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Abstract Valdivia Bank (VB) is a Late Cretaceous oceanic plateau formed by volcanism from the Tristan‐Gough hotspot at the Mid‐Atlantic Ridge (MAR). To better understand its origin and evolution, magnetic data were used to generate a magnetic anomaly grid, which was inverted to determine crustal magnetization. The magnetization model reveals quasi‐linear polarity zones crossing the plateau and following expected MAR paleo‐locations, implying formation by seafloor spreading over ∼4 Myr during the formation of anomalies C34n‐C33r. Paleomagnetism and biostratigraphy data from International Ocean Discovery Program Expedition 391 confirm the magnetic interpretation. Anomaly C33r is split into two negative bands, likely by a westward ridge jump. One of these negative anomalies coincides with deep rift valleys, indicating their age and mechanism of formation. These findings imply that VB originated by seafloor spreading‐type volcanism during a plate reorganization, not from a vertical stack of lava flows as expected for a large volcano.
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This content will become publicly available on February 16, 2026
Characterizing Marine Magnetic Anomalies: A Machine Learning Approach to Advancing the Understanding of Oceanic Crust Formation
Abstract Linear magnetic anomalies (LMA), resulting from Earth's magnetic field reversals recorded by seafloor spreading serve as crucial evidence for oceanic crust formation and plate tectonics. Traditionally, LMA analysis relies on visual inspection and manual interpretation, which can be subject to biases due to the complexities of the tectonic history, uneven data coverage, and strong local anomalies associated with seamounts and fracture zones. In this study, we present a Machine learning (ML)‐based framework to identify LMA, determine their orientations and distinguish spatial patterns across oceans. The framework consists of three stages and is semi‐automated, scalable and unbiased. First, a generation network produces artificial yet realistic magnetic anomalies based on user‐specified conditions of linearity and orientation, addressing the scarcity of the labeled training dataset for supervised ML approaches. Second, a characterization network is trained on these generated magnetic anomalies to identify LMA and their orientations. Third, the detected LMA features are clustered into groups based on predicted orientations, revealing underlying spatial patterns, which are directly related to propagating ridges and tectonic activity. The application of this framework to magnetic data from seven areas in the Atlantic and Pacific oceans aligns well with established magnetic lineations and geological features, such as the Mid‐Atlantic Ridge, Reykjanes Ridge, Galapagos Spreading Center, Shatsky Rise, Juan de Fuca Ridge and even Easter Microplate and Galapagos hotspot. The proposed framework establishes a solid foundation for future data‐driven marine magnetic analyses and facilitates objective and quantitative geological interpretation, thus offering the potential to enhance our understanding of oceanic crust formation.
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- Award ID(s):
- 2153784
- PAR ID:
- 10572132
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 130
- Issue:
- 2
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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