The Deepest Dive in Antarctica Reveals a Sea Floor Teeming With Life

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Fluid Cam – Seeing through the Ocean Surface

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The ocean is a complex system as a result of coupled forces arising from fluid dynamics. This coupling of the difference forces in the ocean (currents, surface and internal waves, swell, etc…) and the rotating Earth result in a sea surface with complex features obscuring the depths of the ocean.

Utilizing machine learning NASA AMES research scientist Ved Chirayath devised a method called fluid lensing to reduce and make these effects almost negligible. This resulted in a fluid cam capable of peering down to a depth of 10 meters which makes it very useful for studying coastal and coral systems.

I quote from NASA’s website:

So far Fluid Cam, the imaging instrument that carries the fluid lensing software, has flown only on a drone. Someday, this technique could be flown on an orbiting spacecraft to gather image data on the world’s reefs.

source: NASA via @Xavier Hoenner

Credit: Featured image is taken from the video. It is copyright of NASA.

Insight, a mission to explore Mars’ Interior

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So far the only way to get any information about the interior of rocky planets was from the moment of inertia which is related to a planets mass distribution. This only allowed to estimate if a planet has a mantle and what the estimated thicknesses of the core and mantle are. Of course, other (remote sensing) methods like potential (gravity and magnetic) fields can also give more information about a planets interior. However, the only solid way to determine the layer interface and their depth and thicknesses is seismic imaging, a popular technique used to image Earth’s subsurface and interior.

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Snow in the Desert

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source: NASA Earth Observatory

via [NASA Earth Observatory]

A Close-up Look at a Rare Underwater Eruption

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via: Woods Hole scientists took close look at largest underwater volcanic eruption in 100 years

Global SRTM Map

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The following is an 20x downsampled global Shuttle Radar Topography Mission (SRTM)  map made with Python and Basemap.

Full resolution can be found on wikimedia.

One can notice the depth of the ocean floor, on a global scale, ranges between -2000 and -6000 meters. In some regions though like the Pacific exceed this range and reach 11 kilometers below the sea surface. One such region is the deepest point on Earth, the Mariana trench as shown below followed by a map for a perspective of its location.

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Location of the Mariana trench. Wikimedia commons.

Credits: The SRTM dataset used was provided by IFREMER.

Python wrappers for the Generic Mapping Tools on the way

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An interface for interoperability between the Generic Mapping Tools (GMT), a tool used by geophysicists to create research-quality figures, and Matlab has recently been developed that allows GMT users to interact with Matlab and Matlab users to make use of GMT.

GMT wrappers are currently also being developed for the Python programming language, particularly to be used in the IPython/Jupyter notebook due to an initiative by Leonardo Uieda (and his professor Paul Wessel) whose Postdoc is being funded by the NSF. You can watch his talk at the SciPy 2017 conference below.

Some of the mentioned advantages to which I attest  include:

  • Begin and End statements are introduced to eliminate the need to pipe postscript results into a file in each line of code being written. This also eliminates the need to use the -K and -O flags which keep the file open and updates it, respectively. The -K and -O flags are a major confusion for newcomers to GMT.
  • temporary files are created under the /tmp directory, in Linux, so they will automatically be cleaned once the jupyter notebook is closed or the operating system is rebooted. Moreover, every project will have its own  directory so files from different projects don’t get mixed up.
  • GMT documentation straight in the Jupyter notebook
  • Matplotlib- & Basemap-like behaviour, particularly inline viewing of figures, using gmt.show()
  • Pythonic aliases make the compact GMT flags

To contribute: github.com/GenericMappingTools

Reference

Cook, T. (2017), A powerful new tool for research, Eos, 98, https://doi.org/10.1029/2017EO077489. Published on 17 July 2017.