Skip to main content
U.S. flag

An official website of the United States government

Travis S.J. Gabriel, Ph.D.

Travis is a Research Physical Scientist at the Astrogeology Science Center.

Professional Experience

  • Science Team Member – NASA Mars 2020, Perseverance Rover

  • Science Team Member – NASA Mars Science Laboratory, Curiosity Rover

Science and Products

link
USGS

Availability, documentation, & community support for an open-source machine learning tool

We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.
By
Community for Data Integration (CDI)
link

Availability, documentation, & community support for an open-source machine learning tool

We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.
Learn More

Moon-forming impactor as a source of Earth’s basal mantle anomalies

Seismic images of Earth’s interior have revealed two continent-sized anomalies with low seismic velocities, known as the large low-velocity provinces (LLVPs), in the lowermost mantle. The LLVPs are often interpreted as intrinsically dense heterogeneities that are compositionally distinct from the surrounding mantle. Here we show that LLVPs may represent buried relics of Theia mantle material (TMM)
Authors
Qian Yuan, Mingming Li, Steven J. Desch, Byeongkwan Ko, Hongping Deng, Edward J. Garnero, Travis S. J. Gabriel, Jacob A. Kegerreis, Yoshinori Miyazaki, Vincent Eke, Paul D. Asimow
By
Natural Hazards Mission Area, Astrogeology Science Center

The role of giant impacts in planet formation

Planets are expected to conclude their growth through a series of giant impacts: energetic, global events that significantly alter planetary composition and evolution. Computer models and theory have elucidated the diverse outcomes of giant impacts in detail, improving our ability to interpret collision conditions from observations of their remnants. However, many open questions remain, as even th
Authors
Travis S. J. Gabriel, Saverio Cambioni
By
Natural Hazards Mission Area, Astrogeology Science Center

Assessment of lunar resource exploration in 2022

The idea of mining the Moon, once purely science-fiction, is now on the verge of becoming reality. Taking advantage of the resources on the Moon is part of the plans of many nations and some enterprising commercial entities; demonstrating in-situ (in place) resource utilization near the lunar south pole is an explicit goal of the United States’ Artemis program. Economic extraction and sustainable
Authors
Laszlo P. Keszthelyi, Joshua A. Coyan, Kristen A. Bennett, Lillian R. Ostrach, Lisa R. Gaddis, Travis S. J. Gabriel, Justin Hagerty
By
Natural Hazards Mission Area, Geology, Minerals, Energy, and Geophysics Science Center, Astrogeology Science Center

An examination of soil crusts on the floor of Jezero crater, Mars

Martian soils are critically important for understanding the history of Mars, past potentially habitable environments, returned samples, and future human exploration. This paper examines soil crusts on the floor of Jezero crater encountered during initial phases of the Mars 2020 mission. Soil surface crusts have been observed on Mars at other locations, starting with the two Viking Lander missions
Authors
E. M. Hausrath, C. T. Adcock, A. Bechtold, P. S. A. Beck, K. Benison, A. Brown, E. L. Cardarelli, N. A. Carman, B. Chide, J. Christian, B. C. Clark, E. Cloutis, A. Cousin, O. Forni, Travis S. J. Gabriel, O. Gasnault, M. P. Golombek, F. Gomez, M. H. Hecht, T. L. J. Henley, J. Huidobro, J. C. Johnson, M. W. M. Jones, P. B. Kelemen, A. Knight, J. A. Lasue, S. Le Mouelic, J. M. Madariaga, J. N. Maki, L. Mandon, G. Martinez, J. Martinez-Frias, T. H. McConnochie, P. -Y. Meslin, M. -P. Zorzano, H. Newsom, G. Paar, N. Randazzo, C. Royer, S. Siljestroem, M. E. Schmidt, S. Schroeder, M. A. Sephton, R. Sullivan, N. Turenne, A. Udry, S. VanBommel, A. Vaughan, R. C. Wiens, N. Williams
By
Natural Hazards Mission Area, Astrogeology Science Center

