Converted wave velocity model using 4C Ocean Bottom Seismometer data

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multi-channel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom seismometer (OBS) acquisition program acquired a four-component dataset in East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we applied a full-waveform inversion (FWI) workflow to this dense four-component OBS dataset. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from 3D to 2D, a preconditioned first-arrival traveltime tomography based on an improved scattering integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at 2.0 km and 4.7 km depth. Initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic full-waveform inversion to refine both velocity models. Compared to a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single-component, the workflow presented in this study represents a good approach for inverting the four-component OBS dataset to characterize sub-seafloor velocity structures.

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Wide-area imaging from OBS multiples

Geophysics ◽

10.1190/1.3223623 ◽

2009 ◽

Vol 74 (6) ◽

pp. Q41-Q47 ◽

Cited By ~ 40

Author(s):

Ranjan Dash ◽

George Spence ◽

Roy Hyndman ◽

Sergio Grion ◽

Yi Wang ◽

...

Keyword(s):

Single Channel ◽

Water Layer ◽

Velocity Model ◽

Imaging Method ◽

Ocean Bottom ◽

Ocean Bottom Seismometer ◽

Conventional Imaging ◽

Depth Migration ◽

Reflection Data ◽

Obs Data

The subseafloor structure offshore western Canada was imaged using first-order water-layer multiples from ocean-bottom seismometer (OBS) data and the results were compared to conventional imaging using primary reflections. This multiple-migration (mirror-imaging) method uses the downgoing pressure wavefield just above the seafloor, which is devoid of any primary reflections but consists of receiver-side ghosts of these primary reflections. The mirror-imaging method employs a primaries-only Kirchhoff prestack depth migration algorithm to image the receiver ghosts. The additional travel path of the multiples through the water layer is accounted for by a simple manipulation of the velocity model and processing datum: the receivers lie not on the seabed but on a sea surface twice as high as the true water column. Migration results show that the multiple-migrated image provides a much broader illumination of the subsurface than is possible for conventional imaging using the primaries, especially for the very shallow reflections and sparse OBS spacing. The resulting image from mirror imaging has illumination comparable to the vertical incidence surface streamer (single-channel) reflection data.

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Converted wave velocity model building with VTI anisotropy: Case study from Sichuan Basin, Southwest China

10.1190/segam2019-3215353.1 ◽

2019 ◽

Author(s):

Xiaowei Wang ◽

Zhe Yang ◽

Donghui Bian ◽

Shuhua Hu

Keyword(s):

Wave Velocity ◽

Sichuan Basin ◽

Southwest China ◽

Model Building ◽

Velocity Model ◽

Converted Wave ◽

Velocity Model Building

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S‐wave velocity structure of the crust in the Sigsbee plain in the Gulf of Mexico determined from multi‐component ocean bottom seismometer data

10.1190/1.1815597 ◽

2000 ◽

Author(s):

Sangmyung D. Kim ◽

Seiichi Nagihara ◽

Yosio Nakamura

Keyword(s):

Gulf Of Mexico ◽

Wave Velocity ◽

Velocity Structure ◽

Ocean Bottom ◽

Ocean Bottom Seismometer ◽

S Wave

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Shear-wave velocity structure of the oceanic lithosphere from ocean bottom seismometer studies

Geophysical Journal International ◽

10.1111/j.1365-246x.1984.tb01927.x ◽

1984 ◽

Vol 77 (1) ◽

pp. 105-123 ◽

Cited By ~ 31

Author(s):

D. Au ◽

R. M. Clowes

Keyword(s):

Shear Wave ◽

Wave Velocity ◽

Shear Wave Velocity ◽

Velocity Structure ◽

Oceanic Lithosphere ◽

Ocean Bottom ◽

Ocean Bottom Seismometer ◽

Shear Wave Velocity Structure

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Crustal-scale depth imaging via joint full-waveform inversion of ocean-bottom seismometer data and pre-stack depth migration of multichannel seismic data: a case study from the eastern Nankai Trough

Solid Earth ◽

10.5194/se-10-765-2019 ◽

2019 ◽

Vol 10 (3) ◽

pp. 765-784 ◽

Cited By ~ 3

Author(s):

Andrzej Górszczyk ◽

Stéphane Operto ◽

Laure Schenini ◽

Yasuhiro Yamada

Keyword(s):

