into northern Hellas Planitia (10°-45° S, 35°-80° E) using a CTX mosaic. Although we mapped different types of ridges and negative relief features, the focus of this paper are the...
Authors
Amber Gullikson, Ryan Anderson, Rebecca Williams
By
Natural Hazards Mission Area, Astrogeology Science Center

Morphology and paleohydrology of intracrater alluvial fans north of Hellas Basin, Mars Morphology and paleohydrology of intracrater alluvial fans north of Hellas Basin, Mars

Alluvial fans and sinuous ridges are both important records of the history of fluvial activity on Mars, and they often occur together. We present observations of alluvial fans, many of which exhibit inverted relief, in five craters in the region north of Hellas basin. The observed fans ranged in size from ~10 to 820 km2. We identified three primary fan surface morphology classes (chute...
Authors
Ryan Anderson, Rebecca Williams, Amber Gullikson, William Nelson
By
Natural Hazards Mission Area, Astrogeology Science Center

Overview of the morphology and chemistry of diagenetic features in the clay-rich Glen Torridon Unit of Gale Crater, Mars Overview of the morphology and chemistry of diagenetic features in the clay-rich Glen Torridon Unit of Gale Crater, Mars

The clay-rich Glen Torridon region of Gale crater, Mars, was explored between sols 2300 and 3007. Here, we analyzed the diagenetic features observed by Curiosity, including veins, cements, nodules, and nodular bedrock, using the ChemCam, Mastcam, and Mars Hand Lens Imager instruments. We discovered many diagenetic features in Glen Torridon, including dark-toned iron- and manganese-rich...
Authors
Patrick Gasda, Jade Comellas, A Essunfeld, D. Das, Alex B Bryk, Erwin Dehouck, Susanne Schwenzer, Laura Crossey, Kenneth E. Herkenhoff, Jeffrey Johnson, Horton Newsom, Nina Lanza, William Rapin, Walter Goetz, Pierre-Yves Meslin, John Bridges, Ryan Anderson, Gael David, S M R Turner, M T Thorpe, Linda Kah, Jens Frydenvang, Rachel Kronyak, G. Caravaca, Ann Ollila, Stephane Le Mouelic, M Nellessen, Megan Hoffman, Deirdra Fey, Agnes Cousin, Roger Wiens, Sam Clegg, Sylvestre Maurice, Olivier Gasnault, Dorothy Delapp, A. Reyes-Newell
By
Natural Hazards Mission Area, Astrogeology Science Center

In situ recording of Mars soundscape In situ recording of Mars soundscape

Prior to the Perseverance rover landing, the acoustic environment of Mars was unknown. Models predicted that: (i) atmospheric turbulence changes at centimeter scales or smaller at the point where molecular viscosity converts kinetic energy into heat1, (ii) the speed of sound varies at the surface with frequency2,3, and (iii) high frequency waves are strongly attenuated with distance in...
Authors
Sylvestre Maurice, Baptiste Chide, Naomi Murdoch, Ralph Lorenz, David Mimoun, Roger Wiens, Alexander Stott, X. Jacob, T. Bertrand, F. Montmessin, Nina Lanza, C. Alvarez-Llamas, S. Angel, M. Aung, J. Balaram, O. Beyssac, A. Cousin, G. Delory, O. Forni, T. Fouchet, O. Gasnault, H. Grip, M. Hecht, J. Hoffman, J. Laserna, J. Lasue, J. Maki, J. McClean, P. Meslin, S. Le Mouélic, A. Munguira, C. Newman, J. Rodriguez Manfredi, J. Moros, A. Ollila, P. Pilleri, S.E. Schröder, M. de la Torre Juarez, T. Tzanetos, K. Stack, K. Farley, K. Williford, T. Acosta-Maeda, Ryan Anderson, D.M. Applin, G. Arana, M. Bassas-Portus, R. Beal, P.S.A. Beck, K. Benzerara, S. Bernard, P. Bernardi, T. Bosak, B. Bousquet, A.J. Brown, A. Cadu, P. Caïs, K. Castro, E. Clavé, S. Clegg, E. Cloutis, S. Connell, A. Debus, E. Dehouck, D. Delapp, C. Donny, A. Dorresoundiram, G. Dromart, B. Dubois, C. Fabre, A. Fau, W. Fischer, R. Francis, J. Frydenvang, Travis Gabriel, E. Gibbons, I. Gontijo, J. R. Johnson, H. Kalucha, E. Kelly, E. Knutsen, G. Lacombe, C. Legett, R. Leveille, E. Lewin, G. Lopez-Reyes, E. Lorigny, J. Madariaga, M. Madsen, S. Madsen, L. Mandon, N. Mangold, M. Mann, J.-A. Manrique, J. Martinez-Frias, L.E. Mayhew, F. Meunier, T. McConnochie, S. M. McLennan, G. Montagnac, V. Mousset, T. Nelson, R. Newell, Y. Parot, C. Pilorget, P. Pinet, G. Pont, C. Quantin-Nataf, B. Quertier, W. Rapin, A. Reyes-Newell, S. Robinson, L. Rochas, C. Royer, F. Rull, V. Sautter, S. Sharma, V. Shridar, A. Sournac, M. Toplis, I. Torre-Fdez, N. Turenne, A. Udry, M. Veneranda, D. Venhaus, D. Vogt, P. Willis
By
Natural Hazards Mission Area, Astrogeology Science Center

