Acoustic full waveform inversion using reflection energy: A case study from the Ekofisk LoFS ocean bottom data set

2013 ◽  
Author(s):  
Yi Wang ◽  
Kirk Wallace ◽  
Houzhu Zhang ◽  
Alexandre Bertrand ◽  
YunQing Shen
Keyword(s):  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Data Set ◽  
Full Waveform

scholarly journals A parameterization study for elastic VTI full-waveform inversion of hydrophone components: Synthetic and North Sea field data examples

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. R299-R308 ◽  
Author(s):  
Antoine Guitton ◽  
Tariq Alkhalifah
Keyword(s):  
North Sea ◽  
Waveform Inversion ◽  
Transversely Isotropic ◽  
Horizontal Velocity ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
S Wave ◽  
Data Set ◽  
Full Waveform ◽  
Streamer Data

Choosing the right parameterization to describe a transversely isotropic medium with a vertical symmetry axis (VTI) allows us to match the scattering potential of these parameters to the available data in a way that avoids a potential tradeoff and focuses on the parameters to which the data are sensitive. For 2D elastic full-waveform inversion in VTI media of pressure components and for data with a reasonable range of offsets (as with those found in conventional streamer data acquisition systems), assuming that we have a kinematically accurate normal moveout velocity ([Formula: see text]) and anellipticity parameter [Formula: see text] (or horizontal velocity [Formula: see text]) obtained from tomographic methods, a parameterization in terms of horizontal velocity [Formula: see text], [Formula: see text], and [Formula: see text] is preferred to the more conventional parameterization in terms of [Formula: see text], [Formula: see text], and [Formula: see text]. In the [Formula: see text], [Formula: see text], and [Formula: see text] parameterization and for reasonable scattering angles (<[Formula: see text]), [Formula: see text] acts as a “garbage collector” and absorbs most of the amplitude discrepancies between the modeled and observed data, more so when density [Formula: see text] and S-wave velocity [Formula: see text] are not inverted for (a standard practice with streamer data). On the contrary, in the [Formula: see text], [Formula: see text], and [Formula: see text] parameterization, [Formula: see text] is mostly sensitive to large scattering angles, leaving [Formula: see text] exposed to strong leakages from [Formula: see text] mainly. These assertions will be demonstrated on the synthetic Marmousi II as well as a North Sea ocean bottom cable data set, in which inverting for the horizontal velocity rather than the vertical velocity yields more accurate models and migrated images.

Three-dimensional frequency-domain full-waveform inversion with an iterative solver

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCC149-WCC157 ◽  
Author(s):  
René-Édouard Plessix
Keyword(s):  
Frequency Domain ◽  
Waveform Inversion ◽  
Three Dimensional ◽  
Iterative Solvers ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Iterative Solver ◽  
Data Set ◽  
Full Waveform

With the acquisition of wide-aperture seismic data sets, full-waveform inversion is an attractive method for deriving velocity models. Three-dimensional implementations require an efficient solver for the wave equation. Computing 3D time-harmonic responses with a frequency-domain solver is complicated because a large linear system with negative and positive eigenvalues must be solved. Time-domain schemes are an alternative. Nevertheless, existing frequency-domain iterative solvers with an efficient preconditioner are a viable option when full-waveform inversion is formulated in the frequency domain. An iterative solver with a multigrid preconditioner is competitive because of a high-order spatial discretization. Numerical examples illustrated the efficiency of the iterative solvers. Three dimensional full-waveform inversion was then studied in the context of deep-water ocean-bottom seismometer acquisition. Three dimensional synthetic data inversion results showed the behavior of full-waveform inversion with respect to the initial model and the minimum frequency available in the data set. Results on a 3D real ocean-bottom seismometer data set demonstrated the relevance of full-waveform inversion, especially to image the shallow part of the model.

Application of image-guided full waveform inversion to a 2D ocean-bottom cable data set

2012 ◽  
Author(s):  
Yong Ma ◽  
Jianxin (Jerry) Yuan ◽  
Yunqing Shen ◽  
Bin Gong
Keyword(s):  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Data Set ◽  
Image Guided ◽  
Full Waveform

scholarly journals Wavefront picking for 3D tomography and full-waveform inversion

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. B201-B210 ◽  
Author(s):  
Abdullah AlTheyab ◽  
G. T. Schuster
Keyword(s):  
Data Storage ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Interactive Software ◽  
Data Set ◽  
3D Tomography ◽  
Full Waveform ◽  
Early Arrival

We have developed an efficient approach for picking first-break wavefronts on coarsely sampled time slices of 3D shot gathers. Our objective was to compute a smooth initial velocity model for multiscale full-waveform inversion (FWI). Using interactive software, first-break wavefronts were geometrically modeled on time slices with a minimal number of picks. We picked sparse time slices, performed traveltime tomography, and then compared the predicted traveltimes with the data in-between the picked slices. The picking interval was refined with iterations until the errors in traveltime predictions fell within the limits necessary to avoid cycle skipping in early arrivals FWI. This approach was applied to a 3D ocean-bottom-station data set. Our results indicate that wavefront picking has 28% fewer data slices to pick compared with picking traveltimes in shot gathers. In addition, by using sparse time samples for picking, data storage is reduced by 88%, and therefore allows for a faster visualization and quality control of the picks. Our final traveltime tomogram is sufficient as a starting model for early arrival FWI.

