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Seismic oceanography improving ocean knowledge
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Seismic oceanography re-uses legacy marine multichannel reflection data (MCS) giving it a new porpose for ocean knowledge enrichement. Targeting the water column, detailed MCS processing for high-resolution water column imaging facilitates new interpretations and ocean modelling technics. Seismic acoustic response is intrinsically conected to the water column propreties, such as temperature an salinity (e.g., Azevedo, L. et al., 2021), this brings the study of fine-scale ocean processes to another level giving the possibility of lateraly predicting their spatiotemporal continuity.To retrieve the water collumn reflections the MCS processing workflow requires specific steps that enhance the signal-to-noise ratio and preserve the true amplitude content (Duarte, et al., 2025) . Later, the resultant images can be interpreted within the context of expected and present ocean processes in the study region (Duarte, et al., 2024) and integrating measurements acquired in oceanographic campains.Since seismic oceanography signal depends on the water column propreties, seismic oceanography inversion enables the re-constrution of the ocean temperature and salinity distributions. However, seismic inversion is inherently non-linear, identical seismic responses can be originated from different combinations of temperature and salinity pairs. Consequently, the inversion problem is ill-posed and admits multiple solutions, requiring smart strategies to condition and solve the ambiguity (Azevedo, L. et al., 2021).Our results demonstrate a sucessful atempt on reconstructing the high-resolution temperature and salinty models for two Portuguese regions with stochastic seismic oceanography inversion. This aproach highlights how seismic oceanography can resolve fine-scale thermohaline structures, often neglected by conventional sampling technics and shades lighight into its spatial continuity while assessing the predictions of the uncertainty. The resulting models provide valuable insights confirming the potential of seismic data as a complement tool for oceanographic studies and encouragning its integration in further oceanographic studies.
Title: Seismic oceanography improving ocean knowledge
Description:
Seismic oceanography re-uses legacy marine multichannel reflection data (MCS) giving it a new porpose for ocean knowledge enrichement.
Targeting the water column, detailed MCS processing for high-resolution water column imaging facilitates new interpretations and ocean modelling technics.
Seismic acoustic response is intrinsically conected to the water column propreties, such as temperature an salinity (e.
g.
, Azevedo, L.
et al.
, 2021), this brings the study of fine-scale ocean processes to another level giving the possibility of lateraly predicting their spatiotemporal continuity.
To retrieve the water collumn reflections the MCS processing workflow requires specific steps that enhance the signal-to-noise ratio and preserve the true amplitude content (Duarte, et al.
, 2025) .
Later, the resultant images can be interpreted within the context of expected and present ocean processes in the study region (Duarte, et al.
, 2024) and integrating measurements acquired in oceanographic campains.
Since seismic oceanography signal depends on the water column propreties, seismic oceanography inversion enables the re-constrution of the ocean temperature and salinity distributions.
However, seismic inversion is inherently non-linear, identical seismic responses can be originated from different combinations of temperature and salinity pairs.
Consequently, the inversion problem is ill-posed and admits multiple solutions, requiring smart strategies to condition and solve the ambiguity (Azevedo, L.
et al.
, 2021).
Our results demonstrate a sucessful atempt on reconstructing the high-resolution temperature and salinty models for two Portuguese regions with stochastic seismic oceanography inversion.
This aproach highlights how seismic oceanography can resolve fine-scale thermohaline structures, often neglected by conventional sampling technics and shades lighight into its spatial continuity while assessing the predictions of the uncertainty.
The resulting models provide valuable insights confirming the potential of seismic data as a complement tool for oceanographic studies and encouragning its integration in further oceanographic studies.
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