Les mer om Leiv J. Gelius på engelsk webprofil.
Undervisning
GEO4260 – Reservoir geophysics / GEO9260 – Reservoir geophysics
GEO4280 – Seismic Signal Processing and Imaging / GEO9280 – Seismic Signal Processing and Imaging
GeoCLASS; Et omfattende e-læringssystem som dekker petroleumsgeofysikk, utviklet i samarbeid med UiB med støtte fra Statoil/Hydro. GeoCLASS har vært brukt som pensum på MCs-emner ved UiO og UiB i mange år. Programmet er komersialisert gjennom UniGEO.
Emneord:
Geofysikk,
Avbildning,
Seismisk analyse,
Reservoargeofysikk,
Miljøgeofysikk
Publikasjoner
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Zizi, Mohammed Outhmane Faouzi; Turquais, Pierre; Day, Anthony; Pedersen, Morten W. & Gelius, Leiv-J.
(2024).
Low-frequency seismic deghosting in a compressed domain using parabolic dictionary learning.
Geophysical Prospecting.
ISSN 0016-8025.
doi:
10.1111/1365-2478.13475.
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Corseri, Romain; Planke, Sverre; Gelius, Leiv Jacob; Faleide, Jan Inge; Senger, Kim & Abdelmalak, Mohamed Mansour
(2022).
Magnetotelluric image of a hyper-extended and serpentinized rift system.
Earth and Planetary Science Letters.
ISSN 0012-821X.
602.
doi:
10.1016/j.epsl.2022.117914.
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Magnetotelluric (MT) data can image the Earth's electrical resistivity down to the mantle but are rarely used for investigation of offshore rifted margins. In such settings, the lower crust and upper mantle are altered by distinct tectono-thermal processes but often display similar seismic velocities and densities. By integrating resistivity models from MT data, we aim at resolving such ambiguity. Here, 3D inversion of long period, marine MT data (1 – 3000 s) is performed on 104 receivers located along two, ∼300 km long transects in the SW Barents Sea. The resolving power of MT data is assessed with synthetic tests in an archetypal rift system where ample crustal thickness variation occurs. The results highlight that our MT data sense the transition from necking to hyper-extended domain where the crust (<10 km) is not recovered by 3D inversion. In the Bjørnøya Basin – the northernmost member of a hyper-extended Cretaceous basin chain in the NE Atlantic – we combine seismic interpretation and MT inversion models to assign resistivity properties at two depth intervals: (1) 0.1-1 Ωm within Lower Cretaceous marine shales buried at 10-15 km depth (2) 1-10 Ωm within the uppermost mantle. Based on a fluid-rock model, we emphasize that seawater as a sole pore fluid phase is not conductive enough to explain such high bulk conductivities at both intervals. A 25% serpentinization of mantle rocks can account for a fivefold rise in salinity of the residual fluid and is compatible with bulk resistivity, density, and seismic velocities in the Bjørnøya Basin. Such high-salinity fluid can ascend and mix with seawater in pore spaces of the sediments, supporting our model of saline fluid circulation in hyper-extended basins. In conclusion, electrical resistivity models can disambiguate interpretation of deep structures in rifted margin by detecting saline fluids from partial serpentinization, intermixing with seawater in overlying marine sediments.
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Xue, Junjie; Cheng, Jiuolong; Gelius, Leiv-J.; Wu, Xing & Zhao, Yang
(2022).
Full Waveform Inversion of Transient Electromagnetic Data in the Time Domain.
IEEE Transactions on Geoscience and Remote Sensing.
ISSN 0196-2892.
60.
doi:
10.1109/TGRS.2022.3202739.
Fulltekst i vitenarkiv
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Sun, Jing; Hou, Song; Vinje, Vetle; Poole, Gordon & Gelius, Leiv-J.
(2022).
Deep learning-based shot-domain seismic deblending.
Geophysics.
ISSN 0016-8033.
87(3),
s. 215–226.
doi:
10.1190/geo2020-0865.1.
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Thorkildsen, Vemund Stenbekk; Gelius, Leiv-J. & Robinson, Enders A.
(2021).
Revisiting holistic migration.
The Leading Edge.
ISSN 1070-485X.
40(10),
s. 768–777.
doi:
10.1190/tle40100768.1.
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Greiner, Thomas Larsen; Lie, Jan Erik; Kolbjørnsen, Odd; Evensen, Andreas Kjelsrud; Nilsen, Espen Harris & Zhao, Hao
[Vis alle 8 forfattere av denne artikkelen]
(2021).
Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction.
Geophysics.
ISSN 0016-8033.
87(2),
s. 59–73.
doi:
10.1190/geo2021-0099.1.