Aqueously altered igneous rocks sampled on the floor of Jezero crater, Mars

The Perseverance rover landed in Jezero crater, Mars, to investigate ancient lake and river deposits. We report observations of the crater floor, below the crater’s sedimentary delta, finding the floor consists of igneous rocks altered by water. The lowest exposed unit, informally named Séítah, is a coarsely crystalline olivine-rich rock, which accumulated at the base of a magma body. Fe-Mg carbon
Authors
K.A. Farley, K.M. Stack, D.L. Shuster, B.H.N. Horgan, J.A. Hurowitz, J. D. Tarnas, J.I. Simon, V.Z. Sun, E.L. Scheller, K.R. Moore, S.M. McLennan, P.M. Vasconcelos, R. C. Wiens, A.H. Treiman, L.E. Mayhew, O. Beyssac, T.V. Kizovski, N. J. Tosca, K.H. Williford, L.S. Crumpler, L.W. Beegle, J.F. Bell III, B.L. Ehlmann, Y. Liu, J.N. Maki, M. E. Schmidt, A.C. Allwood, H.E.F. Amundsen, R. Ghartia, T. Bosak, A.J. Brown, B. C. Clark, A. Cousin, O. Forni, Travis S. J. Gabriel, Y. Goreva, S. Gupta, S.-E. Hamran, C.D.K. Herd, K. Hickman-Lewis, J.R. Johnson, L.C. Kah, P. B. Kelemen, K.B. Kinch, L. Mandon, N. Mangold, C. Quantun-Nataf, M.S. Rice, P.S. Russell, S. Sharma, S. Siljestroem, A. Steele, R. Sullivan, M. Wadhwa, B. P. Weiss, A.J. Williams, B.V. Wogsland, P.A. Willis, T.A. Acosta-Maeda, B. Peck, K. Benzerara, S. Bernard, A.S. Burton, E. L. Cardarelli, B. Chide, E. Clave, E.A. Cloutis, A.D. Czaja, V. Debaille, E. Dehouck, A.G. Fairen, D.T. Flannery, S.Z. Fleron, T. Fouchet, J. Frydenvang, B.J. Garczynski, E.F. Gibbons, E. M. Hausrath, A.G. Hayes, J. Henneke, J.L. Jorgensen, E.M. Kelly, J. Lasue, S. Le Mouelic, J. M. Madariaga, S. Maurice, M. Merusi, P. -Y. Meslin, S.M. Milkovich, C.C. Million, R.C. Moeller, J.I. Nunez, A.M. Ollila, G. Paar, D.A. Paige, D.A.K. Pedersen, P. Pilleri, C. Pilorget, P.C. Pinet, J.W. Rice Jr., C. Royer, V. Sautter, M. Schulte, M. A. Sephton, S.K. Sharma, S.F. Sholes, N. Spanovich, M. St. Clair, C.D. Tate, K. Uckert, S.J. VanBommel, A.G. Yanchilina, M. -P. Zorzano
By
Natural Hazards Mission Area, Astrogeology Science Center

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 CO22–4. How
Authors
Sylvestre Maurice, Baptiste Chide, Naomi Murdoch, Ralph D. Lorenz, David Mimoun, Roger C. Wiens, Alexander E. Stott, X. Jacob, T. Bertrand, F. Montmessin, Nina L. Lanza, C. Alvarez-Llamas, S. M. 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. N. Maki, J. McClean, P. -Y. Meslin, S. Le Mouélic, A. Munguira, C. E. Newman, J. A. Rodríguez Manfredi, J. Moros, A. Ollila, P. Pilleri, S. E. Schröder, M. de la Torre Juárez, T. Tzanetos, K. Stack, K. Farley, K. H. 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. Brown, A. Cadu, P. Caïs, K. Castro, E. Clavé, S. M. Clegg, E. Cloutis, S. Connell, A. Debus, E. Dehouck, D. Delapp, C. Donny, A. Dorresoundiram, G. Dromart, B. Dubois, C. Fabre, A. Fau, W. F. Fischer, R. Francis, J. Frydenvang, Travis S. J. 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. M. Madariaga, M. B. 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. Aliste Nelson, R. T. 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