Introduction to the Python Hyperspectral Analysis Tool (PyHAT) Introduction to the Python Hyperspectral Analysis Tool (PyHAT)

Spectroscopic data are rich in information and are commonly used in planetary research. Many mission teams, research labs, and individual research scientists derive thematic products from multi- and hyperspectral data sets and apply spectroscopic analysis techniques to derive new understanding. The PyHAT is a powerful and versatile, free, and open-source Python library designed to...
Authors
Jason Laura, Lisa R. Gaddis, Ryan Anderson, Itiya Aneece
By
Natural Hazards Mission Area, Astrogeology Science Center, Western Geographic Science Center

Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy

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...
Authors
Ryan Anderson, Olivier Forni, Agnes Cousin, Roger Wiens, Samuel Clegg, Jens Frydenvang, Travis Gabriel, Ann Ollila, Susanne Schröder, Olivier Beyssac, Erin Gibbons, David Vogt, Elise Clave, Jose-Antonio Manrique, Carey Legett, Paolo Pilleri, Raymond Newell, Joseph Sarrao, Sylvestre Maurice, Gorka Arana, Karim Benzerara, Pernelle Bernardi, Sylvain Bernard, Bruno Bousquet, Adrian Brown, Cesar Alvarez-Llamas, Baptiste Chide, Edward Cloutis, Jade Comellas, Stephanie Connell, Erwin Dehouck, Dorothea Delapp, Ari Essunfeld, Cecile Fabre, Thierry Fouchet, Cristina Garcia, Laura Garcia-Gomez, Patrick Gasda, Olivier Gasnault, Elisabeth Hausrath, Nina Lanza, Javier Laserna, Jeremie Lasue, Guillermo Lopez, Juan Madariaga, Lucia Mandon, Nicolas Mangold, Pierre-Yves Meslin, Marion Nachon, Anthony Nelson, Horton Newsom, Adriana Reyes-Newell, Scott Robinson, Fernando Rull, Shiv Sharma, Justin Simon, Pablo Sobron, Imanol Torre Fernandez, Arya Udry, Dawn Venhaus, Scott McLennan, Richard V. Morris, Bethany Ehlmann
By
Natural Hazards Mission Area, Astrogeology Science Center

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

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...
Authors
Roger Wiens, A. Blazon-Brown, N. Melikechi, J. Frydenvang, E. Dehouck, S. Clegg, D. Delapp, Ryan Anderson, A. Cousin, S. Maurice
By
Natural Hazards Mission Area, Astrogeology Science Center

Quantification of manganese for ChemCam Mars and laboratory spectra using a multivariate model Quantification of manganese for ChemCam Mars and laboratory spectra using a multivariate model

We report a new calibration model for manganese using the laser-induced breakdown spectroscopy instrument that is part of the ChemCam instrument suite onboard the NASA Curiosity rover. The model has been trained using an expanded set of 523 manganese-bearing rock, mineral, metal ore, and synthetic standards. The optimal calibration model uses the Partial Least Squares (PLS) and Least...
Authors
Patrick Gasda, Ryan Anderson, A. Cousin, O. Forni, S. Clegg, A. Ollila, Nina Lanza, S Lamm, Roger Wiens, Sylvestre Maurice, Olivier Gasnault, R. Beal, A. Reyes-Newell, D. Delapp
By
Natural Hazards Mission Area, Astrogeology Science Center

