Time-jittered ocean bottom seismic acquisition Haneet Wason and Felix J. Herrmann
|
|
|
- Charity Short
- 5 years ago
- Views:
Transcription
1 Time-jittered ocean bottom seismic acquisition Haneet Wason and Felix J. Herrmann SLIM University of British Columbia
2 Challenges Need for full sampling - wave-equation based inversion (RTM & FWI) - SRME/EPSI or related techniques Full azimuthal coverage - multiple source vessels - simultaneous/blended acquisition Deblending or wavefield reconstruction - recover unblended data from blended data - challenging to recover weak late events
3 Motivation Rethink marine acquisition (OBC, OBN) - sources (and receivers) at random locations - exploit natural variations in the acquisition (e.g., cable feathering) - as long as you know where sources were afterwards... it is fine! Want more for less... - shorter survey times - increased spatial sampling
4 Motivation Rethink marine acquisition (OBC, OBN) - sources (and receivers) at random locations - exploit natural variations in the acquisition (e.g., cable feathering) - as long as you know where sources were afterwards... it is fine! Want more for less... - shorter survey times - increased spatial sampling How is this possible? - (multi) vessel acquisition w/ jittered sampling & blending via compressed randomized intershot firing times - sparsity-promoting recovery using constraints ( deblending ) `1
5 x (m) More for less (no overlap) conventional jittered recovered `1 t (s) 2 X aperiodic compressed overlapping irregular periodic sparse no overlap periodic & dense
6 Outline Measurement model & recovery strategy Design of jittered, ocean bottom acquisition - jitter in time jittered in space (shot locations) Experimental results of sparsity-promoting processing - wavefield recovery via deblending & interpolation from (coarse) jittered/irregular to (fine) regular sampling grid
7 Compressed Sensing Successful sampling & reconstruction scheme exploit structure via sparsifying transform subsampling decreases sparsity large scale optimization look for sparsest solution
8 Time-jittered acquisition Compress inter-shot times random jitter in time =) jitter in space for a given speed discrete jittering - start by being on the grid maximum (acquisition) gap effectively controlled Challenges: recover fully sampled data from jittered data and remove overlaps (but no fear... sparse recovery is here!) Recent work - use non-uniform grid [Hennenfent et.al., 2010]
9 Measurement model Solve an underdetermined system of linear equations: data (measurements /observations) = b A b C n A C n P n P A = RMS H x 0 unknown { sampling matrix transform matrix x 0 C P
10 acquire in the field on irregular grid (subsampled shots w/ overlap between shot records) b would like to have on regular grid (all shots w/o overlaps between shot records) d Shot # 1 = RM Conventional acquisition time samples (#) Shot # 2 Shot # 3 Shot # ns
11 Sparsity-promoting recovery Exploit curvelet-domain sparsity of seismic data Sparsity-promoting program: x = arg min x x 1 subject to Ax = b { support detection { data-consistent amplitude recovery Sparsity-promoting solver: SPG 1 [van den Berg and Friedlander, 2008] Recover single-source prestack data volume: d = SH x
12 Outline Measurement model & recovery strategy Design of jittered, ocean bottom acquisition - jitter in time jittered in space (shot locations) Experimental results of sparsity-promoting processing - wavefield recovery via deblending & interpolation from (coarse) jittered/irregular to (fine) regular sampling grid
13 Sampling schemes regularly undersampled spatial grid full sampling regular undersampling ( η = 4 ) uniform random undersampling ( η = 4 ) jittered undersampling ( η = 4 ) [Hennenfent et.al., 2008]
14 Conventional vs. jittered sources [Speed of source vessel = 5 knots 2.5 m/s] Array 1 Array Array Array 2 η = 2 Recording time (s) Recording time (s) Source position (m) Source position (m)
15 Conventional vs. jittered sources [Speed of source vessel = 5 knots 2.5 m/s] Array 1 Array Array Array 2 Recording time (s) Recording time (s) Source position (m) Source position (m)
16 Significant spatial jittering 2 Number of shots Distance covered (m) Number of shots Bin center (m)
17 Simultaneous source acquisition & deblending - A new look at simultaneous sources by Beasley et. al., 98, 08 - High quality separation of simultaneous sources by sparse inversion by Abma et. al., 13 - Changing the mindset in seismic data acquisition by Berkhout, 08 - Utilizing dispersed source arrays in blended acquisition by Berkhout et. al., 12 - Random sampling: a new strategy for marine acquisition by Moldoveanu, 10 - Multi-vessel coil shooting acquisition by Moldoveanu, 10 - Simultaneous source separation by sparse radon transform by Akerberg et. al., 08 - Simultaneous source separation using dithered sources by Moore et. al., 08 - Simultaneous source separation via multi-directional vector-median filter by Huo et. al., 09 - Separation of blended data by iterative estimation and subtraction of blending interference noise by Mahdad et. al., 11
