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.
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
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 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.
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.
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).
Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example
linkThis 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
linkThis 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.
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
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.
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.
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.
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.
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.
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
I'm Lauren Edgar. I'm a research geologist at the USGS astrogeology Science Center here in Flagstaff AZ
I'm Lauren Edgar. I'm a research geologist at the USGS astrogeology Science Center here in Flagstaff AZ
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.