Alternating wet and dry depositional environments recorded in the stratigraphy of Mt Sharp at Gale Crater, Mars Alternating wet and dry depositional environments recorded in the stratigraphy of Mt Sharp at Gale Crater, Mars

The Curiosity rover is exploring Hesperian-aged stratigraphy in Gale crater, Mars, where a transition from clay-bearing units to a layered sulfate-bearing unit has been interpreted to represent a major environmental transition of unknown character. We present the first description of key facies in the sulfate-bearing unit, recently observed in the distance by the rover, and propose a...
Authors
William Rapin, Gilles Dromart, Dave Rubin, Laticia Le Deit, Nicolas Mangold, Lauren Edgar, Olivier Gasnault, Kenneth E. Herkenhoff, S. Lemouelic, Ryan Anderson, S. Maurice, V. Fox, B. Ehlmann, J. Dickson, R. Wiens
By
Natural Hazards Mission Area, Astrogeology Science Center
The Solar Eclipse and Our Place in Space

The Solar Eclipse and Our Place in Space

On April 8, 2024, millions of people across North America will have the chance to witness a rare and spectacular phenomenon – a total solar eclipse. 

Read Article
It is easier than ever to view Mars landscapes in high resolution

It is easier than ever to view Mars landscapes in high resolution

U.S. Geological Survey releases huge amounts of Mars data in ready-to-use formats. This will allow scientists and the public to view Mars at high...

Read Article
Icy Mystery

Icy Mystery

Let’s take an illustrative look at a new study that explored a recently formed crater on Mars. The new icy crater will help scientists better...

Read Article
Mars 2020 Mission: The Perseverance Rover Landing

Mars 2020 Mission: The Perseverance Rover Landing

The excitement of the Perseverance rover landing on Mars can be witnessed on NASA TV starting at 11:15 PST on February 18, 2021.

Read Article
Mars 2020 Mission to be Guided by USGS Astrogeology Maps

Mars 2020 Mission to be Guided by USGS Astrogeology Maps

FLAGSTAFF, Ariz. – When you’re planning to explore someplace new, it’s always a good idea to bring a map so you can avoid dangerous terrain. This is...

Read Article

Science and Products

USGS

Terrestrial Remote Sensing Data Ingestion with PyHAT (Python Hyperspectral Analysis Tool)

This work will make it easier to work with multiple terrestrial data sets in PyHAT, a USGS tool that enables machine learning analysis of spectral datasets
By
Community for Data Integration (CDI)
Terrestrial Remote Sensing Data Ingestion with PyHAT (Python Hyperspectral Analysis Tool)

Terrestrial Remote Sensing Data Ingestion with PyHAT (Python Hyperspectral Analysis Tool)

This work will make it easier to work with multiple terrestrial data sets in PyHAT, a USGS tool that enables machine learning analysis of spectral datasets
Learn More
Thumbnail graphic for the PyHAT software package

Python Hyperspectral Analysis Tool (PyHAT)

The Python Hyperspectral Analysis Tool (PyHAT) provides access to data processing, analysis, and machine learning capabilities for spectroscopic applications. It includes a GUI so you can get straight to analyzing data without writing any code. Or, if you are comfortable writing code, PyHAT can be imported just like any other Python package.
By
Astrogeology Science Center
Python Hyperspectral Analysis Tool (PyHAT)

Python Hyperspectral Analysis Tool (PyHAT)

The Python Hyperspectral Analysis Tool (PyHAT) provides access to data processing, analysis, and machine learning capabilities for spectroscopic applications. It includes a GUI so you can get straight to analyzing data without writing any code. Or, if you are comfortable writing code, PyHAT can be imported just like any other Python package.
Learn More

Digital Terrain Models for Mars Sinuous Ridges and Alluvial Fans Projects Digital Terrain Models for Mars Sinuous Ridges and Alluvial Fans Projects

Metadata on this main landing page applies to all DTMs listed under Child Items, apart from USGS-made DTMs [HRSC DTM_75 (centered at -28.8N, 78.6E)_USGS and CTM DTM over "Crater L" (centered at -23.1N, 74.3E)_USGS]. Metadata associated with USGS-made DTMs are located with DTM files under their respective Child Item.
By
Astrogeology Science Center
Python Hyperspectral Analysis Tool (PyHAT) Logo
Python Hyperspectral Analysis Tool (PyHAT) Logo
Python Hyperspectral Analysis Tool (PyHAT) Logo
Python Hyperspectral Analysis Tool (PyHAT) Logo

Python Hyperspectral Analysis Tool (PyHAT) Logo

Python Hyperspectral Analysis Tool (PyHAT) Logo

This a version of the logo for the Python Hyperspectral Analysis Tool (PyHAT). It is intended for use in info boxes on the USGS website. The spectrum in the graphic is a laser induced breakdown spectroscopy spectrum, plotted on a logarithmic y axis to emphasize weaker emission peaks.

