A Global Probabilistic Prediction of Cold Seeps and Associated SEAfloor FLuid ExpulsionAnomalies (SEAFLEAs)

• Published in: Geochemistry, geophysics, geosystems : G3 geophysics, geosystems : G3, 2020-01, Vol.21 (1), p.n/a
• Main Authors: Phrampus, Benjamin J., Lee, Taylor R., Wood, Warren T.
• Format: Article
• Language: English
• Subjects: Active margins; Algorithms; Anomalies; Compaction; Continental margins; Distribution; fluid expulsion; Fluid flow; global prediction; machine learning; Ocean floor; Passive margins; probability; Probability theory; Regions; seepage; Seepages; Water depth
• ISSN: 1525-2027; 1525-2027
• DOI: 10.1029/2019GC008747

A newly compiled, open‐source database of focused fluid flow sites (e.g., cold seeps) and associated SEAfloor FLuid Expulsion Anomalies (SEAFLEAs) reveals a variable distribution of anomalies across global continental margins. The SEAFLEA distribution is heavily biased toward North American continental margins, with most observations between 100‐ and 200‐m water depth globally, and with an equal distribution between active and passive margins. Using a machine learning classification methodology based on outlier detection algorithms, we predict the probability of encountering a SEAFLEA globally. Results show the highest probability in regions with multiple SEAFLEA observations and parametrically similar regions concentrated on continental margins. In general, geologic, biologic, and chemical predictors are the best predictors of SEAFLEAs. We validate our results using a random and geospatial validation technique that reveals our methods are robust to random variations in observations, but that certain margins, such as the Svalbard Margin, represent parametrically distinct locals. These distinct regions have control over the global distribution of predicted anomalies due to their unique features. Our final prediction on a global 5 × 5 arc minute grid reveals that the average probability of encountering a SEAFLEA is 33.1 ± 17.7% on active margins and 31.2 ± 18.9% on passive margins, showing equal likelihood of encountering fluid expulsion between passive and active margins. Therefore, the lateral compaction on active margins does not increase the likelihood of fluid expulsion relative to the predominantly vertical compaction on passive margins. These results however say nothing of the fluid flux rates or density of expulsion features. Key Points New database of SEAfloor FLuid Expulsion Anomalies (SEAFLEAs) shows a variable distribution of anomalies concentrated on continental margins Using a machine learning classification method based on outlier detection algorithms we predict the probability of occurrence of SEAFLEAs Predictions reveal variable probabilities on global continental margins with no quantitative difference between active and passive margins