scholarly journals Crustal-scale depth imaging via joint FWI of OBS data and PSDM of MCS data: a case study from the eastern Nankai Trough

2019 ◽  
Author(s):  
Andrzej Górszczyk ◽  
Stephane Operto ◽  
Laure Schenini ◽  
Yasuhiro Yamada
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Nankai Trough ◽  
Imaging Techniques ◽  
Velocity Model ◽  
Ocean Bottom ◽  
Structural Factors ◽  
Air Gun ◽  
Imaging Results ◽  
Obs Data

Abstract. Imaging via Pre-Stack Depth Migration (PSDM) from reflection towed-streamer Multi-Channel Seismic (MCS) data at the scale of the whole crust is inherently difficult. This mainly results because the depth-penetration of the seismic wavefield is controlled, firstly (i) by the acquisition design, like streamer length and air-gun source configuration, and secondly (ii) 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. The problem is even more pronounced when processing 2D seismic data, due to the lack of multi-azimuthal coverage. Therefore, in order to broaden our knowledge about the deep crust using seismic methods, one shall target the development of specific imaging workflows integrating different seismic data. Here we propose the combination of velocity model-building using (i) first-arrival traveltime tomography (FAT) and full-waveform inversion (FWI) of wide-angle/long-offset data collected by stationary Ocean Bottom Seismometers (OBS) and (ii) PSDM of short-spread towed-streamer MCS data for reflectivity imaging, using the former velocity model as background model. We present an application of such workflow to seismic data collected by Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER) in the eastern Nankai Trough (Tokai area) during the 2000/2001 SFJ experiment. We show that the FWI model, although derived from OBS data, provides yet an acceptable background velocity field for the PSDM of the MCS data. Furthermore, from the initial PSDM, we first refine the FWI background velocity model by minimizing the residual moveouts (RMO) picked in the prestack migrated volume through slope tomography (ST), from which we generate a better focused migrated image. Such integration of different seismic data sets and leading-edge imaging techniques led to optimal imaging results at different resolution levels. That is, the large-to-intermediate scale crustal units identified in the high-resolution FWI velocity model extensively complement the short-scale reflectivity inferred from the MCS data to better constrain the structural factors controlling the geodynamics of the Nankai Trough area.

scholarly journals 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 ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 765-784 ◽  
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.

The Wolfspar experience with low-frequency seismic source field data: Motivation, processing, and implications

2022 ◽  
Vol 41 (1) ◽  
pp. 9-18
Author(s):  
Andrew Brenders ◽  
Joe Dellinger ◽  
Imtiaz Ahmed ◽  
Esteban Díaz ◽  
Mariana Gherasim ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Seismic Data ◽  
Field Data ◽  
Model Building ◽  
Low Frequency ◽  
Seismic Source ◽  
Velocity Model ◽  
Seismic Sources ◽  
Velocity Model Building ◽  
Air Gun

The promise of fully automatic full-waveform inversion (FWI) — a (seismic) data-driven velocity model building process — has proven elusive in complex geologic settings, with impactful examples using field data unavailable until recently. In 2015, success with FWI at the Atlantis Field in the U.S. Gulf of Mexico demonstrated that semiautomatic velocity model building is possible, but it also raised the question of what more might be possible if seismic data tailor-made for FWI were available (e.g., with increased source-receiver offsets and bespoke low-frequency seismic sources). Motivated by the initial value case for FWI in settings such as the Gulf of Mexico, beginning in 2007 and continuing into 2021 BP designed, built, and field tested Wolfspar, an ultralow-frequency seismic source designed to produce seismic data tailor-made for FWI. A 3D field trial of Wolfspar was conducted over the Mad Dog Field in the Gulf of Mexico in 2017–2018. Low-frequency source (LFS) data were shot on a sparse grid (280 m inline, 2 to 4 km crossline) and recorded into ocean-bottom nodes simultaneously with air gun sources shooting on a conventional dense grid (50 m inline, 50 m crossline). Using the LFS data with FWI to improve the velocity model for imaging produced only incremental uplift in the subsalt image of the reservoir, albeit with image improvements at depths greater than 25,000 ft (approximately 7620 m). To better understand this, reprocessing and further analyses were conducted. We found that (1) the LFS achieved its design signal-to-noise ratio (S/N) goals over its frequency range; (2) the wave-extrapolation and imaging operators built into FWI and migration are very effective at suppressing low-frequency noise, so that densely sampled air gun data with a low S/N can still produce useable model updates with low frequencies; and (3) data density becomes less important at wider offsets. These results may have significant implications for future acquisition designs with low-frequency seismic sources going forward.