By
Astrogeology Science Center
Python Hyperspectral Analysis Tool (PyHAT) Logo

Python Hyperspectral Analysis Tool (PyHAT) Logo

Python Hyperspectral Analysis Tool (PyHAT) Logo

This a version of the logo for the Python Hyperspectral Analysis Tool (PyHAT). It is intended for use in info boxes on the USGS website. The spectrum in the graphic is a laser induced breakdown spectroscopy spectrum, plotted on a logarithmic y axis to emphasize weaker emission peaks.

By
Astrogeology Science Center
screenshot of an example data table in PyHAT format
Python Hyperspectral Analysis Tool (PyHAT) Data Format Example
Python Hyperspectral Analysis Tool (PyHAT) Data Format Example
screenshot of an example data table in PyHAT format

Python Hyperspectral Analysis Tool (PyHAT) Data Format Example

Python Hyperspectral Analysis Tool (PyHAT) Data Format Example

Screenshot showing the simple data format used by the Python Hyperspectral Analysis Tool (PyHAT). Spectra are stored in rows of the table, along with their associated metadata and compositional information.

By
Astrogeology Science Center
screenshot of an example data table in PyHAT format

Python Hyperspectral Analysis Tool (PyHAT) Data Format Example

Python Hyperspectral Analysis Tool (PyHAT) Data Format Example

Screenshot showing the simple data format used by the Python Hyperspectral Analysis Tool (PyHAT). Spectra are stored in rows of the table, along with their associated metadata and compositional information.

By
Astrogeology Science Center
CRISM mineral map image, with Red = Olivine, Green = High-Ca Pyroxene, and Blue = Low-Ca pyroxene
Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars
Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars
CRISM mineral map image, with Red = Olivine, Green = High-Ca Pyroxene, and Blue = Low-Ca pyroxene

Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars

Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars

This figure shows an example mineral parameter map image generated using PyHAT. The area in this Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) image is Jezero crater, the landing site of NASA's Mars Perseverance rover.

By
Astrogeology Science Center, Astrogeology Science Center
CRISM mineral map image, with Red = Olivine, Green = High-Ca Pyroxene, and Blue = Low-Ca pyroxene

Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars

Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars

This figure shows an example mineral parameter map image generated using PyHAT. The area in this Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) image is Jezero crater, the landing site of NASA's Mars Perseverance rover.

By
Astrogeology Science Center, Astrogeology Science Center
Graph of PCA scores, color coded by Fe2O3T content, and the loading vectors used to calculate the scores.
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example
Graph of PCA scores, color coded by Fe2O3T content, and the loading vectors used to calculate the scores.

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum.

By
Astrogeology Science Center
Graph of PCA scores, color coded by Fe2O3T content, and the loading vectors used to calculate the scores.

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum.

By
Astrogeology Science Center
Plot of PCA scores and loadings. The points in the scores plot are color coded based on the cluster assigned by k-means
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example
Plot of PCA scores and loadings. The points in the scores plot are color coded based on the cluster assigned by k-means

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum.

By
Astrogeology Science Center
Plot of PCA scores and loadings. The points in the scores plot are color coded based on the cluster assigned by k-means

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum.

By
Astrogeology Science Center
Scatter plot showing PCA scores as points. Several points are marked in red as outliers.
Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example
Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example
Scatter plot showing PCA scores as points. Several points are marked in red as outliers.

Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example

Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example

This figure shows an example of outlier identification using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum. Dimensionality was then reduced using principal components analysis (PCA).

By
Astrogeology Science Center
Scatter plot showing PCA scores as points. Several points are marked in red as outliers.

Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example

Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example

This figure shows an example of outlier identification using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum. Dimensionality was then reduced using principal components analysis (PCA).

