English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Identifying the locations of hydrothermal chimneys across mapped areas of seafloor spreading ridges unlocks the ability to research questions about their correlations to geology, the cooling of the lithosphere, and deep-sea biogeography. We developed a Chimney Identification Tool (CIT) that utilizes a Convolutional Neural Network (CNN) to classify 1 m gridded AUV bathymetry and identify the locations of hydrothermal vent chimneys. A CNN is a type of Machine-Learning model that is able to classify raster data based on the shapes and textures in the input, making it ideal for this task. The criteria that have been used in previous manual classifications of chimneys have focused on the round base and spire shape of the features, and are not easily quantifiable. Machine-Learning techniques have previously been implemented with sonar data to classify seafloor geology, but this is the first application of these methods to hydrothermal systems. In developing the CIT, we compiled the bathymetry data from two rasters from the Endeavor Ridge—each gridded at a 1 m resolution—containing 34 locations of known hydrothermal chimneys, and from the 92° W segment of the Galapagos Spreading Center (GSC) containing 14. The CIT produced a primary group of outputs with 96% agreement with the manual classification; moreover, it correctly caught 29 of the 34 known chimneys from Endeavor and 10 of the 14 from the GSC. The CIT is trained to identify features with the characteristic shape of a hydrothermal vent chimney; therefore, it is susceptible to the misclassification of unusually shaped cases, given the limited training data. As a result, to provide the option of having a more inclusive application, the CIT also produced a secondary group of output locations with 61% agreement with the manual classification; moreover, it caught three of the four additional known chimneys from the GSC and four of the five from Endeavor. The CIT will be used in future investigations where an inventory of individual chimneys is important, such as the cataloguing of off-axis hydrothermal venting and the investigation of chimney distribution in connection to seafloor eruptions.

Chimney Identification Tool for Automated Detection of Hydrothermal Chimneys from High-Resolution Bathymetry Using Machine Learning