18 Our approach Combination of multiple-source time-jittered acquisition - random jitter in time =) jitter in space for a constant speed (favours recovery compared to periodic sampling) - shorter acquisition times sparsity-promoting processing - data is sparse in curvelets - optimization: use constraints `1 Address two challenges - overlap and jittered sampling (regularize & interpolate)
19 Outline Measurement model & recovery strategy Design of jittered, ocean bottom acquisition - jitter in time jittered in space (shot locations) Experimental results of sparsity-promoting processing - wavefield recovery via deblending & interpolation from (coarse) jittered/irregular to (fine) regular sampling grid
20 Time-jittered OBC acquisition [1 source vessel, speed = 5 knots, underlying grid: 25 m] [no. of jittered source locations is half the number of sources in ideal periodic survey w/o overlap] measurements ( ) b 200 Array 1 Array 2 { Recording time (s) Source position (m)
21 Sparsity-promoting recovery on the grid (14.2 db) [ deblending + interpolation from jittered 50m grid to regular 25m grid] receiver gather shot gather
22 Sparsity-promoting recovery on the grid (14.2 db) [ deblending + interpolation from jittered 50m grid to regular 25m grid] (difference) receiver gather shot gather
23 FDCT vs. NFDCT fast discrete curvelet transform x k t Input 2- D FFT f curvelet tilling 2- D IFFT curvelet coefficients non-equispaced fast discrete curvelet transform x k t Input 1- D FFT on t 1- D NFFT on x f curvelet tilling 1- D IFFT on f 1- D INFFT on k curvelet coefficients
24 NFFT-generated data receiver gather shot gather
25 Recovery with FDCT ( binning ) [ deblending + interpolation from jittered 50m grid to regular 25m grid] separation result difference
26 Recovery with FDCT ( binning ) [ deblending + interpolation from jittered 50m grid to regular 25m grid] separation result difference
27 Sparsity-promoting recovery on irregular grid with NFDCT (17.6 db) [ deblending + interpolation from jittered 50m grid to regular 25m grid] receiver gather shot gather
28 Sparsity-promoting recovery on irregular grid with NFDCT (17.6 db) [ deblending + interpolation from jittered 50m grid to regular 25m grid] receiver gather shot gather
29 Sparsity-promoting recovery on irregular grid with NFDCT (17.6 db) [ deblending + interpolation from jittered 50m grid to regular 25m grid] (difference) receiver gather shot gather
30 Performance Improvement spatial sampling ratio = no. of spatial grid points recovered from jittered sampling via sparse recovery no. of spatial grid points in conventional sampling = =2
31 Multiple source vessels improves recovery shorter times lead to better spatial sampling at the expense of more overlap better azimuthal coverage
32 Time-jittered OBC acquisition [2 source vessels, speed = 5 knots, underlying grid: 25 m] [no. of jittered source locations is half the number of sources in ideal periodic survey w/o overlap] measurements ( ) b 200 Array 1 Array { Recording time (s) Source position (m)
33 Sparsity-promoting recovery on irregular grid with NFDCT (21.5 db) [ deblending + interpolation from jittered 50m grid to regular 25m grid] receiver gather shot gather
34 Sparsity-promoting recovery on irregular grid with NFDCT (21.5 db) [ deblending + interpolation from jittered 50m grid to regular 25m grid] receiver gather shot gather
35 Sparsity-promoting recovery on irregular grid with NFDCT (21.5 db) [ deblending + interpolation from jittered 50m grid to regular 25m grid] (difference) receiver gather shot gather
36 Time-jittered OBC acquisition [2 source vessels, speed = 5 knots, underlying grid: 12.5 m] [no. of jittered source locations is one-fourth the number of sources in ideal periodic survey w/o overlap] measurements ( ) b 100 Array 1 Array Recording time (s) { Source position (m)
37 Sparsity-promoting recovery on irregular grid with NFDCT (16.8 db) [ deblending + interpolation from jittered 50m grid to regular 12.5m grid] receiver gather shot gather
38 Sparsity-promoting recovery on irregular grid with NFDCT (16.8 db) [ deblending + interpolation from jittered 50m grid to regular 12.5m grid] receiver gather shot gather
39 Sparsity-promoting recovery on irregular grid with NFDCT (16.8 db) [ deblending + interpolation from jittered 50m grid to regular 12.5m grid] (difference) receiver gather shot gather
40 Performance Improvement spatial sampling ratio = no. of spatial grid points recovered from jittered sampling via sparse recovery no. of spatial grid points in conventional sampling = =4
41 Summary deblend + interpolate (jittered (m) to regular (m)) sparsity-promoting recovery with NFDCT [SNR (db)] 50 to source vessel (2 airgun arrays) 50 to source vessels 50 to (2 airgun arrays per vessel) 50 to
42 Observations Time-jittered marine acquisition is an instance of compressed sensing With sparsity-promoting recovery we can: - deblend recover the wavefield, and - regularize from a jittered/irregular to a regular grid - interpolate from a coarse jittered (50m) grid to a fine regular grid (25m, 12.5m, and finer)
43 Acknowledgements Thank you! This work was in part financially supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant (22R81254) and the Collaborative Research and Development Grant DNOISE II ( ). This research was carried out as part of the SINBAD II project with support from the following organizations: BG Group, BGP, BP, Chevron, ConocoPhillips, Petrobras, PGS, Total SA, and WesternGeco.