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 masked to r
Authors
Ryan Anderson, Olivier Forni, Agnes Cousin, Roger C. Wiens, Samuel M. Clegg, Jens Frydenvang, Travis S. J. Gabriel, Ann M. 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 J. Brown, Cesar Alvarez-Llamas, Baptiste Chide, Edward A. Cloutis, Jade Comellas, Stephanie Connell, Erwin Dehouck, Dorothea Delapp, Ari Essunfeld, Cecile Fabre, Thierry Fouchet, Cristina Garcia, Laura Garcia-Gomez, Patrick J. Gasda, Olivier Gasnault, Elisabeth Hausrath, Nina L. Lanza, Javier Laserna, Jeremie Lasue, Guillermo Lopez, Juan Manuel Madariaga, Lucia Mandon, Nicolas Mangold, Pierre-Yves Meslin, Marion Nachon, Anthony Nelson, Horton E. Newsom, Adriana Reyes-Newell, Scott Robinson, Fernando Rull, Shiv Sharma, Justin I Simon, Pablo Sobron, Imanol Torre Fernandez, Arya Udry, Dawn Venhaus, Scott McLennan, Richard V. Morris, Bethany L. Ehlmann
By
Natural Hazards Mission Area, Astrogeology Science Center

Clustering supported classification of ChemCam data from Gale crater, Mars

The Chemistry and Camera (ChemCam) instrument on board the MSL rover Curiosity has collected a very large and unique dataset of in-situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot LIBS (laser-induced breakdown spectroscopy) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a dataset is ideally suited for the appl
Authors
K. Rammelkamp, Olivier Gasnault, Olivier Forni, Candice C. Bedford, Erwin Dehouck, Agnès Cousin, Jeremie Lasue, Gaël David, Travis S. J. Gabriel, Sylvestre Maurice, Roger C. Wiens
By
Natural Hazards Mission Area, Astrogeology Science Center

Active neutron interrogation experiments and simulation verification using the SIngle-scintillator Neutron and Gamma-Ray spectrometer (SINGR) for geosciences

We present a new SIngle-scintillator Neutron and Gamma Ray spectrometer (SINGR) instrument for use with both passive and active measurement techniques. Here we discuss the application of SINGR for planetary exploration missions, however, hydrology, nuclear non-proliferation, and resource prospecting are all potential areas where the instrument could be applied. SINGR uses an elpasolite scintillato
Authors
Lena E. Heffern, Craig J. Hardgrove, Ann Parsons, E. B. Johnson, R. Starr, G. Stoddard, R. E. Blakeley, T. Prettyman, Travis S. J. Gabriel, H. Barnaby, J. Christian, M.A. Unzueta, C. Tate, A. Martin, J. Moersch
By
Natural Hazards Mission Area, 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

Science and Products

link
USGS

Availability, documentation, & community support for an open-source machine learning tool

We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.
By
Community for Data Integration (CDI)
link

Availability, documentation, & community support for an open-source machine learning tool

We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.
Learn More

Moon-forming impactor as a source of Earth’s basal mantle anomalies

Seismic images of Earth’s interior have revealed two continent-sized anomalies with low seismic velocities, known as the large low-velocity provinces (LLVPs), in the lowermost mantle. The LLVPs are often interpreted as intrinsically dense heterogeneities that are compositionally distinct from the surrounding mantle. Here we show that LLVPs may represent buried relics of Theia mantle material (TMM)
Authors
Qian Yuan, Mingming Li, Steven J. Desch, Byeongkwan Ko, Hongping Deng, Edward J. Garnero, Travis S. J. Gabriel, Jacob A. Kegerreis, Yoshinori Miyazaki, Vincent Eke, Paul D. Asimow
By
Natural Hazards Mission Area, Astrogeology Science Center

The role of giant impacts in planet formation

Planets are expected to conclude their growth through a series of giant impacts: energetic, global events that significantly alter planetary composition and evolution. Computer models and theory have elucidated the diverse outcomes of giant impacts in detail, improving our ability to interpret collision conditions from observations of their remnants. However, many open questions remain, as even th
Authors
Travis S. J. Gabriel, Saverio Cambioni
By
Natural Hazards Mission Area, Astrogeology Science Center

Assessment of lunar resource exploration in 2022

The idea of mining the Moon, once purely science-fiction, is now on the verge of becoming reality. Taking advantage of the resources on the Moon is part of the plans of many nations and some enterprising commercial entities; demonstrating in-situ (in place) resource utilization near the lunar south pole is an explicit goal of the United States’ Artemis program. Economic extraction and sustainable
Authors
Laszlo P. Keszthelyi, Joshua A. Coyan, Kristen A. Bennett, Lillian R. Ostrach, Lisa R. Gaddis, Travis S. J. Gabriel, Justin Hagerty
By
Natural Hazards Mission Area, Geology, Minerals, Energy, and Geophysics Science Center, Astrogeology Science Center

