Journal
2021 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR THE SEA (METROSEA 2021)
Volume -, Issue -, Pages 178-182Publisher
IEEE
DOI: 10.1109/MetroSea52177.2021.9611554
Keywords
seismic oceanography; stochastic inversion; Madeira Abyssal plain
Funding
- CERENA [FCT- UIDB/04028/2020]
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Seismic oceanography data processing and inversion techniques reveal potential insights into large- and small-scale ocean processes. Processed seismic sections show features such as eddies, steeply dipping reflectors, and strong horizontal reflections between water masses. By combining temperature and salinity information from various sources, geostatistical techniques were used to predict and interpolate spatial temperature and salinity distributions.
Seismic oceanography can be used to provide oceanographic insights about ocean processes happening simultaneously at large- and small-scales. We illustrate the potential of seismic oceanography data, by processing and inverting a set of three parallel two-dimensional multichannel seismic reflection profiles acquired in the Madeira Abyssal Plain during June 2006. The seismic sections were processed to image in detail the fine scale structure of the water column close to the sea surface. A similar processing sequence was applied to the three sections aiming to preserve, as much as possible, the relative seismic amplitudes of the data and enhance the shallow structure of the water column by effectively suppressing the direct arrival. The final processed seismic oceanography sections show several features of interest, comprising eddies at the expected Mediterranean Outflow Water depths, steeply dipping reflectors, which indicate the possible presence of frontal activity or secondary dipping eddy structures, and strong horizontal reflections between intermediate water masses suggestive of double diffuse processes. Then, we applied an iterative geostatistical seismic oceanography inversion methodology to predict the spatial distribution of temperature and salinity along each section. For the inversion we combined information about the ocean temperature and salinity from different sources. After the inversion, the predicted temperature are salinity models were interpolated with geostatistical simulation techniques to provide information regarding the region in between sections.
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