Randomized sampling strategies
Randomized sampling strategies Felix J. Herrmann SLIM Seismic Laboratory for Imaging and Modeling the University of British Columbia SLIM Drivers & Acquisition costs impediments Full-waveform inversion
Randomized sampling without repetition in timelapse seismic surveys
Randomized sampling without repetition in timelapse seismic surveys Felix Oghenekohwo SLIM University of British Columbia Randomized sampling without repetition in timelapse seismic surveys Felix Oghenekohwo
Sub-Nyquist sampling and sparsity: getting more information from fewer samples
Sub-Nyquist sampling and sparsity: getting more information from fewer samples Felix J. Herrmann SLIM Seismic Laboratory for Imaging and Modeling the University of British Columbia SLIM Drivers Our incessant
Summary. Introduction. Source separation method
Separability of simultaneous source data based on distribution of firing times Jinkun Cheng, Department of Physics, University of Alberta, jinkun@ualberta.ca Summary Simultaneous source acquisition has
AVA analysis and geological dip estimation via twoway wave-equation based extended images
AVA analysis and geological dip estimation via twoway wave-equation based extended images Rajiv Kumar, Tristan van Leeuwen and Felix J. Herrmann SLIM University of British Columbia Motivation Computation
Seismic Data Interpolation With Symmetry
Seismic Data Interpolation With Symmetry James Johnson joint work with Gilles Hennenfent 8 Consortium Meeting Outline Introduction Reciprocity Real Data Example Data Interpolation Problem Statement CRSI
SUMMARY. In combination with compressive sensing, a successful reconstruction
Higher dimensional blue-noise sampling schemes for curvelet-based seismic data recovery Gang Tang, Tsinghua University & UBC-Seismic Laboratory for Imaging and Modeling (UBC-SLIM), Reza Shahidi, UBC-SLIM,
Simultaneous source separation via robust time variant Radon operators
Simultaneous source separation via robust time variant Radon operators Summary Amr Ibrahim, University of Alberta, Edmonton, Canada amr.ibrahim@ualberta.ca and Mauricio D. Sacchi, University of Alberta,
Deconvolution with curvelet-domain sparsity Vishal Kumar, EOS-UBC and Felix J. Herrmann, EOS-UBC
Deconvolution with curvelet-domain sparsity Vishal Kumar, EOS-UBC and Felix J. Herrmann, EOS-UBC SUMMARY We use the recently introduced multiscale and multidirectional curvelet transform to exploit the
Source Estimation for Wavefield Reconstruction Inversion
Source Estimation for Wavefield Reconstruction Inversion Zhilong Fang * and Felix J. Herrmann * * Seismic Laboratory for Imaging and Modeling SLIM), University of British Columbia Abstract Wavefield reconstruction
Summary. Introduction
Multivessel coil shooting acquisition with simultaneous sources Nick Moldoveanu¹, Ying Ji², Craig Beasley¹ ¹WesternGeco, ²Schlumberger Cambridge Research Summary Multivessel coil shooting is a towed-streamer
Enhanced adaptive subtraction method for simultaneous source separation Zhaojun Liu*, Bin Wang, Jim Specht, Jeffery Sposato and Yongbo Zhai, TGS
Enhanced adaptive subtraction method for simultaneous source separation Zhaojun Liu*, Bin Wang, Jim Specht, Jeffery Sposato and Yongbo Zhai, TGS Summary We have developed an iterative adaptive subtraction
Main Menu. Summary. sampled) f has a sparse representation transform domain S with. in certain. f S x, the relation becomes
Preliminary study on Dreamlet based compressive sensing data recovery Ru-Shan Wu*, Yu Geng 1 and Lingling Ye, Modeling and Imaging Lab, Earth & Planetary Sciences/IGPP, University of California, Santa
SUMMARY. Pursuit De-Noise (BPDN) problem, Chen et al., 2001; van den Berg and Friedlander, 2008):
Controlling linearization errors in l 1 regularized inversion by rerandomization Ning Tu, Xiang Li and Felix J. Herrmann, Earth and Ocean Sciences, University of British Columbia SUMMARY Linearized inversion
A new take on FWI: Wavefield Reconstruction Inversion
A new take on FWI: Wavefield Reconstruction Inversion T. van Leeuwen 1, F.J. Herrmann and B. Peters 1 Centrum Wiskunde & Informatica, Amsterdam, The Netherlands University of British Columbia, dept. of
Incoherent noise suppression with curvelet-domain sparsity Vishal Kumar, EOS-UBC and Felix J. Herrmann, EOS-UBC
Incoherent noise suppression with curvelet-domain sparsity Vishal Kumar, EOS-UBC and Felix J. Herrmann, EOS-UBC SUMMARY The separation of signal and noise is a key issue in seismic data processing. By
We LHR2 08 Simultaneous Source Separation Using an Annihilation Filter Approach
We LHR2 08 Simultaneous Source Separation Using an Annihilation Filter Approach J. Rohnke* (CGG) & G. Poole (CGG) SUMMARY Simultaneous shooting increases acquisition efficiency by activating more than
Acquisition of high shot density blended seismic data: a WAZ sea trial Thomas Mensch*, Damien Grenié, Risto Siliqi and Yunfeng Li.