By
Astrogeology Science Center
Plot showing the training set and cross validation error vs number of components for a PLS model predicting CaO
Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example
Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example
Plot showing the training set and cross validation error vs number of components for a PLS model predicting CaO

Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example

Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example

This figure shows the results of cross-validating a Partial Least Squares (PLS) model to predict the abundance of CaO in geologic targets using PyHAT. Cross validation is necessary to optimize the parameters of a regression algorithm to avoid overfitting.

By
Astrogeology Science Center
Plot showing the training set and cross validation error vs number of components for a PLS model predicting CaO

Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example

Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example

This figure shows the results of cross-validating a Partial Least Squares (PLS) model to predict the abundance of CaO in geologic targets using PyHAT. Cross validation is necessary to optimize the parameters of a regression algorithm to avoid overfitting.

By
Astrogeology Science Center
Plot showing the LIBS spectrum of basalt, with colored lines approximating the baseline using different algorithms.
Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example
Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example
Plot showing the LIBS spectrum of basalt, with colored lines approximating the baseline using different algorithms.

Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example

Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example

This figure shows an example spectrum plot generated using PyHAT. The black line is a laser induced breakdown spectroscopy (LIBS) spectrum of a basalt sample. The colored lines show the baseline estimated using several different algorithms. 

By
Astrogeology Science Center
Plot showing the LIBS spectrum of basalt, with colored lines approximating the baseline using different algorithms.

Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example

Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example

This figure shows an example spectrum plot generated using PyHAT. The black line is a laser induced breakdown spectroscopy (LIBS) spectrum of a basalt sample. The colored lines show the baseline estimated using several different algorithms. 

By
Astrogeology Science Center
Scatter plot comparing predicted vs actual CaO content for a set of spectra of geologic targets using two regression models
Python Hyperspectral Analysis Tool (PyHAT) Regression Example
Python Hyperspectral Analysis Tool (PyHAT) Regression Example
Scatter plot comparing predicted vs actual CaO content for a set of spectra of geologic targets using two regression models

Python Hyperspectral Analysis Tool (PyHAT) Regression Example

Python Hyperspectral Analysis Tool (PyHAT) Regression Example

This figure compares the results of two regression models to predict the abundance of CaO in geologic standards based on their laser induced breakdown spectroscopy (LIBS) spectra using PyHAT. The horizontal axis is the independently measured CaO abundance, the vertical axis is the abundance predicted by the models.

By
Astrogeology Science Center, Astrogeology Science Center
Scatter plot comparing predicted vs actual CaO content for a set of spectra of geologic targets using two regression models

Python Hyperspectral Analysis Tool (PyHAT) Regression Example

Python Hyperspectral Analysis Tool (PyHAT) Regression Example

This figure compares the results of two regression models to predict the abundance of CaO in geologic standards based on their laser induced breakdown spectroscopy (LIBS) spectra using PyHAT. The horizontal axis is the independently measured CaO abundance, the vertical axis is the abundance predicted by the models.

By
Astrogeology Science Center, Astrogeology Science Center
Filter Total Items: 32

Python Hyperspectral Analysis Tool (PyHAT) user guide Python Hyperspectral Analysis Tool (PyHAT) user guide

This report is a user guide for the 0.1.2 release of the Python Hyperspectral Analysis Tool (PyHAT) and its graphical user interface (GUI). The GUI is intended to provide an intuitive front end to allow users to apply sophisticated preprocessing and analysis methods to spectroscopic data. Though the PyHAT package has been developed with a particular focus on laser-induced breakdown...
Authors
Ryan Anderson, Itiya Aneece, Travis Gabriel
By
Natural Hazards Mission Area, Astrogeology Science Center, Community for Data Integration (CDI)

Intense alteration on early Mars revealed by high-aluminum rocks at Jezero Crater Intense alteration on early Mars revealed by high-aluminum rocks at Jezero Crater

The NASA Perseverance rover discovered light-toned float rocks scattered across the surface of Jezero crater that are particularly rich in alumina ( ~ 35 wt% Al2O3) and depleted in other major elements (except silica). These unique float rocks have heterogeneous mineralogy ranging from kaolinite/halloysite-bearing in hydrated samples, to spinel-bearing in dehydrated samples also...
Authors
C. Royer, C.C. Bedford, J.R. Johnson, B.H.N. Horgan, A. Broz, O. Forni, S. Connell, R.C. Wiens, L. Mandon, B.S. Kathir, E.M. Hausrath, A. Udry, J.M. Madariaga, E. Dehouck, Ryan Anderson, P.S.A. Beck, O. Beyssac, É. Clavé, S.M. Clegg, E. Cloutis, T. Fouchet, Travis Gabriel, B.J. Garczynski, A. Klidaras, H.T. Manelski, L.E. Mayhew, J. Nunez, A.M. Ollila, S.E. Schröder, J.I. Simon, U. Wolf, K.M. Stack, A. Cousin, S. Maurice
By
Natural Hazards Mission Area, Astrogeology Science Center