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In 3D marine seismic acquisition, the seismic wavefield is not sampled uniformly in the spatial directions. This leads to a seismic wavefield consisting of irregularly and sparsely populated traces with large gaps between consecutive sail-lines especially in the near-offsets. The problem of reconstructing the complete seismic wavefield from a subsampled and incomplete wavefield, is formulated as an underdetermined inverse problem. We investigate unsupervised deep learning based on a convolutional neural network (CNN) for multidimensional wavefield reconstruction of irregularly populated traces defined on a regular grid. The proposed network is based on an encoder-decoder architecture with an overcomplete latent representation, including appropriate regularization penalties to stabilize the solution. We proposed a combination of penalties, which consists of the L2-norm penalty on the network parameters, and a first- and second-order total-variation (TV) penalty on the model. We demonstrate the performance of the proposed method on broad-band synthetic data, and field data represented by constant-offset gathers from a source-over-cable data set from the Barents Sea. In the field data example we compare the results to a full production flow from a contractor company, which is based on a 5D Fourier interpolation approach. In this example, our approach displays improved reconstruction of the wavefield with less noise in the sparse near-offsets compared to the industry approach, which leads to improved structural definition of the near offsets in the migrated sections.
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Corseri, Romain; Planke, Sverre; Faleide, Jan Inge; Senger, Kim; Gelius, Leiv-J. & Johansen, Ståle Emil
(2021).
Opportunistic magnetotelluric transects from CSEM surveys in the Barents Sea.
Geophysical Journal International.
ISSN 0956-540X.
227(3),
s. 1832–1845.
doi:
10.1093/gji/ggab312.
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Jensen, Kristian; Lecomte, Isabelle Christine; Gelius, Leiv-J. & Kaschwich, Tina
(2021).
Point-spread function convolution to simulate prestack depth migrated images: A validation study.
Geophysical Prospecting.
ISSN 0016-8025.
69(8-9),
s. 1571–1590.
doi:
10.1111/1365-2478.13132.
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Seismic migration commonly yields an incomplete reconstruction of the Earth model due to restricted survey aperture, band-limited frequency content, and propagation effects. This affects both illumination and resolution of the structures of interest. Through the application of spatial convolution operators commonly referred to as point-spread functions, simulated prestack depth migrated images incorporating these effects may be obtained. Such simulated images are tailored for analysing distortion effects and enhance our understanding of seismic imaging and subsequent interpretation. Target-oriented point-spread functions may be obtained through a variety of waveform and ray-based approaches. Waveform approaches are generally more robust, but the computational cost involved may be prohibitive. Ray-based approaches, on the other hand, allow for efficient and flexible sensitivity studies at a low computational cost, but inherent limitations may lead to less accuracy. To yield more insight into the similarities and differences between point-spread functions obtained via these two approaches, we first derive analytical expressions of both wave- and ray-based point-spread functions in homogeneous media. By considering single point scatterers embedded in a uniform velocity field, we demonstrate the conditions under which the derived equations diverge. The accuracy of wave-based and ray-based point-spread functions is further assessed and validated at selected targets in a subsection of the complex BP Statics Benchmark model. We also compare our simulated prestack depth migrated images to the output obtained from an actual prestack depth migration (reverse time migration). Our results reveal that both the wave- and ray-based approaches accurately model illumination, resolution and amplitude effects observed in the reverse time migrated image. Furthermore, although some minor deviations between the wave-based and ray-based approaches are observed, the overall results indicate that both approaches can be used, also for complex models.
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Hlebnikov, Volodya; Elboth, Thomas; Vinje, Vetle & Gelius, Leiv-J.
(2021).
Noise types and their attenuation in towed marine seismic: A tutorial.
Geophysics.
ISSN 0016-8033.
86(2).
doi:
10.1190/geo2019-0808.1.
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Sun, Jing; Slang, Sigmund; Elboth, Thomas; Greiner, Thomas Larsen; McDonald, Steven & Gelius, Leiv-J.
(2020).
A convolutional neural network approach to deblending seismic data.
Geophysics.
ISSN 0016-8033.
85(4),
s. WA13–WA26.
doi:
10.1190/geo2019-0173.1.
Fulltekst i vitenarkiv
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Hlebnikov, Volodya; Elboth, Thomas; Vinje, Vetle & Gelius, Leiv-J.
(2019).
Onboard de-noise processing for improved towed marine seismic acquisition efficiency.
I SEG, . (Red.),
SEG Technical Program Expanded Abstracts 2019.
SEG.
ISSN 0916160009.
s. 47–51.
doi:
10.1190/segam2019-3212338.1.
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Greiner, Thomas Larsen; Kolbjørnsen, Odd; Lie, Jan Erik; Harris Nilsen, Espen; Kjeldsrud Evensen, Andreas & Gelius, Leiv-J.