Acquisition of high shot density blended seismic data: a WAZ sea trial Thomas Mensch*, Damien Grenié, Risto Siliqi and Yunfeng Li Summary In this paper, we present the results of a high shot density sea
SUMMARY. In this paper, we present a methodology to improve trace interpolation
Reconstruction of seismic wavefields via low-rank matrix factorization in the hierarchical-separable matrix representation Rajiv Kumar, Hassan Mansour, Aleksandr Y. Aravkin, and Felix J. Herrmann University
A dual domain algorithm for minimum nuclear norm deblending Jinkun Cheng and Mauricio D. Sacchi, University of Alberta
A dual domain algorithm for minimum nuclear norm deblending Jinkun Cheng and Mauricio D. Sacchi, University of Alberta Downloaded /1/15 to 142.244.11.63. Redistribution subject to SEG license or copyright;
Simply denoise: wavefield reconstruction via jittered undersampling
Simply denoise: wavefield reconstruction via jittered undersampling Gilles Hennenfent 1 and Felix J. Herrmann 1 ABSTRACT In this paper, we present a new discrete undersampling scheme designed to favor
Downloaded 01/20/16 to Redistribution subject to SEG license or copyright; see Terms of Use at
D Multi-source Least-squares Reverse Time Migration Wei Dai, C. Boonyasiriwat and Gerard T. Schuster, King Abdullah University of Science and Technology Downloaded 1//1 to 19.171.17.1. Redistribution subject
Reweighted thresholding and orthogonal projections for simultaneous source separation Satyakee Sen*, Zhaojun Liu, James Sheng, and Bin Wang, TGS
Reweighted thresholding and orthogonal projections for simultaneous source separation Satyakee Sen*, Zhaojun Liu, James Sheng, and Bin Wang, TGS Summary The key to success for simultaneous source separation
One-norm regularized inversion: learning from the Pareto curve
One-norm regularized inversion: learning from the Pareto curve Gilles Hennenfent and Felix J. Herrmann, Earth & Ocean Sciences Dept., the University of British Columbia ABSTRACT Geophysical inverse problems
Application of randomized sampling schemes to curvelet-based sparsity-promoting seismic data recovery
Application of randomized sampling schemes to curvelet-based sparsity-promoting seismic data recovery Reza Shahidi 1 (rshahidi@gmail.com), Gang Tang 12 (gtangthu@gmail.com), Jianwei Ma 3 (jma@hit.edu.cn)
Compressive sensing in seismic exploration: an outlook on a new paradigm
Compressive sensing in seismic exploration: an outlook on a new paradigm Felix J. Herrmann 1, Haneet Wason 1, and Tim T.Y. Lin 1 ABSTRACT Many seismic exploration techniques rely on the collection of massive
Large-scale workflows for wave-equation based inversion in Julia
Large-scale workflows for wave-equation based inversion in Julia Philipp A. Witte, Mathias Louboutin and Felix J. Herrmann SLIM University of British Columbia Motivation Use Geophysics to understand the
Seismic wavefield reconstruction using reciprocity
Seismic wavefield reconstruction using reciprocity by James Johnson B.Sc.Engineering Physics, Queen s University Kingston Ontario Canada. B.SC. (Gen) Geology, Queen s University Kingston Ontario Canada.
Full-waveform inversion with randomized L1 recovery for the model updates
Full-waveform inversion with randomized L1 recovery for the model updates Xiang Li, Aleksandr Aravkin, Tristan van Leeuwen, Felix Herrmann March 5, 11 Abstract Full-waveform inversion (FWI) is a data fitting
A simulated Simultaneous Source Experiment in Shallow waters and the Impact of Randomization Schemes Rolf Baardman, Roald van Borselen, PGS
simulated Simultaneous Source Experiment in Shallow waters and the Impact of Randomization Schemes Rolf aardman, Roald van orselen, PGS Summary In simultaneous source acquisition, seismic data can be recorded
Removing blended sources interference using Stolt operator Amr Ibrahim, Department of Physics, University of Alberta,
Removing blended sources interference using Stolt operator Amr Ibrahim, Department of Physics, University of Alberta, amr.ibrahim@ualberta.ca Summary We use Stolt migration/de-migration operator to remove
Sampling functions and sparse reconstruction methods
Sampling functions and sparse reconstruction methods Mostafa Naghizadeh and Mauricio Sacchi Signal Analysis and Imaging Group Department of Physics, University of Alberta EAGE 2008 Rome, Italy Outlines:
Robust Seismic Image Amplitude Recovery Using Curvelets
Robust Seismic Image Amplitude Recovery Using Curvelets Peyman P. Moghaddam Joint work with Felix J. Herrmann and Christiaan C. Stolk UNIVERSITY OF BRITISH COLUMBIA University of Twente References Curvelets
Sparsity-promoting migration accelerated by message passing Xiang LI and Felix J.Herrmann EOS-UBC
