Recovery of temperature, salinity, and potential density from ocean reflectivity

Abstract

This work explores a method to recover temperature, salinity, and potential density of the ocean using acoustic reflectivity data and time and space coincident expendable bathythermographs (XBT). The acoustically derived (vertical frequency >10 Hz) and the XBT-derived (vertical frequency <10 Hz) impedances are summed in the time domain to form impedance profiles. Temperature (T) and salinity (S) are then calculated from impedance using the international thermodynamics equations of seawater (GSW TEOS-10) and an empirical T-S relation derived with neural networks; and finally potential density (q) is calculated from T and S. The main difference between this method and previous inversion works done from real multichannel seismic reflection (MCS) data recorded in the ocean, is that it inverts density and it does not consider this magnitude constant along the profile, either in vertical or lateral dimension. We successfully test this method on MCS data collected in the Gulf of Cadiz (NE Atlantic Ocean). T, S, and q are inverted with accuracies of dT sd 50:1 C, dS sd 50:09, and dq sd 50:02kg=m 3 . Inverted temperature anomalies reveal baroclinic thermohaline fronts with intrusions. The observations support a mix of thermohaline features created by both double-diffusive and isopycnal stirring mechanisms. Our results show that reflectivity is primarily caused by thermal gradients but acoustic reflectors are not isopycnal in all domains.

Marine seismic observation of internal solitary wave packets in the northeast South China Sea

“…From this and previous observation of ISWs [ Tang et al ., ], it can be concluded that the seismic oceanography is an efficient and effective way to detect the ISWs in a near‐surface region below 40, which was considered to be a challenging zone for the conventional seismic technique [ Piété et al ., ; Biescas et al ., ]. To ensure high‐quality seismic data, there are three primary requirements: (1) optimizing air gun type and array so the direct wave can be suppressed; (2) a small nearest offset (<100 m) for minimizing the blank zone; and (3) a mid‐size vessel for reducing the ambient noise.…”

“…[] and Biescas et al . []. The estimated standard deviations from the absolute differences using the neural network are below 0.1 and 0.025 psu at 60 and 200 m, respectively.…”

A Locally Generated High‐Mode Nonlinear Internal Wave Detected on the Shelf of the Northern South China Sea From Marine Seismic Observations

“…The joint inversion consists of two steps. The first step consists of recovering the “full” acoustic impedance field ( ) by adding the high‐frequency component impedance from seismic data ( , >15 Hz) and the low‐frequency component impedance from hydrographic data ( , <15 Hz; Biescas et al, ): where is the reflectivity (Figure ) at depth and is the impedance at depth . Figure shows the how is derived and the result is compared to acquired at 72.5 km along the section (Figure ) which is calculated assuming an empirical T‐S relationship from a neural network (Tang et al, ).…”

Acoustic mapping of mixed layer depth

“…Methods of utilizing ocean reflectivity from multi-channel seismic systems to reconstruct temperature and salinity stratification in between CTD casts have been investigated (Biescas et al, 2014;Papenberg et al, 2010;Wood et al, 2008). The increased vertical resolution (from ~10 m with multi-channel seismic data to <0.5 m with wideband 10 acoustics (Stranne et al, 2017)) facilitates the detection of much finer thermohaline structures in the water column, including the MLD, and can potentially vastly improve these methods.…”