(2019).
Cross-streamer wavefield interpolation using deep convolutional networks.
I SEG, . (Red.),
SEG Technical Program Expanded Abstracts 2019.
SEG.
ISSN 0916160009.
s. 2207–2211.
doi:
https:/doi.org/10.1190/segam2019-3214009.1.
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Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these settings. In addition, large streamer separation introduce aliasing of spatial frequencies across the streamers. A new marine survey configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem. However, introduction of near offset data has shown to be challenging for interpolation and regularization, using conventional methods. In this paper, we investigate deep learning as a tool for interpolation beyond spatial aliasing across the streamers, in the shot domain. The proposed method is based on imaging techniques from single-image super resolution (SISR). The model architecture consist of a deep convolutional neural network (CNN) and a periodic resampling layer for upscaling to the non-aliased wavefield. We demonstrate the performance of proposed method on representative broad-band synthetic data and TopSeis field data from the Barents Sea.
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Losnedahl, Sander Wågenes; Orji, Okwudili Chuks; Gelius, Leiv-J. & Sollner, Walter
(2019).
Marine vibrator: Source wavefield modeling.
I SEG, . (Red.),
SEG Technical Program Expanded Abstracts 2019.
SEG.
ISSN 0916160009.
s. 3864–3868.
doi:
10.1190/segam2019-3215004.1.
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Zhao, Hao; Gelius, Leiv-J.; Tygel, Martin; Harris Nilsen, Espen & Kjelsrud Evensen, Andreas
(2019).
3D Prestack Fourier Mixed-Domain (FMD) depth migration for VTI media with large contrasts.
Journal of Applied Geophysics.
ISSN 0926-9851.
168,
s. 118–127.
doi:
10.1016/j.jappgeo.2019.06.009.
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Waldeland, Anders U.; Coimbra, T.A.; Faccipieri, J. H.; Solberg, Anne H Schistad & Gelius, Leiv-J.
(2019).
Fast estimation of prestack Common Reflection Surface parameters.
Geophysical Prospecting.
ISSN 0016-8025.
67(5),
s. 1163–1183.
doi:
10.1111/1365-2478.12740.
Vis sammendrag
We present a method for fast estimation of finite offset common reflection surface parameters. Firstly, the derivatives with respect to offset are derived from the velocity guide. Secondly, we apply structure tensors to extract the derivatives with respect to midpoint from stacked common offset sections. Finally, the mixed derivative is estimated using a one‐parametric semblance search. The proposed method is compared to the global five‐parametric semblance search and the pragmatic sequential two‐parametric semblance search on one synthetic and one real data set. The experiments show that the proposed method is more robust against noise than the pragmatic search and have comparable robustness with the global search. The proposed method smoothes parameter estimates in a local window, and the window size is set to give the best trade‐off between detail and robustness. Since the proposed method is dependent on a velocity guide, the quality of the other parameter estimates may be influenced by any inaccuracies in the guide. The main advantage of the proposed method is the computational efficiency. When compared with a gridded implementation of the semblance search, the proposed method is 10 and 400 times faster than the pragmatic and global search. Alternative search strategies significantly reduce the computational cost of the global search. However, since more than 99% of the computational cost of the proposed method comes from the semblance search to estimate the mixed derivative, it is expected that such techniques also reduce the computational cost for the proposed method.
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Shantsev, Daniil; Nerland, E.A.; Babakhani, Amir & Gelius, Leiv-J.
(2018).
Relaxed repeatability requirements for 4D marine CSEM: Inversion study.
SEG Technical Program Extended Abstract 2018.
SEGEAB.
ISSN 978-5-8670-8577-3.
s. 934–938.
doi:
10.1190/segam2018-2986235.1.
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Coimbra, T.A.; Faccipieri, J. H.; Speglich, J.H.; Gelius, Leiv-J. & Tygel, M
(2018).
Enhancement of diffractions in prestack domain by means of a finite-offset double-square-root traveltime.
Geophysics.
ISSN 0016-8033.
84(1),
s. V81–V96.
doi:
10.1190/geo2018-0160.1.
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Turquais, Pierre; Asgedom, Endrias Getachew; Sollner, Walter & Gelius, Leiv-J.
(2018).
Parabolic Dictionary Learning for Seismic Wavefield Reconstruction Across the Streamers.
Geophysics.
ISSN 0016-8033.
83(4),
s. V263–V282.
doi:
10.1190/geo2017-0694.1.
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Waldeland, Anders Ueland; Zhao, Hao; Faccipieri, J. H.; Solberg, Anne H Schistad & Gelius, Leiv-J.
(2018).