Sparsity-promoting migration accelerated by message passing Xiang LI and Feli J.Herrmann EOS-UBC SUMMARY Seismic imaging via linearized inversion requires multiple iterations to the least-squares misfit
Seismic data Interpolation in the Continuous Wavenumber Domain, Flexibility and Accuracy
Seismic data Interpolation in the Continuous Wavenumber Domain, Flexibility and Accuracy Ye Zheng Geo-X Exploration Services Inc. Summary Our recently developed algorithm, ASFT (Arbitrarily Sampled Fourier
We N Simultaneous Shooting for Sparse OBN 4D Surveys and Deblending Using Modified Radon Operators
We N101 08 Simultaneous Shooting for Sparse OBN 4D Surveys and Deblending Using Modified Radon Operators R.R. Haacke* (CGG), G. Hampson (Chevron) & B. Golebiowski (CGG) SUMMARY Significant gains in productivity
Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization a
Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization a a Published in Geophysics, 79, no. 5, V179-V189, (2014) Yangkang Chen, Sergey Fomel, and Jingwei Hu
Least-squares migration/inversion of blended data
Least-squares migration/inversion of blended data Yaxun Tang and Biondo Biondi ABSTRACT We present a method based on least-squares migration/inversion to directly image data collected from recently developed
Recent developments in curvelet-based seismic processing
0000 Recent developments in curvelet-based seismic processing Felix J. Herrmann (fherrmann@eos.ubc.ca) Seismic Laboratory for Imaging and Modeling Department of Earth and Ocean Sciences The University
Practical implementation of SRME for land multiple attenuation
Practical implementation of SRME for land multiple attenuation Juefu Wang* and Shaowu Wang, CGGVeritas, Calgary, Canada juefu.wang@cggveritas.com Summary We present a practical implementation of Surface
G009 Multi-dimensional Coherency Driven Denoising of Irregular Data
G009 Multi-dimensional Coherency Driven Denoising of Irregular Data G. Poole* (CGGVeritas Services (UK) Ltd) SUMMARY Many land and ocean bottom datasets suffer from high levels of noise which make the
Synchronized multi-level source, a robust broadband marine solution Risto Siliqi*, Thierry Payen, Ronan Sablon and Karine Desrues, CGG
Risto Siliqi*, Thierry Payen, Ronan Sablon and Karine Desrues, CGG Summary The quest for robust broadband acquisition has become the cornerstone for obtaining high resolution images of the subsurface.
Seismic data interpolation using nonlinear shaping regularization a
Seismic data interpolation using nonlinear shaping regularization a a Published in Journal of Seismic Exploration, 24, no. 5, 327-342, (2015) Yangkang Chen, Lele Zhang and Le-wei Mo ABSTRACT Seismic data
Seismic data reconstruction with Generative Adversarial Networks
Seismic data reconstruction with Generative Adversarial Networks Ali Siahkoohi 1, Rajiv Kumar 1,2 and Felix J. Herrmann 2 1 Seismic Laboratory for Imaging and Modeling (SLIM), The University of British
# of PDE solves flops conventional 2N s N s N hx N hy N hz this paper 2N x N r N s
AVA analysis and geological dip estimation via two-way wave-equation based extended images Rajiv Kumar, Tristan van Leeuwen and Felix J. Herrmann University of British Columbia, Department of Earth, Ocean
Th N Deghosting by Echo-deblending SUMMARY. A.J. Berkhout* (Delft University of Technology) & G. Blacquiere (Delft University of Technology)
Th N103 11 Deghosting by Echo-deblending A.J. Berkhout* (Delft University of Technology) & G. Blacquiere (Delft University of Technology) SUMMARY Because of the strong sea surface reflectivity, a marine
Seismic wavefield inversion with curvelet-domain sparsity promotion Felix J. Herrmann, EOS-UBC and Deli Wang, Jilin University
Seismic wavefield inversion with curvelet-domain sparsity promotion Felix J. Herrmann, EOS-UBC and Deli Wang, Jilin University SUMMARY Inverting seismic wavefields lies at the heart of seismic data processing
Compressed sensing based land simultaneous acquisition using encoded sweeps
Compressed sensing based land simultaneous acquisition using encoded sweeps Rajiv Kumar 1, Shashin Sharan 1, Nick Moldoveanu 2, and Felix J. Herrmann 3 1 University of British Columbia, Vancouver 2 WesternGeco,
Seismic data interpolation beyond aliasing using regularized nonstationary autoregression a
Seismic data interpolation beyond aliasing using regularized nonstationary autoregression a a Published in Geophysics, 76, V69-V77, (2011) Yang Liu, Sergey Fomel ABSTRACT Seismic data are often inadequately
A047 Simultaneous-source Acquisition in the North Sea Prospect Evaluation
