Skip to main content
U.S. flag

An official website of the United States government

Multimedia

Welcome to the Astrogeology Multimedia Gallery. Browse here for some of our available imagery, educational videos, and audios. 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.

Images

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
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. 

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. 

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
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.

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.

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
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.

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.

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
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).

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).

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
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.

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.

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
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.

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.

Videos

Demo showing how to create unit polygons using the PGM toolbox Creating and editing Geologic Units using the PGM Toolbox
Creating and editing Geologic Units using the PGM Toolbox
Creating and editing Geologic Units using the PGM Toolbox

In this demonstration video, you will learn how to create and update geologic unit polygons using the PGM Toolbox Build Polygons tool. The PGM toolbox is online.

In this demonstration video, you will learn how to create and update geologic unit polygons using the PGM Toolbox Build Polygons tool. The PGM toolbox is online.

Thumbnail image for a video showing a computer simulation of two planets colliding and merging. Two planets merging by giant impact
Two planets merging by giant impact
Two planets merging by giant impact

Computer simulation of two planets undergoing a giant impact that results in a merger (accretion). The larger (target) body is one tenth the mass of the Earth and the smaller (impactor) body is 70% the mass of the target. The planets are colliding at 1.08 times their mutual escape velocity, which equates to 3.63 km/s.

Computer simulation of two planets undergoing a giant impact that results in a merger (accretion). The larger (target) body is one tenth the mass of the Earth and the smaller (impactor) body is 70% the mass of the target. The planets are colliding at 1.08 times their mutual escape velocity, which equates to 3.63 km/s.

Thumbnail image for a video showing a computer simulation of two planets colliding but not merging. Two planets undergoing a hit-and-run impact
Two planets undergoing a hit-and-run impact
Two planets undergoing a hit-and-run impact

Computer simulation of two planets undergoing a hit-and-run giant impact. This style of collision comprises around half of the giant impacts expected to occur during the latter stages of Solar System formation. The larger (target) body is one tenth the mass of the Earth and the smaller (impactor) body is 70% the mass of the target.

Computer simulation of two planets undergoing a hit-and-run giant impact. This style of collision comprises around half of the giant impacts expected to occur during the latter stages of Solar System formation. The larger (target) body is one tenth the mass of the Earth and the smaller (impactor) body is 70% the mass of the target.

Thumbnail image for a video showing a computer simulation of two planets colliding and being disrupted. The disruption of two planets in a giant impact
The disruption of two planets in a giant impact
The disruption of two planets in a giant impact

Computer simulation of two planets undergoing a disruptive giant impact. Disruptive collisions are not expected to be common in Solar System formation and due to numerical effects, the amount of disruption shown here is likely overestimated. The larger (target) body is one tenth the mass of the Earth and the smaller (impactor) body is 70% the mass of the target.

Computer simulation of two planets undergoing a disruptive giant impact. Disruptive collisions are not expected to be common in Solar System formation and due to numerical effects, the amount of disruption shown here is likely overestimated. The larger (target) body is one tenth the mass of the Earth and the smaller (impactor) body is 70% the mass of the target.

Simulated view of Valles Marineris, Mars showing areas with CTX topography Flying Over Valles Marineris, Mars with Analysis-Ready Data
Flying Over Valles Marineris, Mars with Analysis-Ready Data
Flying Over Valles Marineris, Mars with Analysis-Ready Data

Flyover of Valles Marineris, the "Grand Canyon" of Mars, highlighting two analysis-ready datasets provided by USGS. The canyon is more than 4,000 km (2,500 miles) long and up to 7 km (23,000 ft) deep.

Flyover of Valles Marineris, the "Grand Canyon" of Mars, highlighting two analysis-ready datasets provided by USGS. The canyon is more than 4,000 km (2,500 miles) long and up to 7 km (23,000 ft) deep.

Demo: Creating custom projections in ArcGIS Pro Demo: Creating custom projections in ArcGIS Pro
Demo: Creating custom projections in ArcGIS Pro
Demo: Creating custom projections in ArcGIS Pro

In this demo, you will learn how to create a custom projection in ArcGIS Pro, using data that is not located on Earth. For this example, we will use the Lunar Reconnaissance Orbiter (LRO) Wide Angle Camera (WAC) mosaic of the Moon, and create custom polar and equatorial projections.

In this demo, you will learn how to create a custom projection in ArcGIS Pro, using data that is not located on Earth. For this example, we will use the Lunar Reconnaissance Orbiter (LRO) Wide Angle Camera (WAC) mosaic of the Moon, and create custom polar and equatorial projections.

Audio

a curvy ridge of loose rocks and gravel sit in the foreground with a glacier in the background
A simple esker in Iceland
A simple esker in Iceland
Terrestrial Analog - Meet Lauren
Terrestrial Analog - Meet Lauren
a curvy ridge of loose rocks and gravel sit in the foreground with a glacier in the background
A simple esker in Iceland
A simple esker in Iceland
Terrestrial Analog - Meet Lauren

I'm Lauren Edgar. I'm a research geologist at the USGS astrogeology Science Center here in Flagstaff AZ

a curvy ridge of loose rocks and gravel sit in the foreground with a glacier in the background
A simple esker in Iceland
A simple esker in Iceland

I'm Lauren Edgar. I'm a research geologist at the USGS astrogeology Science Center here in Flagstaff AZ

a curvy ridge of loose rocks and gravel sit in the foreground with a glacier in the background
A simple esker in Iceland
A simple esker in Iceland
Terrestrial Analog - Meet Kristen
Terrestrial Analog - Meet Kristen
a curvy ridge of loose rocks and gravel sit in the foreground with a glacier in the background
A simple esker in Iceland
A simple esker in Iceland
Terrestrial Analog - Meet Kristen

My name is Kristen Bennett. I'm at the Astrogeology Science Center and I've been there since 2018.

My name is Kristen Bennett. I'm at the Astrogeology Science Center and I've been there since 2018.