Time reprocessing and depth imaging of vintage seismic data: the Southern Adriatic Sea case study

2020 ◽  
Author(s):  
Edy Forlin ◽  
Giuseppe Brancatelli ◽  
Nicolò Bertone ◽  
Anna Del Ben ◽  
Riccardo Geletti
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Adriatic Sea ◽  
Imaging Techniques ◽  
Low Cost ◽  
Velocity Model ◽  
Seismic Section ◽  
P Waves ◽  
Depth Imaging

<p>Nowadays depth imaging of seismic data, using different migration schemes (rays tracing or waves equation methods) and different techniques for velocity model building (i.e. grid or layer-based tomography, isotropic or anisotropic velocity field) is a standard approach for the earth’s subsurface characterization. When dealing with low fold vintage data, acquired with outdated technologies, modern processing algorithms may fail. On the other hand, the reprocessing of these old data with modern techniques may lead to an improvement of quality and resolution, allowing a more accurate interpretation of the investigated geological features. It is important to note that a lot of vintage data were acquired in areas with no recent surveys or currently subject to exploration restrictions. Therefore, available vintage data could be of great importance for all the stakeholders involved in geophysical exploration. We present a case study about the reprocessing of low fold marine seismic data that were acquired in 1971 in the Otranto Channel (Southern Adriatic Sea, Italy).</p><p>The first part of the work consists of a modern broadband sequence processing in the time domain, that allowed us to obtain a pre-stack time migrated seismic section; in the second part, depth imaging has been achieved through a pre-stack depth migration (PSDM). Reliable interval p-waves velocity model has been obtained using two tomographic approaches: grid tomography and layer-based tomography; for both, we carried out several iterations of the refinement loop, consisting of migration, ray tomography, residual velocity analysis, velocity model update.</p><p>The results show significant improvements compared to the original vintage section, in terms of resolution and signal to noise ratio. Moreover, depth imaging and velocity modeling added further information (e.g., reliable interval p-waves velocity model, real geometry and thickness of the main geological units). This study confirms that applying the up-to-date processing and imaging techniques to vintage data, their geophysical and geological value is enhanced and renewed at a relatively low cost.</p>

Full Wavefield Redatuming: Accurate Velocity Modelling for Imaging Beneath Complex Overburden

2021 ◽  
Author(s):  
Farah Syazana Dzulkefli ◽  
Kefeng Xin ◽  
Ahmad Riza Ghazali ◽  
Guo Qiang ◽  
Tariq Alkhalifah
Keyword(s):  
Wave Propagation ◽  
Seismic Data ◽  
Model Building ◽  
Velocity Model ◽  
Surrounding Rock ◽  
Target Area ◽  
Low Density ◽  
Velocity Model Building ◽  
Seismic Image ◽  
Seismic Wavefield

Abstract Salt is known for having a generally low density and higher velocity compared with the surrounding rock layers which causes the energy to scatter once the seismic wavefield hits the salt body and relatively less energy is transmitted through the salt to the deeper subsurface. As a result, most of imaging approaches are unable to image the base of the salt and the reservoir below the salt. Even the velocity model building such as FWI often fails to illuminate the deeper parts of salt area. In this paper, we show that Full Wavefield Redatuming (FWR) is used to retrieved and enhance the seismic data below the salt area, leading to a better seismic image quality and allowing us to focus on updating the velocity in target area below the salt. However, this redatuming approach requires a good overburden velocity model to retrieved good redatumed data. Thus, by using synthetic SEAM model, our objective is to study on the accuracy of the overburden velocity model required for imaging beneath complex overburden. The results show that the kinematic components of wave propagation are preserved through redatuming even with heavily smoothed overburden velocity model.

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

An optimization of the inverse scattering multiple attenuation method for OBS and VC data

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1293-1303 ◽  
Author(s):  
Luc T. Ikelle ◽  
Lasse Amundsen ◽  
Seung Yoo
Keyword(s):  
Free Surface ◽  
Data Storage ◽  
Inverse Scattering ◽  
Water Depth ◽  
Seismic Data ◽  
Computation Time ◽  
Ocean Bottom ◽  
Multiple Attenuation ◽  
Obs Data ◽  
Streamer Data

The inverse scattering multiple attenuation (ISMA) algorithm for ocean‐bottom seismic (OBS) data can be formulated in the form of a series expansion for each of the four components of OBS data. Besides the actual data, which constitute the first term of the series, each of the other terms is computed as a multidimensional convolution of OBS data with streamer data, and aims at removing one specific order of multiples. If the streamer data do not contain free‐surface multiples, we found that the computation of only the second term of the series is needed to predict and remove all orders of multiples, whatever the water depth. As the computation of the various terms of the series is the most expensive part of ISMA, this result can produce significant savings in computation time, even in data storage, as we no longer need to store the various terms of the series. For example, if the streamer data contained free‐surface multiples, OBS seismic data of 6‐s duration, corresponding to a geological model of the subsurface with 250‐m water depth, require the computation of five terms of the series for each of the four components of OBS data. With the new implementation, in which the streamer data do not contain free‐surface multiples, we need the computation of only one term of the series for each component of the OBS data. The saving in CPU time for this particular case is at least fourfold. The estimation of the inverse source signature, which is an essential part of ISMA, also benefits from the reduction of the number of terms needed for the demultiple to two because it becomes a linear inverse problem instead of a nonlinear one. Assuming that the removal of multiple events produces a significant reduction in the energy of the data, the optimization of this problem leads to a stable, noniterative analytic solution. We have also adapted these results to the implementation of ISMA for vertical‐cable (VC) data. This implementation is similar to that for OBS data. The key difference is that the basic model in VC imaging assumes that data consist of receiver ghosts of primaries instead of the primaries themselves. We have used the following property to achieve this goal. The combination of VC data with surface seismic data, which do not contain free‐surface multiples, allows us to predict free‐surface multiples and receiver ghosts as well as the receiver ghosts of primary reflections. However, if the direct wave arrivals are removed from the VC data, this combination will not predict the receiver ghosts of primary reflections. The difference between these two predictions produces data containing only receiver ghosts of primaries.

Wide-area imaging from OBS multiples

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. Q41-Q47 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document