The Apollo 11 Traverses (left) did not travel more than ~1/10th of a mile from the LEM. The Apollo 17 Traverses (base image), on the other hand, traveled 22.2 miles in Grover. This map illustrates the difference in scale between the two missions. Photo Credit: NASA/GFSC/ASU, USGS Astrogeology
Images
Browse here for some of our available imagery. We may get permission to use some non-USGS images and these should be marked and are subject to copyright laws. USGS Astrogeology images can be freely downloaded.
The Apollo 11 Traverses (left) did not travel more than ~1/10th of a mile from the LEM. The Apollo 17 Traverses (base image), on the other hand, traveled 22.2 miles in Grover. This map illustrates the difference in scale between the two missions. Photo Credit: NASA/GFSC/ASU, USGS Astrogeology
GOES-West image of the explosive eruption of the Hunga Tonga volcano in 2022. The explosion atmospheric pressure waves that traveled around the world. Read more here.
GOES-West image of the explosive eruption of the Hunga Tonga volcano in 2022. The explosion atmospheric pressure waves that traveled around the world. Read more here.
Lunar Reconnaissance Orbiter Camera Mosaic of Gruithuisen Domes
Lunar Reconnaissance Orbiter Camera Mosaic of Gruithuisen DomesLunar Reconnaissance Orbiter Camera (LROC) mosaic of the Gruithuisen (pronounced “groot-high-sen”) domes on the Moon. These unusual high-silica volcanic features are the target of the NASA Lunar Vulkan Imaging Spectroscopy Explorer (Lunar-VISE) mission. USGS scientist Kristen Bennett is a member of the Lunar-VISE science team.
Lunar Reconnaissance Orbiter Camera Mosaic of Gruithuisen Domes
Lunar Reconnaissance Orbiter Camera Mosaic of Gruithuisen DomesLunar Reconnaissance Orbiter Camera (LROC) mosaic of the Gruithuisen (pronounced “groot-high-sen”) domes on the Moon. These unusual high-silica volcanic features are the target of the NASA Lunar Vulkan Imaging Spectroscopy Explorer (Lunar-VISE) mission. USGS scientist Kristen Bennett is a member of the Lunar-VISE science team.
Asteroid Impact Modeling Working Group Hikes into Meteor Crater
Asteroid Impact Modeling Working Group Hikes into Meteor CraterThis photograph shows members of the Asteroid Impact Modeling Working Group workshop participants descending into Meteor Crater in northern Arizona. Meteor Crater is the best-preserved asteroid impact crater on Earth. It has been used to study the effects of impact, and as a site to train astronauts.
Asteroid Impact Modeling Working Group Hikes into Meteor Crater
Asteroid Impact Modeling Working Group Hikes into Meteor CraterThis photograph shows members of the Asteroid Impact Modeling Working Group workshop participants descending into Meteor Crater in northern Arizona. Meteor Crater is the best-preserved asteroid impact crater on Earth. It has been used to study the effects of impact, and as a site to train astronauts.
USGS Astrogeology staff table at the Spring 2024 STEM event
USGS Astrogeology staff table at the Spring 2024 STEM eventUSGS Astrogeology staff table at the Spring 2024 STEM event. Photo courtesy of Lori Pigue, USGS Astrogeology.
USGS Astrogeology staff table at the Spring 2024 STEM event
USGS Astrogeology staff table at the Spring 2024 STEM eventUSGS Astrogeology staff table at the Spring 2024 STEM event. Photo courtesy of Lori Pigue, USGS Astrogeology.
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.
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.
Python Hyperspectral Analysis Tool (PyHAT) Data Format Example
Python Hyperspectral Analysis Tool (PyHAT) Data Format ExampleScreenshot 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.
Python Hyperspectral Analysis Tool (PyHAT) Data Format Example
Python Hyperspectral Analysis Tool (PyHAT) Data Format ExampleScreenshot 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.
This image was taken of the Martian surface by the NASA MSL rover on sol 4158, showing an assortment of clasts.
This image was taken of the Martian surface by the NASA MSL rover on sol 4158, showing an assortment of clasts.
Python Hyperspectral Analysis Tool (PyHAT) Banner Image
Python Hyperspectral Analysis Tool (PyHAT) Banner ImageThis image is intended as a summary/promotional image for the Python Hyperspectral Analysis Tool (PyHAT) software.
Python Hyperspectral Analysis Tool (PyHAT) Banner Image
Python Hyperspectral Analysis Tool (PyHAT) Banner ImageThis image is intended as a summary/promotional image for the Python Hyperspectral Analysis Tool (PyHAT) software.
Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars
Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, MarsThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, Mars
Python Hyperspectral Analysis Tool (PyHAT) Mineral Parameter Map Example - Jezero Crater, MarsThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot ExampleThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot Example
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis (PCA) Plot ExampleThis 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.
Image shows a poorly sorted collection of clasts, taken by the NASA Mars Curiosity rover on sol 4139.
Image shows a poorly sorted collection of clasts, taken by the NASA Mars Curiosity rover on sol 4139.
Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example
Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation ExampleThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation Example
Python Hyperspectral Analysis Tool (PyHAT) Partial Least Squares Cross Validation ExampleThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Regression Example
Python Hyperspectral Analysis Tool (PyHAT) Regression ExampleThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Regression Example
Python Hyperspectral Analysis Tool (PyHAT) Regression ExampleThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering ExampleThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering ExampleThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example
Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification ExampleThis 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).
Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example
Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification ExampleThis 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).
Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example
Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot ExampleThis 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.
Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot Example
Python Hyperspectral Analysis Tool (PyHAT) Baseline Removal Plot ExampleThis 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.
This is the small logo for the Python Hyperspectral Analysis Tool (PyHAT). It is intended for use as a thumbnail. The spectrum in the graphic is a laser induced breakdown spectroscopy spectrum, plotted on a logarithmic y axis to emphasize weaker emission peaks.
This is the small logo for the Python Hyperspectral Analysis Tool (PyHAT). It is intended for use as a thumbnail. The spectrum in the graphic is a laser induced breakdown spectroscopy spectrum, plotted on a logarithmic y axis to emphasize weaker emission peaks.
A coloring page and information about Grover, the USGS's geologic rover that went to the moon during the Apollo missions.
A coloring page and information about Grover, the USGS's geologic rover that went to the moon during the Apollo missions.
Screenshot of the user interface of the GeoSTAC project, with symbolized polygons on a Mars map (left) and a selection panel (right).
Screenshot of the user interface of the GeoSTAC project, with symbolized polygons on a Mars map (left) and a selection panel (right).
Photo of the GeoKings team taken by mentor Trent Hare in a ballroom full of other people, which is the poster session where they won their award. GeoKings from left to right: Zack Bryant, Jackson Brittain, John Cardeccia, Andrew Usvat, and Alex Poole.
Photo of the GeoKings team taken by mentor Trent Hare in a ballroom full of other people, which is the poster session where they won their award. GeoKings from left to right: Zack Bryant, Jackson Brittain, John Cardeccia, Andrew Usvat, and Alex Poole.