Seismic estimates of turbulent diffusivity and evidence of nonlinear internal wave forcing by geometric resonance in the <scp>S</scp>outh <scp>C</scp>hina <scp>S</scp>ea

Fortin, W.; Holbrook, W. S.; Schmitt, Raymond W.

doi:10.1002/2017jc012690

Cited by 15 publications

(23 citation statements)

References 48 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The diapycnal diffusivity in m 2 s −1 , K q , is calculated using K q =Γε/N 2 (Osborn, 1980 these studies suggest a few crucial steps before calculating diffusivities. First, harmonic shot-generated noise may contaminate the spectra significantly, especially at the high wavenumbers in the turbulence subrange (Fortin et al, 2017;Holbrook et al, 2013). For the 50-m (i.e., k = 0.02 m -1 ) shot spacing of the seismic data, a notch filter centered at the prominent and harmonic spikes (k = n × 0.02 cpm, where n is any integer) can be applied to suppress the noise spikes.…”

Section: Diapycnal Diffusivity Calculationmentioning

confidence: 99%

Submesoscale Features and Turbulent Mixing of an Oblique Anticyclonic Eddy in the Gulf of Alaska Investigated by Marine Seismic Survey Data

et al. 2020

View full text Add to dashboard Cite

Mesoscale eddies are ubiquitous in the global ocean and can be captured by marine multichannel seismic surveys. In this study, a short-lived anticyclonic eddy is characterized along the seismic line STEEP11 acquired on 30 September 2008 in northern Gulf of Alaska. Fine-scale eddy stratification with alternative strong striae and weak layers is regarded as a typical eddy structure of the study region. The eddy center dips along a NW tilted axis of 1.9 ± 0.2°from the horizontal. Submesoscale structures including fronts and filaments coexist around the eddy periphery and dominate~70% of the eddy volume. The estimated geostrophic current is asymmetric and ageostrophic components must play a significant role in balancing the eddy system. Nonlinearity of the eddy is strong as its rotation speed is much higher than its translation speed. We suggest that Ekman transport is probably responsible for the skewed shape with regards to the asymmetric geostrophic current and asymmetric submesoscale processes. Detailed turbulent diffusivities in and around the eddy are quantified, with an average level of 6.5 × 10 −5 m 2 s −1 . The diapycnal diffusivities show an increasing pattern from the eddy center to the surrounding water. With the pervasive submesoscale processes as the transitional dynamics, a forward cascade of energy could be expected from the mesoscale eddy to the fine-scale wave-breaking and turbulence. The irregular vortex structure may reduce the structural stability, facilitate the energy conversion, and accelerate the eddy dissipation.Plain Language Summary Mesoscale eddies are the weather of the global ocean and can be captured by marine multichannel seismic surveys. Vertical axes of mesoscale eddies could be tilt rather than canonically straight, and therefore, their detailed internal structures may also be asymmetric. Here we use high-resolution seismic methods to characterize a skewed, short-lived (eight weeks) anticyclonic eddy in northern Gulf of Alaska for the first time. The eddy center is strongly displaced at depth with a NW tilted axis of~2°from the horizontal. Geostrophic currents, submesoscale structures, and turbulent diffusivities are asymmetrically distributed in and around the eddy. Except for the standard description/interpretation of a seismic imaged eddy, this study strives to derive numerous and detailed physical oceanographic characteristics from seismic data and stems from a close collaboration between seismologists and oceanographers.

show abstract

Section: Diapycnal Diffusivity Calculationmentioning

confidence: 99%

Submesoscale Features and Turbulent Mixing of an Oblique Anticyclonic Eddy in the Gulf of Alaska Investigated by Marine Seismic Survey Data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…For seismic data, it is important to determine how well the actual amplitude and shape of the true reflection are recovered through the denoising process. In particular, the amplitude information is a key parameter for acquiring the data slope spectrum, which calculates slope spectra directly from the seismic data (Holbrook et al, 2013;Fortin et al, 2017). Therefore, we extracted seismic traces from the denoised section and ground truth and compared the extracted traces, as shown in Fig.…”

Section: Experiments Using Training Datasetmentioning

confidence: 99%

“…It is difficult to apply various noise attenuation methods to SO data because analyzing the internal wave and turbulent subranges of the water column requires the horizontal wavenumber spectrum (Klymak and Moum, 2007) of the seismic data, which is liable to be damaged by data processing. Therefore, minimized noise attenuation processes have been applied to SO data, and for this reason, studies calculating the wavenumber spectrum by using SO data such as those by Holbrook et al (2013) and Fortin et al (2016Fortin et al ( , 2017 have only applied bandpass and notch filters to remove random and harmonic noise. However, when the sparker is used as a seismic source, the bandpass filter alone is not sufficient to attenuate random noise, resulting in great difficulties in analyzing the wavenumber spectrum.…”

