Trans-dimensional Markov chain Monte Carlo inversion of sound speed and temperature: Application to Yellow Sea multichannel seismic data

Jun, Hyoung Oh; Cho, Yongchae; Noh, Joocheul

doi:10.1016/j.jmarsys.2019.05.006

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…Probably to make this methodology more accessible to Chinese researchers, many articles were published in Mandarin and English, however, with the same different content and DOI. (Song et al, 2010;Yang et al, 2010;Huang et al, 2012;Huang et al, 2013;Jiang-Xin et al, 2016;Jiang-Xin et al, 2017;Jun et al, 2019) As seen in figure4 and Tables 2 and 3, there is a predominance of researchers and journals where SO is published in the northern hemisphere. This directly reflects in the places where the studies were conducted.…”

Section: Seismic Oceanography (Consolidated)mentioning

confidence: 96%

“…The observation of the metadata referring to the articles from 2008 to 2023, and in all the articles that defined the processing of seismic reflection data since Holbrook et al (2003), shows a lack of standardization of the steps necessary for the visualization of seismic reflections within the water column (Nakamura et al, 2006;Hardy et al, 2007;Hobbs et al, 2009;Kormann et al, 2009;Krahmann et al, 2009;Eakin et al, 2011;Yamashita et al, 2011;Huang et al, 2012;Rice et al, 2013;Meléndez et al, 2014;Buffett et al, 2017;Moon et al, 2017;Jun et al, 2019;Sun et al, 2019;Bakhtiari Rad and Macelloni, 2020).…”

Section: General Workflow For So Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Seismic Oceanography Method: A Prisma Based Sistematic Review

Tassinari¹,

Araújo²,

Barbosa³

2023

JER

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Seismic Oceanography (SO) is an innovative method that uses seismic reflection data from surveys to study oceanographic features. We conducted a systematic review using the PRISMA method, retrieving 164 articles published between 2008 and 2023 from Web of Science and Scopus. Analyzing the data with Vosviewer and Bibliometrix software, we found that the SO methodology is expanding its applications and identified four sub-areas: 1) analyzing ocean background features, 2) mapping physical oceanographic features, 3) inverting and processing seismic data, and 4) studying interactions with the background. We traced the history of SO from its early studies in 1979 to recent works in 2023. Interestingly, the term "Seismic Oceanography" gained prominence after 2008, leading to the discovery of 15 additional articles through cross-referencing and citation analysis. Based on common processing techniques, we proposed a working guide with a flowchart for different types of seismic data, advanced processing, and relevant databases. This guide serves as a reference, offering professionals a historical perspective and practical guidance when applying the SO methodology. Our systematic review provides a comprehensive understanding of the current state of SO, identifies areas for further research, and introduces a valuable tool for professionals interested in utilizing the methodology.

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Section: Seismic Oceanography (Consolidated)mentioning

confidence: 96%

Section: General Workflow For So Studiesmentioning

confidence: 99%

Seismic Oceanography Method: A Prisma Based Sistematic Review

Tassinari¹,

Araújo²,

Barbosa³

2023

JER

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“…For example, it has been verified that the temperature contrasts in ocean waters can explain the amplitude-variation-with-angle (AVA) behavior of water-column seismic reflections [24]. Extracting accurate velocities (sound-speeds) using post-stack, pre-stack, and stochastic inversion methods to estimate temperature and salinity have also been attempted [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Most of these inversions allow for extraction of the high-frequency sound-speed variations within water-columns but require the low-frequency (background) trends to be available from other sources such as XBTs or CTDs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Temperature and Salinity from Marine Seismic Data—A Two-Step Approach