Regolith of the crater floor units, Jezero crater, Mars: Textures, composition and implications for provenance Regolith of the crater floor units, Jezero crater, Mars: Textures, composition and implications for provenance

A multi-instrument study of the regolith of Jezero crater floor units by the Perseverance rover has identified three types of regolith: fine-grained, coarse-grained, and mixed-type. Mastcam-Z, WATSON, and SuperCam RMI were used to characterize regolith texture, particle size, and roundedness where possible. Mastcam-Z multispectral and SuperCam LIBS data were used to constrain the...
Authors
Alicia Vaughan, Michelle Minitti, Emily Cardarelli, Jeffrey Johnson, Linda Kah, Paolo Pilleri, Mellisa Rice, Mark Sephton, Briony Horgan, Roger Wiens, R. Yingst, Maria-Paz Zorzano Mier, Ryan Anderson, James F. III Bell, Adrian Brown, Edward Cloutis, Agnes Cousin, Kenneth E. Herkenhoff, Elisabeth Housrath, Alexander Hayes, Kjartan Kinch, Marco Merusi, Chase Million, Robert Sullivan, Sandra Siljestrom, Michael St. Clair
By
Natural Hazards Mission Area, Astrogeology Science Center

Spatial and temporal distribution of sinuous ridges in southeastern Terra Sabaea and the northern region of Hellas Planitia, Mars Spatial and temporal distribution of sinuous ridges in southeastern Terra Sabaea and the northern region of Hellas Planitia, Mars

Sinuous ridges are an important yet understudied component of Mars' hydrologic history. We have produced a map of sinuous ridges, valleys and channels, and tectonic ridges across southeastern Terra Sabaea and into northern Hellas Planitia (10°-45° S, 35°-80° E) using a CTX mosaic. Although we mapped different types of ridges and negative relief features, the focus of this paper are the...
Authors
Amber Gullikson, Ryan Anderson, Rebecca Williams
By
Natural Hazards Mission Area, Astrogeology Science Center

Morphology and paleohydrology of intracrater alluvial fans north of Hellas Basin, Mars Morphology and paleohydrology of intracrater alluvial fans north of Hellas Basin, Mars

Alluvial fans and sinuous ridges are both important records of the history of fluvial activity on Mars, and they often occur together. We present observations of alluvial fans, many of which exhibit inverted relief, in five craters in the region north of Hellas basin. The observed fans ranged in size from ~10 to 820 km2. We identified three primary fan surface morphology classes (chute...
Authors
Ryan Anderson, Rebecca Williams, Amber Gullikson, William Nelson
By
Natural Hazards Mission Area, Astrogeology Science Center

Overview of the morphology and chemistry of diagenetic features in the clay-rich Glen Torridon Unit of Gale Crater, Mars Overview of the morphology and chemistry of diagenetic features in the clay-rich Glen Torridon Unit of Gale Crater, Mars

The clay-rich Glen Torridon region of Gale crater, Mars, was explored between sols 2300 and 3007. Here, we analyzed the diagenetic features observed by Curiosity, including veins, cements, nodules, and nodular bedrock, using the ChemCam, Mastcam, and Mars Hand Lens Imager instruments. We discovered many diagenetic features in Glen Torridon, including dark-toned iron- and manganese-rich...
Authors
Patrick Gasda, Jade Comellas, A Essunfeld, D. Das, Alex B Bryk, Erwin Dehouck, Susanne Schwenzer, Laura Crossey, Kenneth E. Herkenhoff, Jeffrey Johnson, Horton Newsom, Nina Lanza, William Rapin, Walter Goetz, Pierre-Yves Meslin, John Bridges, Ryan Anderson, Gael David, S M R Turner, M T Thorpe, Linda Kah, Jens Frydenvang, Rachel Kronyak, G. Caravaca, Ann Ollila, Stephane Le Mouelic, M Nellessen, Megan Hoffman, Deirdra Fey, Agnes Cousin, Roger Wiens, Sam Clegg, Sylvestre Maurice, Olivier Gasnault, Dorothy Delapp, A. Reyes-Newell
By
Natural Hazards Mission Area, Astrogeology Science Center