Section: Introductionmentioning

confidence: 99%

Random Noise Attenuation of Sparker Seismic Oceanography Data with Machine Learning

Jun¹,

Jou²,

Kim³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Seismic oceanography (SO) acquires water column reflections by seismic exploration compensating for the drawbacks of conventional physical oceanographic equipment. Most SO studies obtain data using air guns, which have relatively low-frequency bands. For higher-frequency bands at a low exploration cost, using a smaller seismic exploration system, such as a sparker source with a shorter receiver length, would be an alternative. However, the sparker source has a relatively low energy and consequently produces data with a low signal-to-noise (S / N) ratio. To solve the problem of the low S / N ratio of sparker SO data, we applied machine learning. The purpose of this study is to attenuate the random noise in the East Sea sparker SO data without distorting the true shape and amplitude of water column reflections. A denoising convolutional neural network (DnCNN) that successfully suppresses random noise in a natural image is adopted as the machine learning network architecture. One of the most important factors of machine learning is the generation of an appropriate training dataset. We have generated two different training datasets using synthetic and field data. Models trained with the different training datasets are applied to the test data, and the denoised results are quantitatively compared. The trained models are applied to the target seismic data, i.e., the East Sea sparker water column seismic reflection data, and the denoised seismic sections are evaluated. The results show that machine learning can successfully attenuate the random noise of sparker water column seismic reflection data.

show abstract

“…Seismic exploration acquires data continuously in the horizontal direction; thus, it has the advantage of generating data with improved horizontal resolution relative to that obtained by conventional probebased oceanographic methods (Dagnino et al, 2016). Therefore, SO is used to image the structure of water layers (Tsuji et al, 2005;Sheen et al, 2012;Piété et al, 2013;Moon et al, 2017) and provide quantitative information, such as the physical properties (i.e., temperature, salinity) (Papenberg et al, 2010;Blacic et al, 2016;Dagnino et al, 2016;Jun et al, 2019) or the spectral distribution of the internal waves and turbulence (Sheen et al, 2009;Holbrook et al, 2013;Fortin et al, 2016) after analysis where temperature or salinity contrasts produce clear seismic reflections.…”

Section: Introductionmentioning

confidence: 99%

“…However, the noise attenuation method not only removes noise but also potentially alters important seismic signals (Jun et al, 2014). Especially for SO data, careful processing is essential to recover the actual shape of the water column reflections (Fortin et al, 2016), which contain internal wave and turbulence information. It is difficult to apply various noise attenuation methods to SO data because analyzing the internal wave and turbulent subranges of the water column requires the horizontal wavenumber spectrum (Klymak and Moum, 2007) of the seismic data, which is liable to be damaged by data processing.…”

Section: Introductionmentioning

confidence: 99%

Random noise attenuation of sparker seismic oceanography data with machine learning

et al. 2020

View full text Add to dashboard Cite

Abstract. Seismic oceanography (SO) acquires water column reflections using controlled source seismology and provides high lateral resolution that enables the tracking of the thermohaline structure of the oceans. Most SO studies obtain data using air guns, which can produce acoustic energy below 100 Hz bandwidth, with vertical resolution of approximately 10 m or more. For higher-frequency bands, with vertical resolution ranging from several centimeters to several meters, a smaller, low-cost seismic exploration system may be used, such as a sparker source with central frequencies of 250 Hz or higher. However, the sparker source has a relatively low energy compared to air guns and consequently produces data with a lower signal-to-noise (S∕N) ratio. To attenuate the random noise and extract reliable signal from the low S∕N ratio of sparker SO data without distorting the true shape and amplitude of water column reflections, we applied machine learning. Specifically, we used a denoising convolutional neural network (DnCNN) that efficiently suppresses random noise in a natural image. One of the most important factors of machine learning is the generation of an appropriate training dataset. We generated two different training datasets using synthetic and field data. Models trained with the different training datasets were applied to the test data, and the denoised results were quantitatively compared. To demonstrate the technique, the trained models were applied to an SO sparker seismic dataset acquired in the Ulleung Basin, East Sea (Sea of Japan), and the denoised seismic sections were evaluated. The results show that machine learning can successfully attenuate the random noise in sparker water column seismic reflection data.

show abstract

Seismic estimates of turbulent diffusivity and evidence of nonlinear internal wave forcing by geometric resonance in the South China Sea

Cited by 15 publications

References 48 publications

Submesoscale Features and Turbulent Mixing of an Oblique Anticyclonic Eddy in the Gulf of Alaska Investigated by Marine Seismic Survey Data

Submesoscale Features and Turbulent Mixing of an Oblique Anticyclonic Eddy in the Gulf of Alaska Investigated by Marine Seismic Survey Data

Random Noise Attenuation of Sparker Seismic Oceanography Data with Machine Learning

Random noise attenuation of sparker seismic oceanography data with machine learning

Contact Info

Product

Resources

About