Elastic full-waveform inversion application using multicomponent measurements of seismic data collection

Geophysics ◽  
2014 ◽  
Vol 79 (2) ◽  
pp. R63-R77 ◽  
Author(s):  
Denes Vigh ◽  
Kun Jiao ◽  
Dave Watts ◽  
Dong Sun
Keyword(s):  
Waveform Inversion ◽  
Noise Removal ◽  
Velocity Fields ◽  
Multiscale Approach ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Reverse Time ◽  
Data Set ◽  
Full Waveform ◽  
Time Migration

Recent computational improvements allowed us to simulate elastic wavefields in a 3D manner and undertake the challenge of executing elastic full-waveform inversion (EFWI). The 3D SEG/EAGE overthrust synthetic data were used to run the initial tests, which included using all three components for the simulation. The inversion targeted two regions: the channel system and the overthrusted zone, which proved the effectiveness of EFWI to delineate geology in terms of [Formula: see text] and [Formula: see text] velocity fields. For the field data experiment to demonstrate the technologies, we elected to use a Gulf of Mexico ocean bottom cable data set, which allowed us to take advantage of relatively large offsets along with the 4C acquisition. The input data were minimally processed mostly through noise removal, and the initial model was a Gaussian smoothed version of grid tomography output, which is done by a prestack migrated gather flattening process. During EWFI, a multiscale approach was followed to ensure convergence, and the early stages of the [Formula: see text]/[Formula: see text] ratio were constrained by the mud rock-line ratio. When the last sets of inversions were executed, this constraint was eliminated to ensure the simultaneous update of the [Formula: see text] and [Formula: see text] velocity fields. The density was kept constant to keep the inversion at a simple level, which allowed us to draw essential conclusions. The velocity fields were validated through an imaging algorithm of the elastic reverse time migration, and the imaging shows clear structural improvements when inputting the inverted velocities in conjunction with the measurements. If full-waveform inversion can provide multiple earth parameters, the user can use the process to detect gas zones along with sand and shale content of the subsurface, which will further assist the drilling decisions. We achieved this by simulating the earth more accurately with the elastic wave propagation in the algorithms.

Gas hydrate quantification using full-waveform inversion of sparse ocean-bottom seismic data: A case study from Green Canyon Block 955, Gulf of Mexico

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. B167-B181 ◽  
Author(s):  
Jiliang Wang ◽  
Priyank Jaiswal ◽  
Seth S. Haines ◽  
Patrick E. Hart ◽  
Shiguo Wu
Keyword(s):  
Gulf Of Mexico ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Full Waveform ◽  
Sonic Log ◽  
Obs Data

We present a case study of gas hydrate quantification using dense short-offset multichannel seismic (MCS) and sparse long-offset ocean-bottom-seismometer (OBS) data in lease block Green Canyon 955 (GC955), Gulf of Mexico (GOM), where the presence of gas hydrate was interpreted using logging while drilling (LWD) data acquired by the GOM Gas Hydrate Joint Industry Project Leg II expedition. We use frequency-domain full-waveform inversion (FWI) of seven OBS gathers to invert for a P-wave velocity model of an approximately 7 km long MCS profile connecting two LWD sites, GC955-H and GC955-Q. We build the starting model for FWI using traveltime inversion (TI) of the MCS and OBS data. In addition, we use the TI model for depth migrating the MCS stack. At the LWD sites, we constrain the hydrate saturation ([Formula: see text]) using sonic and resistivity logs. Unfortunately, as is typical of seismic quantification problems, the FWI model resolution is not sufficient to extrapolate the LWD-based [Formula: see text]. Therefore, we apply Backus averaging to the sonic log, at 60 m wavelength, bringing it within approximately 8% of the FWI model and make the assumption that averaging the sonic log is same as redistributing the gas hydrate within the Backus wavelength. In this manner, instead of [Formula: see text], the FWI model is able to estimate the total gas hydrate volume. In the end, we use the FWI model and the migrated stack to constrain the locations and bulk volumes of free gas and gas hydrate. Our results demonstrate that with careful processing, reasonable estimates on locations and bulk volumes of submarine gas hydrate accumulations can be achieved even with sparse seismic data that are not adequate for amplitude-based assessments.

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