World map showing distribution of SEAfloor FLuid Expulsion Anomalies (SEAFLEASs)
SEAFLEA observation locations plotted in red showing distribution mostly on continental margins and global distribution biased by the large amount of observation around North America. This paper reports on using machine learning to predict and validate the probability of encountering SEAFLEAs globally. Credit: Phrampus et al. [2020], Figure 1a
Source: Geochemistry, Geophysics, Geosystems

Fluid migration in marine sediments is a widespread but still poorly understood process. The presence of focus fluid emissions is relevant because of the related gas emission and carbon budget estimate, for local ecosystem and for potential risk, as fluid emission may trigger slope instabilities. Despite their relevance, an estimate of fluid emission at a global scale is still lacking.

Phrampus et al. [2020] present the first database of focused fluid flow sites (e.g., cold seeps) and associated SEAfloor FLuid Expulsion Anomalies (SEAFLEASs) at a global scale. The paper presents a compilation of data and observations about their distribution.

In general, SEAFLEASs shows a random distribution along the continental margin but this observation is heavily biased toward the North America continental margin. Using a machine learning technique, the authors produced a probability model for the distribution of SEAFLEASs and this model has been validated using a random and geospatial validation technique.

This new global database, along with its prediction, covers a gap in knowledge while adopting a modern technique, and it will certainly be of interest to a wide audience of marine geologists, geophysicists and biologist.

Citation: Phrampus, B. J., Lee, T. R., & Wood, W. T. [2020]. A global probabilistic prediction of cold seeps and associated SEAfloor FLuid Expulsion Anomalies (SEAFLEAs). Geochemistry, Geophysics, Geosystems, 21, e2019GC008747. https://doi.org/10.1029/2019GC008747

—Claudio Faccenna, Editor in Chief, Geochemistry, Geophysics, Geosystems

Text © 2020. The authors. CC BY-NC-ND 3.0
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