Seismic Data ◽

Nankai Trough ◽

Waveform Inversion ◽

Velocity Model ◽

Full Waveform Inversion ◽

Ocean Bottom ◽

Ocean Bottom Seismometer ◽

Depth Migration ◽

Full Waveform ◽

Air Gun

Abstract. Imaging via pre-stack depth migration (PSDM) of reflection towed-streamer multichannel seismic (MCS) data at the scale of the whole crust is inherently difficult. This is because the depth penetration of the seismic wavefield is controlled, firstly, by the acquisition design, such as streamer length and air-gun source configuration, and secondly by the complexity of the crustal structure. Indeed, the limited length of the streamer makes the estimation of velocities from deep targets challenging due to the velocity–depth ambiguity. This problem is even more pronounced when processing 2-D seismic data due to the lack of multi-azimuthal coverage. Therefore, in order to broaden our knowledge about the deep crust using seismic methods, we present the development of specific imaging workflows that integrate different seismic data. Here we propose the combination of velocity model building using (i) first-arrival tomography (FAT) and full-waveform inversion (FWI) of wide-angle, long-offset data collected by stationary ocean-bottom seismometers (OBSs) and (ii) PSDM of short-spread towed-streamer MCS data for reflectivity imaging, with the former velocity model as a background model. We present an application of such a workflow to seismic data collected by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) in the eastern Nankai Trough (Tokai area) during the 2000–2001 Seize France Japan (SFJ) experiment. We show that the FWI model, although derived from OBS data, provides an acceptable background velocity field for the PSDM of the MCS data. From the initial PSDM, we refine the FWI background velocity model by minimizing the residual move-outs (RMOs) picked in the pre-stack-migrated volume through slope tomography (ST), from which we generate a better-focused migrated image. Such integration of different seismic datasets and leading-edge imaging techniques led to greatly improved imaging at different scales. That is, large to intermediate crustal units identified in the high-resolution FWI velocity model extensively complement the short-wavelength reflectivity inferred from the MCS data to better constrain the structural factors controlling the geodynamics of the Nankai Trough.

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OBS Data Analysis to Quantify Gas Hydrate and Free Gas in the South Shetland Margin (Antarctica)

Energies ◽

10.3390/en11123290 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3290 ◽

Cited By ~ 6

Author(s):

Sha Song ◽

Umberta Tinivella ◽

Michela Giustiniani ◽

Sunny Singhroha ◽

Stefan Bünz ◽

...

Keyword(s):

Velocity Field ◽

Wave Velocity ◽

Gas Hydrate ◽

P Wave ◽

Ocean Bottom ◽

Ocean Bottom Seismometer ◽

S Wave ◽

Free Gas ◽

Hydrate Reservoir ◽

Gas Hydrate Reservoir

The presence of a gas hydrate reservoir and free gas layer along the South Shetland margin (offshore Antarctic Peninsula) has been well documented in recent years. In order to better characterize gas hydrate reservoirs, with a particular focus on the quantification of gas hydrate and free gas and the petrophysical properties of the subsurface, we performed travel time inversion of ocean-bottom seismometer data in order to obtain detailed P- and S-wave velocity estimates of the sediments. The P-wave velocity field is determined by the inversion of P-wave refractions and reflections, while the S-wave velocity field is obtained from converted-wave reflections received on the horizontal components of ocean-bottom seismometer data. The resulting velocity fields are used to estimate gas hydrate and free gas concentrations using a modified Biot‐Geertsma‐Smit theory. The results show that hydrate concentration ranges from 10% to 15% of total volume and free gas concentration is approximately 0.3% to 0.8% of total volume. The comparison of Poisson’s ratio with previous studies in this area indicates that the gas hydrate reservoir shows no significant regional variations.

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Seismic Ambient Noise Imaging of a Quasi-Amagmatic Ultra-Slow Spreading Ridge

Remote Sensing ◽

10.3390/rs13142811 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2811

Author(s):

Mohamadhasan Mohamadian Sarvandani ◽

Emanuel Kästle ◽

Lapo Boschi ◽

Sylvie Leroy ◽

Mathilde Cannat

Keyword(s):

Wave Velocity ◽

Vertical Component ◽

Velocity Model ◽

Dispersion Curves ◽

Southwest Indian Ridge ◽

Spreading Ridge ◽

Ocean Bottom ◽

S Wave ◽

Crust And Upper Mantle ◽

Phase Velocity Dispersion

Passive seismic interferometry has become very popular in recent years in exploration geophysics. However, it has not been widely applied in marine exploration. The purpose of this study is to investigate the internal structure of a quasi-amagmatic portion of the Southwest Indian Ridge by interferometry and to examine the performance and reliability of interferometry in marine explorations. To reach this goal, continuous vertical component recordings from 43 ocean bottom seismometers were analyzed. The recorded signals from 200 station pairs were cross-correlated in the frequency domain. The Bessel function method was applied to extract phase–velocity dispersion curves from the zero crossings of the cross-correlations. An average of all the dispersion curves was estimated in a period band 1–10 s and inverted through a conditional neighborhood algorithm which led to the final 1D S-wave velocity model of the crust and upper mantle. The obtained S-wave velocity model is in good agreement with previous geological and geophysical studies in the region and also in similar areas. We find an average crustal thickness of 7 km with a shallow layer of low shear velocities and high Vp/Vs ratio. We infer that the uppermost 2 km are highly porous and may be strongly serpentinized.

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