A047 Simultaneous-source Acquisition in the North Sea Prospect Evaluation B. Szydlik* (WesternGeco), C.J. Beasley (WesternGeco) & I. Moore (WesternGeco) SUMMARY This paper outlines a method for quantitative
Five Dimensional Interpolation:exploring different Fourier operators
Five Dimensional Interpolation:exploring different Fourier operators Daniel Trad CREWES-University of Calgary Summary Five-Dimensional interpolation has become a very popular method to pre-condition data
COMPRESSIVE SENSING FOR FULL WAVEFIELD IMAGE RECOVERY IN STRUCTURAL MONITORING APPLICATIONS
7th European Workshop on Structural Health Monitoring July 8-11, 14. La Cité, Nantes, France More Info at Open Access Database www.ndt.net/?id=1769 COMPRESSIVE SENSING FOR FULL WAVEFIELD IMAGE RECOVERY
Application of 3D source deghosting and designature to deep-water ocean bottom node data Xu Li*, Jing Yang, Hui Chen, Melanie Vu, and Ping Wang (CGG)
Xu Li*, Jing Yang, Hui Chen, Melanie Vu, and Ping Wang (CGG) Summary Compared to towed-streamer data, deep-water ocean bottom node (OBN) data by nature have a broader bandwidth; however, the presence of
Randomized sampling and sparsity: getting more information from fewer samples
Randomized sampling and sparsity: getting more information from fewer samples Felix J. Herrmann 1 ABSTRACT Many seismic exploration techniques rely on the collection of massive data volumes that are subsequently
Anisotropy-preserving 5D interpolation by hybrid Fourier transform
Anisotropy-preserving 5D interpolation by hybrid Fourier transform Juefu Wang and Shaowu Wang, CGG Summary We present an anisotropy-preserving interpolation method based on a hybrid 5D Fourier transform,
Fast "online" migration with Compressive Sensing Felix J. Herrmann
Fast "online" migration with Compressive Sensing Felix J. Herrmann SLIM University of British Columbia Fast "online" migration with Compressive Sensing Felix J. Herrmann, Ning Tu, and Ernie Esser with
Successes and challenges in 3D interpolation and deghosting of single-component marinestreamer
Successes and challenges in 3D interpolation and deghosting of single-component marinestreamer data James Rickett*, Schlumberger Gould Research Summary Combining deghosting with crossline interpolation
P. Bilsby (WesternGeco), D.F. Halliday* (Schlumberger Cambridge Research) & L.R. West (WesternGeco)
I040 Case Study - Residual Scattered Noise Attenuation for 3D Land Seismic Data P. Bilsby (WesternGeco), D.F. Halliday* (Schlumberger Cambridge Research) & L.R. West (WesternGeco) SUMMARY We show that
SUMMARY. These two projections are illustrated in the equation
Mitigating artifacts in Projection Onto Convex Sets interpolation Aaron Stanton*, University of Alberta, Mauricio D. Sacchi, University of Alberta, Ray Abma, BP, and Jaime A. Stein, Geotrace Technologies
P071 Land Data Regularization and Interpolation Using Azimuth Moveout (AMO)
P071 Land Data Regularization and Interpolation Using Azimuth Moveout (AMO) A.M. Popovici* (3DGeo Development Inc.), S. Crawley (3DGeo), D. Bevc (3DGeo) & D. Negut (Arcis Processing) SUMMARY Azimuth Moveout
Azimuth Moveout (AMO) for data regularization and interpolation. Application to shallow resource plays in Western Canada
Azimuth Moveout (AMO) for data regularization and interpolation. Application to shallow resource plays in Western Canada Dan Negut, Samo Cilensek, Arcis Processing, Alexander M. Popovici, Sean Crawley,
y Input k y2 k y1 Introduction
A regularization workflow for the processing of cross-spread COV data Gordon Poole 1, Philippe Herrmann 1, Erika Angerer 2, and Stephane Perrier 1 1 : CGG-Veritas, 2 : OMV Data regularization is critical
Interpolation of irregularly sampled data
Chapter 3 Interpolation of irregularly sampled data Most modern seismic acquisition methods aim to sample data regularly along all axes. Deviations from this sampling happen for various reasons. On land
SUMMARY. min. m f (m) s.t. m C 1
Regularizing waveform inversion by projections onto convex sets application to the D Chevron synthetic blind-test dataset Bas Peters *, Zhilong Fang *, Brendan Smithyman #, Felix J. Herrmann * * Seismic
A hybrid stochastic-deterministic optimization method for waveform inversion
A hybrid stochastic-deterministic optimization method for waveform inversion Tristan van Leeuwen, Mark Schmidt, Michael Friedlander and Felix Herrmann Dept. of Earth and Ocean sciences University of British
Inversion after depth imaging
Robin P. Fletcher *, Stewart Archer, Dave Nichols, and Weijian Mao, WesternGeco Summary In many areas, depth imaging of seismic data is required to construct an accurate view of the reservoir structure.