Chakraborty,

Mallick

2024

JMSE

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Ocean-water temperature and salinity are two vital properties that are required for weather-, climate-, and marine biology-related research. These properties are usually measured using disposable instruments at sparse locations, typically from tens to hundreds of kilometers apart. Laterally interpolating these sparse measurements provides smooth temperature and salinity distributions within the oceans, although they may not be very accurate. Marine seismic data, on the other hand, show visible reflections within the water-column which are primarily controlled by subtle sound-speed variations. Because these variations are functions of the temperature, salinity, and pressure, estimating sound-speed from marine seismic data and relating them to temperature and salinity have been attempted in the past. These seismically derived properties are of much higher lateral resolution (less than 25 m) than the sparse measurements and can be potentially used for climate and marine biology research. Estimating sound-speeds from seismic data, however, requires running iterative seismic inversions, which need a good initial model. Currently practiced ways to generate this initial model are computationally challenging, labor-intensive, and subject to human error and bias. In this research, we outline an automated method to generate the initial model which is neither computational and labor-intensive nor prone to human errors and biases. We also use a two-step process of, first, estimating the sound-speed from seismic inversion data and then estimating the salinity and temperature. Furthermore, by applying this method to real seismic data, we demonstrate the feasibility of our approach and discuss how the use of machine learning can further improve the computational efficiency of the method and make an impact on the future of climate modeling, weather prediction, and marine biology research.

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“…SO data are indirect information of relevant oceanographic features and complement the information provided by conventional oceanographic casts, which are direct measurements of the ocean properties but sparsely distributed. More recently, these data have been used to estimate the oceanic turbulent dissipation (Holbrook et al, 2013;Sallarès et al, 2016;Dickinson et al, 2017;Fortin et al, 2017) and to estimate the spatial distribution of the ocean's temperature and salinity using amplitude-vs.-offset analysis (Páramo and Holbrook, 2005), deterministic seismic oceanography inversion methods (Wood et al, 2008;Sallarès et al, 2009;Papenberg et al, 2010;Kormann et al, 2011;Song et al, 2012;Bornstein et al, 2013;Biescas et al, 2014;Padhi et al, 2015;Blacic et al, 2016;Dagnino et al, 2016Dagnino et al, , 2018Minakov et al, 2017;Gunn et al, 2018;Tang et al, 2018Tang et al, , 2019Gunn et al, 2020), stochastic seismic oceanography inversion (Tang et al, 2016;Azevedo et al, 2018;Jun et al, 2019) and automatic velocity analysis (Chhun and Tsuji, 2020).…”

Section: Introductionmentioning

confidence: 99%

Geostatistical Seismic Inversion for Temperature and Salinity in the Madeira Abyssal Plain

et al. 2021

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A two-dimensional multichannel seismic reflection profile acquired in the Madeira Abyssal Plain during June 2016 was used in a modeling workflow comprising seismic oceanography processing, geostatistical inversion and Bayesian classification to predict the probability of occurrence of distinct water masses. The seismic section was processed to image in detail the fine scale structure of the water column using seismic oceanography. The processing sequence was developed 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 migrated seismic oceanography section shows an eddy 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. We then developed and applied an iterative geostatistical seismic oceanography inversion methodology to predict the spatial distribution of temperature and salinity. Due to the lack of contemporaneous direct measurements of temperature and salinity we used a global ocean model as spatial constraint during the inversion and nearby contemporaneous ARGO data to infer the expected statistical properties of both model parameters. After the inversion, Bayesian classification was applied to all temperature and salinity models inverted during the last iteration to predict the spatial distribution of three distinct water masses. A preliminary interpretation of these probabilistic models agrees with the expected ocean dynamics of the region.

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Trans-dimensional Markov chain Monte Carlo inversion of sound speed and temperature: Application to Yellow Sea multichannel seismic data

Cited by 13 publications

References 20 publications

Seismic Oceanography Method: A Prisma Based Sistematic Review

Seismic Oceanography Method: A Prisma Based Sistematic Review

Estimation of Temperature and Salinity from Marine Seismic Data—A Two-Step Approach

Geostatistical Seismic Inversion for Temperature and Salinity in the Madeira Abyssal Plain

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