An examination of soil crusts on the floor of Jezero crater, Mars

Martian soils are critically important for understanding the history of Mars, past potentially habitable environments, returned samples, and future human exploration. This paper examines soil crusts on the floor of Jezero crater encountered during initial phases of the Mars 2020 mission. Soil surface crusts have been observed on Mars at other locations, starting with the two Viking Lander missions
Authors
E. M. Hausrath, C. T. Adcock, A. Bechtold, P. S. A. Beck, K. Benison, A. Brown, E. L. Cardarelli, N. A. Carman, B. Chide, J. Christian, B. C. Clark, E. Cloutis, A. Cousin, O. Forni, Travis S. J. Gabriel, O. Gasnault, M. P. Golombek, F. Gomez, M. H. Hecht, T. L. J. Henley, J. Huidobro, J. C. Johnson, M. W. M. Jones, P. B. Kelemen, A. Knight, J. A. Lasue, S. Le Mouelic, J. M. Madariaga, J. N. Maki, L. Mandon, G. Martinez, J. Martinez-Frias, T. H. McConnochie, P. -Y. Meslin, M. -P. Zorzano, H. Newsom, G. Paar, N. Randazzo, C. Royer, S. Siljestroem, M. E. Schmidt, S. Schroeder, M. A. Sephton, R. Sullivan, N. Turenne, A. Udry, S. VanBommel, A. Vaughan, R. C. Wiens, N. Williams
By
Natural Hazards Mission Area, Astrogeology Science Center

Aqueously altered igneous rocks sampled on the floor of Jezero crater, Mars

The Perseverance rover landed in Jezero crater, Mars, to investigate ancient lake and river deposits. We report observations of the crater floor, below the crater’s sedimentary delta, finding the floor consists of igneous rocks altered by water. The lowest exposed unit, informally named Séítah, is a coarsely crystalline olivine-rich rock, which accumulated at the base of a magma body. Fe-Mg carbon
Authors
K.A. Farley, K.M. Stack, D.L. Shuster, B.H.N. Horgan, J.A. Hurowitz, J. D. Tarnas, J.I. Simon, V.Z. Sun, E.L. Scheller, K.R. Moore, S.M. McLennan, P.M. Vasconcelos, R. C. Wiens, A.H. Treiman, L.E. Mayhew, O. Beyssac, T.V. Kizovski, N. J. Tosca, K.H. Williford, L.S. Crumpler, L.W. Beegle, J.F. Bell III, B.L. Ehlmann, Y. Liu, J.N. Maki, M. E. Schmidt, A.C. Allwood, H.E.F. Amundsen, R. Ghartia, T. Bosak, A.J. Brown, B. C. Clark, A. Cousin, O. Forni, Travis S. J. Gabriel, Y. Goreva, S. Gupta, S.-E. Hamran, C.D.K. Herd, K. Hickman-Lewis, J.R. Johnson, L.C. Kah, P. B. Kelemen, K.B. Kinch, L. Mandon, N. Mangold, C. Quantun-Nataf, M.S. Rice, P.S. Russell, S. Sharma, S. Siljestroem, A. Steele, R. Sullivan, M. Wadhwa, B. P. Weiss, A.J. Williams, B.V. Wogsland, P.A. Willis, T.A. Acosta-Maeda, B. Peck, K. Benzerara, S. Bernard, A.S. Burton, E. L. Cardarelli, B. Chide, E. Clave, E.A. Cloutis, A.D. Czaja, V. Debaille, E. Dehouck, A.G. Fairen, D.T. Flannery, S.Z. Fleron, T. Fouchet, J. Frydenvang, B.J. Garczynski, E.F. Gibbons, E. M. Hausrath, A.G. Hayes, J. Henneke, J.L. Jorgensen, E.M. Kelly, J. Lasue, S. Le Mouelic, J. M. Madariaga, S. Maurice, M. Merusi, P. -Y. Meslin, S.M. Milkovich, C.C. Million, R.C. Moeller, J.I. Nunez, A.M. Ollila, G. Paar, D.A. Paige, D.A.K. Pedersen, P. Pilleri, C. Pilorget, P.C. Pinet, J.W. Rice Jr., C. Royer, V. Sautter, M. Schulte, M. A. Sephton, S.K. Sharma, S.F. Sholes, N. Spanovich, M. St. Clair, C.D. Tate, K. Uckert, S.J. VanBommel, A.G. Yanchilina, M. -P. Zorzano
By
Natural Hazards Mission Area, Astrogeology Science Center