G012 Scattered Ground-roll Attenuation for 2D Land Data Using Seismic Interferometry
G012 Scattered Ground-roll Attenuation for 2D Land Data Using Seismic Interferometry D.F. Halliday* (Schlumberger Cambridge Research), P.J. Bilsby (WesternGeco), J. Quigley (WesternGeco) & E. Kragh (Schlumberger
Curvelet-domain matched filtering Felix J. Herrmann and Peyman P. Moghaddam EOS-UBC, and Deli Wang, Jilin University
Curvelet-domain matched filtering Felix J. Herrmann and Peyman P. Moghaddam EOS-UBC, and Deli Wang, Jilin University SUMMARY Matching seismic wavefields and images lies at the heart of many pre-/post-processing
Progress Report on: Interferometric Interpolation of 3D SSP Data
Progress Report on: Interferometric Interpolation of 3D SSP Data Sherif M. Hanafy ABSTRACT We present the theory and numerical results for interferometrically interpolating and extrapolating 3D marine
G009 Scale and Direction-guided Interpolation of Aliased Seismic Data in the Curvelet Domain
G009 Scale and Direction-guided Interpolation of Aliased Seismic Data in the Curvelet Domain M. Naghizadeh* (University of Alberta) & M. Sacchi (University of Alberta) SUMMARY We propose a robust interpolation
SUMMARY. forward/inverse discrete curvelet transform matrices (defined by the fast discrete curvelet transform, FDCT, with wrapping
Seismic data processing with curvelets: a multiscale and nonlinear approach Felix J. Herrmann, EOS-UBC, Deli Wang, Jilin University and Gilles Hennenfent and Peyman Moghaddam SUMMARY In this abstract,
Azimuth Moveout Transformation some promising applications from western Canada
Azimuth Moveout Transformation some promising applications from western Canada Satinder Chopra and Dan Negut Arcis Corporation, Calgary, Canada Summary Azimuth moveout (AMO) is a partial migration operator
Exploration seismology and the return of the supercomputer
Exploration seismology and the return of the supercomputer Exploring scalability for speed in development and delivery Sverre Brandsberg-Dahl Chief Geophysicist, Imaging and Engineering Marine seismic
Optimized Compressed Sensing for Curvelet-based. Seismic Data Reconstruction
Optimized Compressed Sensing for Curvelet-based Seismic Data Reconstruction Wen Tang 1, Jianwei Ma 1, Felix J. Herrmann 2 1 Institute of Seismic Exploration, School of Aerospace, Tsinghua University, Beijing
C014 Shot Based Pre-Processing Solutions for a WATS Survey An Example from a Field Trial in Green Canyon Gulf of Mexico
C014 Shot Based Pre-Processing Solutions for a WATS Survey An Example from a Field Trial in Green Canyon Gulf of Mexico M. Magesan (CGGVeritas), J.-C. Ferran* (CGGVeritas), S. Kaculini (CGGVeritas), C.J.
Interpolation using asymptote and apex shifted hyperbolic Radon transform
Interpolation using asymptote and apex shifted hyperbolic Radon transform Amr Ibrahim, Paolo Terenghi and Mauricio D. Sacchi Department of Physics, University of Alberta PGS Abstract The asymptote and
Compressive least-squares migration with source estimation
Compressive least-squares migration with source estimation Philipp A. Witte*, Mengmeng Yang* and Felix J. Herrmann Seismic Laboratory for Imaging and Modeling (SLIM), University of British Columbia Email:
Random noise attenuation using local signal-and-noise orthogonalization a
Random noise attenuation using local signal-and-noise orthogonalization a a Published in Geophysics, 80, no. 6, WD1-WD9, (2015) Yangkang Chen and Sergey Fomel ABSTRACT We propose a novel approach to attenuate
Mostafa Naghizadeh and Mauricio D. Sacchi
Ground-roll elimination by scale and direction guided curvelet transform Mostafa Naghizadeh and Mauricio D. Sacchi Summary We propose a curvelet domain strategy to subtract ground-roll from seismic records.
SUMMARY. denoise the original data at each iteration. This can be
A comparison of D reconstruction methods Aaron Stanton*, Nadia Kreimer, David Bonar, Mostafa Naghizadeh, and Mauricio Sacchi, Department of Physics, University of Alberta SUMMARY A comparison is made between
A Nuclear Norm Minimization Algorithm with Application to Five Dimensional (5D) Seismic Data Recovery
A Nuclear Norm Minimization Algorithm with Application to Five Dimensional (5D) Seismic Data Recovery Summary N. Kreimer, A. Stanton and M. D. Sacchi, University of Alberta, Edmonton, Canada kreimer@ualberta.ca
Mitigation of the 3D Cross-line Acquisition Footprint Using Separated Wavefield Imaging of Dual-sensor Streamer Seismic
Mitigation of the 3D Cross-line Acquisition Footprint Using Separated Wavefield Imaging of Dual-sensor Streamer Seismic A.S. Long* (PGS), S. Lu (PGS), D. Whitmore (PGS), H. LeGleut (PGS), R. Jones (Lundin
SINBAD 2016 Consortium Meeting Oct 25-26, Houston
SINBAD 2016 Consortium Meeting Oct 25-26, Houston Seismic Laboratory for Imaging and Modeling Department of Earth, Ocean and Atmospheric Sciences University of British Columbia 2207 Main Mall Vancouver,
