Haroon, Amir , Paasche, Hendrik, Graber, Sebastian, Petersen, Sven , Attias, Eric, Jegen, Marion , Gehrmann, Romina, Hölz, Sebastian and Klischies, Meike (2023) Automated Seafloor Massive Sulfide Detection Through Integrated Image Segmentation and Geophysical Data Analysis: Revisiting the TAG Hydrothermal Field. Geochemistry, Geophysics, Geosystems, 24 (12). e2023GC011250. DOI 10.1029/2023GC011250.

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Abstract

Accessible seafloor minerals located near mid‐ocean ridges are noticed to mitigate the projected metal demands of the net‐zero energy transition, promoting growing interest in quantifying the global distributions of seafloor massive sulfides (SMS). Mineral potentials are commonly estimated using geophysical and geological data that lastly rely on additional confirmation studies using sparsely available, locally limited, seafloor imagery, grab samples, and coring data. This raises the challenge of linking in situ confirmation data to geophysical data acquired at disparate spatial scales to obtain quantitative mineral predictions. Although multivariate data sets for marine mineral research are incessantly acquired, robust, integrative data analysis requires cumbersome workflows and experienced interpreters. We introduce an automated two‐step machine learning approach that integrates the mound detection through image segmentation with geophysical data. SMS predictors are subsequently clustered into distinct classes to infer marine mineral potentials that help guide future exploration. The automated workflow employs a U‐Net convolutional neural network to identify mound structures in bathymetry data and distinguishes different mound classes through the classification of mound architectures and magnetic signatures. Finally, controlled source electromagnetic data are utilized together with in situ sampling data to reassess predictions of potential SMS volumes. Our study focuses on the Trans‐Atlantic Geotraverse area, which is among the most explored SMS areas worldwide and includes 15 known SMS sites. The automated workflow classifies 14 of the 15 known mounds as exploration targets of either high or medium priority. This reduces the exploration area to less than 7% of the original survey area from 49 to 3.1 km 2 .

Key Points

A two‐step machine learning workflow identifies mound structures in bathymetry data and classifies their origins based on auxiliary data
Significant increase in potential seafloor massive sulfides (SMS) edifices detected within the trans‐Atlantic geo‐traverse hydrothermal field distributed within latitudinal bands
SMS mineral potential is likely lower than previously assumed due to heterogeneously distributed mineralization within mounds

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