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 CO22–4. How
Authors
Sylvestre Maurice, Baptiste Chide, Naomi Murdoch, Ralph D. Lorenz, David Mimoun, Roger C. Wiens, Alexander E. Stott, X. Jacob, T. Bertrand, F. Montmessin, Nina L. Lanza, C. Alvarez-Llamas, S. M. 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. N. Maki, J. McClean, P. -Y. Meslin, S. Le Mouélic, A. Munguira, C. E. Newman, J. A. Rodríguez Manfredi, J. Moros, A. Ollila, P. Pilleri, S. E. Schröder, M. de la Torre Juárez, T. Tzanetos, K. Stack, K. Farley, K. H. 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. Brown, A. Cadu, P. Caïs, K. Castro, E. Clavé, S. M. Clegg, E. Cloutis, S. Connell, A. Debus, E. Dehouck, D. Delapp, C. Donny, A. Dorresoundiram, G. Dromart, B. Dubois, C. Fabre, A. Fau, W. F. Fischer, R. Francis, J. Frydenvang, Travis S. J. 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. M. Madariaga, M. B. 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. Aliste Nelson, R. T. 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

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 masked to r
Authors
Ryan Anderson, Olivier Forni, Agnes Cousin, Roger C. Wiens, Samuel M. Clegg, Jens Frydenvang, Travis S. J. Gabriel, Ann M. 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 J. Brown, Cesar Alvarez-Llamas, Baptiste Chide, Edward A. Cloutis, Jade Comellas, Stephanie Connell, Erwin Dehouck, Dorothea Delapp, Ari Essunfeld, Cecile Fabre, Thierry Fouchet, Cristina Garcia, Laura Garcia-Gomez, Patrick J. Gasda, Olivier Gasnault, Elisabeth Hausrath, Nina L. Lanza, Javier Laserna, Jeremie Lasue, Guillermo Lopez, Juan Manuel Madariaga, Lucia Mandon, Nicolas Mangold, Pierre-Yves Meslin, Marion Nachon, Anthony Nelson, Horton E. Newsom, Adriana Reyes-Newell, Scott Robinson, Fernando Rull, Shiv Sharma, Justin I Simon, Pablo Sobron, Imanol Torre Fernandez, Arya Udry, Dawn Venhaus, Scott McLennan, Richard V. Morris, Bethany L. Ehlmann
By
Natural Hazards Mission Area, Astrogeology Science Center

Clustering supported classification of ChemCam data from Gale crater, Mars

The Chemistry and Camera (ChemCam) instrument on board the MSL rover Curiosity has collected a very large and unique dataset of in-situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot LIBS (laser-induced breakdown spectroscopy) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a dataset is ideally suited for the appl
Authors
K. Rammelkamp, Olivier Gasnault, Olivier Forni, Candice C. Bedford, Erwin Dehouck, Agnès Cousin, Jeremie Lasue, Gaël David, Travis S. J. Gabriel, Sylvestre Maurice, Roger C. Wiens
By
Natural Hazards Mission Area, Astrogeology Science Center

Active neutron interrogation experiments and simulation verification using the SIngle-scintillator Neutron and Gamma-Ray spectrometer (SINGR) for geosciences

We present a new SIngle-scintillator Neutron and Gamma Ray spectrometer (SINGR) instrument for use with both passive and active measurement techniques. Here we discuss the application of SINGR for planetary exploration missions, however, hydrology, nuclear non-proliferation, and resource prospecting are all potential areas where the instrument could be applied. SINGR uses an elpasolite scintillato
Authors
Lena E. Heffern, Craig J. Hardgrove, Ann Parsons, E. B. Johnson, R. Starr, G. Stoddard, R. E. Blakeley, T. Prettyman, Travis S. J. Gabriel, H. Barnaby, J. Christian, M.A. Unzueta, C. Tate, A. Martin, J. Moersch
By
Natural Hazards Mission Area, 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