OS31B-08
High-resolution seismic attribute analysis for the detection of methane hydrate and substrate fluid migration pathways along the central U.S. Atlantic Margin

Wednesday, 16 December 2015: 09:45
3009 (Moscone West)
Jared Kluesner1, Carolyn D Ruppel2, Daniel S Brothers1, William W Danforth3, Joel H Edwards4 and Patrick E Hart5, (1)USGS Pacific Coastal and Marine Science Center Santa Cruz, Santa Cruz, CA, United States, (2)USGS Coastal and Marine Science Center Woods Hole, Woods Hole, MA, United States, (3)US Geological Survey, Woods Hole, MA, United States, (4)University of California Santa Cruz, Santa Cruz, CA, United States, (5)USGS California Water Science Center Menlo Park, Menlo Park, CA, United States
Abstract:
High-resolution multi-channel (72 channel) seismic (MCS) reflection profiles and coincident water column methane plume imagery were collected by the USGS on the U.S. mid-Atlantic margin aboard the R/V Endeavor in April 2015. The seismic data are analyzed using advanced attributes to detect and delineate the base of the gas hydrate stability (BGHS) and fluid-migration pathways associated with recently discovered seafloor methane seeps.  The sparker was operated at 2.6 kJ, and the amplitude frequency spectrum of the resulting data ranges from ~50-700 Hz, with the dominant frequency centered at 150 Hz. Using a frequency attribute workflow, we calculate and visualize changes in dominant frequency content within the seismic profiles. Laterally-distributed and abrupt high-to-low frequency changes are observed at depth. High frequencies are attenuated below this transition, which commonly mimics the seafloor and gradually shoals towards the seafloor with decreasing water depth.  The BGHS depths calculated using gas hydrate stability constraints and geothermal gradients closely coincide with these transitions, which are likely caused by free gas that scatters and attenuates higher frequencies. This approach allows for improved delineation of the BGHS on high-frequency MCS data that lack a reverse-polarity bottom-simulating reflector and on upper slopes where the BGHS is hard to discern. We also apply a neural-network seismic attribute workflow to analyze potential fluid-pathways below methane plumes imaged in the water column. The workflow uses structural steering calculations and multiple weighted attributes in a neural-network algorithm targeted for gas chimney detection.  The results highlight probable fluid flow pathways in areas with and without seafloor methane seeps and delineate deep-seated features (e.g., fractures) that supply gas to some of the deepwater (> 1000 m) seep sites.