In situ recording of Mars soundscape In situ recording of Mars soundscape

Prior to the Perseverance rover landing, the acoustic environment of Mars was unknown. Models predicted that: (i) atmospheric turbulence changes at centimeter scales or smaller at the point where molecular viscosity converts kinetic energy into heat1, (ii) the speed of sound varies at the surface with frequency2,3, and (iii) high frequency waves are strongly attenuated with distance in...
Authors
Sylvestre Maurice, Baptiste Chide, Naomi Murdoch, Ralph Lorenz, David Mimoun, Roger Wiens, Alexander Stott, X. Jacob, T. Bertrand, F. Montmessin, Nina Lanza, C. Alvarez-Llamas, S. Angel, M. Aung, J. Balaram, O. Beyssac, A. Cousin, G. Delory, O. Forni, T. Fouchet, O. Gasnault, H. Grip, M. Hecht, J. Hoffman, J. Laserna, J. Lasue, J. Maki, J. McClean, P. Meslin, S. Le Mouélic, A. Munguira, C. Newman, J. Rodriguez Manfredi, J. Moros, A. Ollila, P. Pilleri, S.E. Schröder, M. de la Torre Juarez, T. Tzanetos, K. Stack, K. Farley, K. Williford, T. Acosta-Maeda, Ryan Anderson, D.M. Applin, G. Arana, M. Bassas-Portus, R. Beal, P.S.A. Beck, K. Benzerara, S. Bernard, P. Bernardi, T. Bosak, B. Bousquet, A.J. Brown, A. Cadu, P. Caïs, K. Castro, E. Clavé, S. Clegg, E. Cloutis, S. Connell, A. Debus, E. Dehouck, D. Delapp, C. Donny, A. Dorresoundiram, G. Dromart, B. Dubois, C. Fabre, A. Fau, W. Fischer, R. Francis, J. Frydenvang, Travis Gabriel, E. Gibbons, I. Gontijo, J. R. Johnson, H. Kalucha, E. Kelly, E. Knutsen, G. Lacombe, C. Legett, R. Leveille, E. Lewin, G. Lopez-Reyes, E. Lorigny, J. Madariaga, M. Madsen, S. Madsen, L. Mandon, N. Mangold, M. Mann, J.-A. Manrique, J. Martinez-Frias, L.E. Mayhew, F. Meunier, T. McConnochie, S. M. McLennan, G. Montagnac, V. Mousset, T. Nelson, R. Newell, Y. Parot, C. Pilorget, P. Pinet, G. Pont, C. Quantin-Nataf, B. Quertier, W. Rapin, A. Reyes-Newell, S. Robinson, L. Rochas, C. Royer, F. Rull, V. Sautter, S. Sharma, V. Shridar, A. Sournac, M. Toplis, I. Torre-Fdez, N. Turenne, A. Udry, M. Veneranda, D. Venhaus, D. Vogt, P. Willis
By
Natural Hazards Mission Area, Astrogeology Science Center

Introduction to the Python Hyperspectral Analysis Tool (PyHAT) Introduction to the Python Hyperspectral Analysis Tool (PyHAT)

Spectroscopic data are rich in information and are commonly used in planetary research. Many mission teams, research labs, and individual research scientists derive thematic products from multi- and hyperspectral data sets and apply spectroscopic analysis techniques to derive new understanding. The PyHAT is a powerful and versatile, free, and open-source Python library designed to...
Authors
Jason Laura, Lisa R. Gaddis, Ryan Anderson, Itiya Aneece
By
Natural Hazards Mission Area, Astrogeology Science Center, Western Geographic Science Center

Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy

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...
Authors
Ryan Anderson, Olivier Forni, Agnes Cousin, Roger Wiens, Samuel Clegg, Jens Frydenvang, Travis Gabriel, Ann Ollila, Susanne Schröder, Olivier Beyssac, Erin Gibbons, David Vogt, Elise Clave, Jose-Antonio Manrique, Carey Legett, Paolo Pilleri, Raymond Newell, Joseph Sarrao, Sylvestre Maurice, Gorka Arana, Karim Benzerara, Pernelle Bernardi, Sylvain Bernard, Bruno Bousquet, Adrian Brown, Cesar Alvarez-Llamas, Baptiste Chide, Edward Cloutis, Jade Comellas, Stephanie Connell, Erwin Dehouck, Dorothea Delapp, Ari Essunfeld, Cecile Fabre, Thierry Fouchet, Cristina Garcia, Laura Garcia-Gomez, Patrick Gasda, Olivier Gasnault, Elisabeth Hausrath, Nina Lanza, Javier Laserna, Jeremie Lasue, Guillermo Lopez, Juan Madariaga, Lucia Mandon, Nicolas Mangold, Pierre-Yves Meslin, Marion Nachon, Anthony Nelson, Horton Newsom, Adriana Reyes-Newell, Scott Robinson, Fernando Rull, Shiv Sharma, Justin Simon, Pablo Sobron, Imanol Torre Fernandez, Arya Udry, Dawn Venhaus, Scott McLennan, Richard V. Morris, Bethany Ehlmann
By
Natural Hazards Mission Area, Astrogeology Science Center

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

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...
Authors
Roger Wiens, A. Blazon-Brown, N. Melikechi, J. Frydenvang, E. Dehouck, S. Clegg, D. Delapp, Ryan Anderson, A. Cousin, S. Maurice
By
Natural Hazards Mission Area, Astrogeology Science Center

Quantification of manganese for ChemCam Mars and laboratory spectra using a multivariate model Quantification of manganese for ChemCam Mars and laboratory spectra using a multivariate model

We report a new calibration model for manganese using the laser-induced breakdown spectroscopy instrument that is part of the ChemCam instrument suite onboard the NASA Curiosity rover. The model has been trained using an expanded set of 523 manganese-bearing rock, mineral, metal ore, and synthetic standards. The optimal calibration model uses the Partial Least Squares (PLS) and Least...
Authors
Patrick Gasda, Ryan Anderson, A. Cousin, O. Forni, S. Clegg, A. Ollila, Nina Lanza, S Lamm, Roger Wiens, Sylvestre Maurice, Olivier Gasnault, R. Beal, A. Reyes-Newell, D. Delapp
By
Natural Hazards Mission Area, Astrogeology Science Center

Alternating wet and dry depositional environments recorded in the stratigraphy of Mt Sharp at Gale Crater, Mars Alternating wet and dry depositional environments recorded in the stratigraphy of Mt Sharp at Gale Crater, Mars

The Curiosity rover is exploring Hesperian-aged stratigraphy in Gale crater, Mars, where a transition from clay-bearing units to a layered sulfate-bearing unit has been interpreted to represent a major environmental transition of unknown character. We present the first description of key facies in the sulfate-bearing unit, recently observed in the distance by the rover, and propose a...
Authors
William Rapin, Gilles Dromart, Dave Rubin, Laticia Le Deit, Nicolas Mangold, Lauren Edgar, Olivier Gasnault, Kenneth E. Herkenhoff, S. Lemouelic, Ryan Anderson, S. Maurice, V. Fox, B. Ehlmann, J. Dickson, R. Wiens
By
Natural Hazards Mission Area, Astrogeology Science Center
The Solar Eclipse and Our Place in Space

The Solar Eclipse and Our Place in Space

On April 8, 2024, millions of people across North America will have the chance to witness a rare and spectacular phenomenon – a total solar eclipse. 

Read Article
It is easier than ever to view Mars landscapes in high resolution

It is easier than ever to view Mars landscapes in high resolution

U.S. Geological Survey releases huge amounts of Mars data in ready-to-use formats. This will allow scientists and the public to view Mars at high...

Read Article
Icy Mystery

Icy Mystery

Let’s take an illustrative look at a new study that explored a recently formed crater on Mars. The new icy crater will help scientists better...

Read Article
Mars 2020 Mission: The Perseverance Rover Landing

Mars 2020 Mission: The Perseverance Rover Landing

The excitement of the Perseverance rover landing on Mars can be witnessed on NASA TV starting at 11:15 PST on February 18, 2021.

Read Article
Mars 2020 Mission to be Guided by USGS Astrogeology Maps

Mars 2020 Mission to be Guided by USGS Astrogeology Maps

FLAGSTAFF, Ariz. – When you’re planning to explore someplace new, it’s always a good idea to bring a map so you can avoid dangerous terrain. This is...

Read Article
Was this page helpful?