Increased Fluid Flow Activity in Shallow Sediments at the 3 km Long Hugin Fracture in the Central North Sea

Abstract

The North Sea hosts a wide variety of seafloor seeps that may be important for transfer of chemical species, such as methane, from the Earth's interior to its exterior. Here we provide geochemical and geophysical evidence for fluid flow within shallow sediments at the recently discovered, 3 km long Hugin Fracture in the Central North Sea. Although venting of gas bubbles was not observed, concentrations of dissolved methane were significantly elevated (up to six‐times background values) in the water column at various locations above the fracture, and microbial mats that form in the presence of methane were observed at the seafloor. Seismic amplitude anomalies revealed a bright spot at a fault bend that may be the source of the water column methane. Sediment porewaters recovered in close proximity to the Hugin Fracture indicate the presence of fluids from two different shallow (<550 m) sources: (i) a reduced fluid characterized by elevated methane concentrations and/or high levels of dissolved sulfide (up to 6 mmol L−1), and (ii) a low‐chlorinity fluid (Cl ∼305 mmol L−1) that has low levels of dissolved methane and/or sulfide. The area of the seafloor affected by the presence of methane‐enriched fluids is similar to the footprint of seepage from other morphological features in the North Sea.

Geological and biological diversity of seeps in the Sea of Marmara

“…Seep activity seems well-rooted over all the studied areas and locally connected to tectonic and sedimentological processes (Dupré et al 2015;Grall et al, 2018a;Henry et al, 2018). Structural control of seeps has already been established in many other cold seep contexts (Orange et al, 1999;Eichhubl et al, 2000;Law et al, 2010;Sun et al, 2012;Lichtschlag et al, 2018). The faults, fracture networks and up-fault permeability are the most important factors controlling distribution and temporal and spatial variability of seeps (Talukder, 2012).…”

MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration

“…The images of the first dataset were captured in the Central North Sea near the Sleipner CO2 storage site [ 26 ]. It comprises 6321 images of size 2448 × 2048 pixels (see Fig 5a ).…”