Shot-based pre-processing solutions for wide azimuth towed streamer datasets
first break volume 25, March 2007 focus on azimuth Shot-based pre-processing solutions for wide azimuth towed streamer datasets Philippe Herrmann, 1* Gordon Poole, 2 Antonio Pica, 1 Sylvain Le Roy, 1 and
Flexi Binning : A Solution for Irregular Acquisition Geometry
5th Conference & Exposition on Petroleum Geophysics, Hyderabad-2004, India PP 199-203 Flexi Binning : A Solution for Irregular Acquisition Geometry M. S. Rawat 1, M. Goswamy 1 & M. Das 2 1 GEOPIC, ONGC,
Extending the search space of time-domain adjoint-state FWI with randomized implicit time shifts
Extending the search space of time-domain adjoint-state FWI with randomized implicit time shifts Mathias Louboutin * and Felix J. Herrmann Seismic Laboratory for Imaging and Modeling (SLIM), The University
Module 4. K-Space Symmetry. Review. K-Space Review. K-Space Symmetry. Partial or Fractional Echo. Half or Partial Fourier HASTE
MRES 7005 - Fast Imaging Techniques Module 4 K-Space Symmetry Review K-Space Review K-Space Symmetry Partial or Fractional Echo Half or Partial Fourier HASTE Conditions for successful reconstruction Interpolation
Recovering the Reflectivity Matrix and Angledependent Plane-wave Reflection Coefficients from Imaging of Multiples
Recovering the Reflectivity Matrix and Angledependent Plane-wave Reflection Coefficients from Imaging of Multiples A. Ordoñez* (PGS), W.F. Sollner (PGS), T. Klüver (PGS) & L.G. Gelius (UiO) SUMMARY A joint
Sparse wavelet expansions for seismic tomography: Methods and algorithms
Sparse wavelet expansions for seismic tomography: Methods and algorithms Ignace Loris Université Libre de Bruxelles International symposium on geophysical imaging with localized waves 24 28 July 2011 (Joint
Seismic physical modeling I: acquisition of 2D and 3D surveys involving HTI targets
Physical modeling of 2D and 3D surveys Seismic physical modeling I: acquisition of 2D and 3D surveys involving HTI targets Joe Wong ABSTRACT Scaled-down physical modeling of 2D land and 3D marine seismic
Th Towards Improved Time-lapse Seismic Repetition Accuracy by Use of Multimeasurement Streamer Reconstruction
Th-11-02 Towards Improved Time-lapse Seismic Repetition Accuracy by Use of Multimeasurement Streamer Reconstruction P.J. Smith* (WesternGeco), J. Thekkekara (WesternGeco), G. Byerley (Apache North Sea
Multicomponent land data pre-processing for FWI: a benchmark dataset
Multicomponent land data pre-processing for FWI: a benchmark dataset Raul Cova, Bernie K. Law and Kris Innanen CRWES/University of Calgary Summary Successful full-waveform inversion (FWI) studies using
TERM PAPER ON The Compressive Sensing Based on Biorthogonal Wavelet Basis
TERM PAPER ON The Compressive Sensing Based on Biorthogonal Wavelet Basis Submitted By: Amrita Mishra 11104163 Manoj C 11104059 Under the Guidance of Dr. Sumana Gupta Professor Department of Electrical
SEG Houston 2009 International Exposition and Annual Meeting. paper are the work of TGS-Nopec on a subset of the Freedom WAZ data.
Freedom Wide-zimuth Processing and Imaging, a case history study of WZ imaging in Mississippi Canyon, Gulf of Mexico Simon aldock*, Roy Camp, James Cai, in Wang and Xuening Ma, TGS-Nopec Geophysical Company
Reflection seismic Method - 3D
Reflection seismic Method - 3D 3-D acquisition 3-D binning Land Marine 3-D data processing and display Reading: Sheriff and Geldart, Chapter 12 Land 3-D acquisition Key considerations: Cost minimize the
SEISMIC INTERPOLATION VIA CONJUGATE GRADIENT PURSUIT L. Fioretti 1, P. Mazzucchelli 1, N. Bienati 2 1
SEISMIC INTERPOLATION VIA CONJUGATE GRADIENT PURSUIT L. Fioretti 1, P. Mazzucchelli 1, N. Bienati 2 1 Aresys, Milano, Italy 2 eni E&P, San Donato Milanese, Italy Introduction. Seismic processing methods
We ELI1 02 Evaluating Ocean-bottom Seismic Acquisition in the North Sea - A Phased Survey Design Case Study
We ELI1 02 Evaluating Ocean-bottom Seismic Acquisition in the North Sea - A Phased Survey Design Case Study M. Branston* (Schlumberger Geosolutions), R. Campbell (Schlumberger Geosolutions), M. Rowlands
Tu N Internal Multiple Attenuation on Radial Gathers With Inverse-scattering Series Prediction
Tu N114 05 Internal Multiple Attenuation on Radial Gathers With Inverse-scattering Series Prediction F. Xavier de Melo* (Schlumberger), C. Kostov (Schlumberger) & J. Wu (Schlumberger) SUMMARY We present
Internal Multiple Attenuation on Radial Gathers With Inverse- Scattering Series Prediction
Internal Multiple Attenuation on Radial Gathers With Inverse- Scattering Series Prediction Frederico Xavier de Melo, Clément Kostov, James Wu Schlumberger Summary We present a novel workflow for pre-stack
Interpolation with pseudo-primaries: revisited
Stanford Exploration Project, Report 129, May 6, 2007, pages 85 94 Interpolation with pseudo-primaries: revisited William Curry ABSTRACT Large gaps may exist in marine data at near offsets. I generate