Fast and robust common-reflection-surface parameter estimation.
Geophysics.
ISSN 0016-8033.
83(1),
s. O1–O13.
doi:
10.1190/GEO2017-0113.1.
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The common-reflection-surface (CRS) method offers a stack with higher signal-to-noise ratio at the cost of a time-consuming semblance search to obtain the stacking parameters. We have developed a fast method for extracting the CRS parameters using local slope and curvature. We estimate the slope and curvature with the gradient structure tensor and quadratic structure tensor on stacked data. This is done under the assumption that a stacking velocity is already available. Our method was compared with an existing slope-based method, in which the slope is extracted from prestack data. An experiment on synthetic data shows that our method has increased robustness against noise compared with the existing method. When applied to two real data sets, our method achieves accuracy comparable with the pragmatic and full semblance searches. Our method has the advantage of being approximately two and four orders of magnitude faster than the semblance searches.
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Waldeland, Anders Ueland; Jensen, Are Charles; Gelius, Leiv-J. & Solberg, Anne H Schistad
(2018).
Convolutional neural networks for automated seismic interpretation.
The Leading Edge.
ISSN 1070-485X.
37(7),
s. 529–537.
doi:
10.1190/tle37070529.1.
Vis sammendrag
Deep-learning methods have proved successful recently for solving problems in image analysis and natural language processing. One of these methods, convolutional neural networks (CNNs), is revolutionizing the field of image analysis and pushing the state of the art. CNNs consist of layers of convolutions with trainable filters. The input to the network is the raw image or seismic amplitudes, removing the need for feature/attribute engineering. During the training phase, the filter coefficients are found by iterative optimization. The network thereby learns how to compute good attributes to solve the given classification task. However, CNNs require large amounts of training data and must be carefully designed and trained to perform well. We look into the intuition behind this method and discuss considerations that must be made in order to make the method reliable. In particular, we discuss how deep learning can be used for automated seismic interpretation. As an example, we show how a CNN can be used for automatic interpretation of salt bodies.
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Pedersen, H M; Gelius, Leiv-J. & Stamnes, J J
(2016).
Numerical and Physical Modeling of Diffractions.
I Klem-Musatov, Kamill D.; Hoeber, H; Pelissier, M & Moser, T J (Red.),
Seismic Difraction.
Society of Exploration Geophysicists (SEG).
ISSN 9781560803171.
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Ordonez, Alba; Sollner, Walter; Kluver, T. & Gelius, Leiv-J.
(2016).
Subsurface reflectivity estimation from imaging of primaries and multiples using amplitude-normalized separated wavefields.
Geophysics.
ISSN 0016-8033.
81(3),
s. S101–S117.
doi:
10.1190/GEO2015-0385.1.
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Faccipieri, J. H.; Coimbra, T.A.; Gelius, Leiv-J. & Tygel, M.
(2016).
Stacking apertures and estimation strategies for reflection and diffraction enhancement.
Geophysics.
ISSN 0016-8033.
81(4),
s. V271–V282.
doi:
10.1190/GEO2015-0525.1.
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Gelius, Leiv-J. & Tygel, Martin
(2015).
Migration-velocity building in time and depth from 3D (2D) Common-Reflection-Surface (CRS) stacking - theoretical framework.
Studia Geophysica et Geodaetica.
ISSN 0039-3169.
59(2),
s. 253–282.
doi:
10.1007/s11200-014-1036-6.
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Xue, G.Q.; Li, X; Gelius, Leiv-J.; Qi, Z.; Zhou, N & Chen, W
(2015).
A New Apparent Resistivity Formula for In-loop Fast Sounding TEM - theory and applications.
Journal of Environmental & Engineering Geophysics.
ISSN 1083-1363.
20(2),
s. 107–118.
doi:
10.2113/JEEG20.2.107.
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Xue, G.Q.; Yan, S.; Gelius, Leiv-J.; Chen, W; Zhou, N & Hai, L.
(2015).
Discovery of a major coal deposit in China with the use of a modified CSAMT method.
Journal of Environmental & Engineering Geophysics.
ISSN 1083-1363.
20(1),
s. 47–56.
doi:
10.2113/JEEG20.1.47.
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Gelius, Leiv-J.
(2014).
High-resolution imaging of seismic data: how to combine wave-theory with signal processing techniques.
I IEEE, Org. (Red.),
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
IEEE.
ISSN 978-1-4799-2892-7.
doi:
10.1109/icassp.2014.6854023.
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Ordonez, Alba; Sollner, Walter; Kluver, T. & Gelius, Leiv-J.
(2014).
Migration of primaries and multiples using an imaging condition for amplitude-normalized separated wavefields.
Geophysics.
ISSN 0016-8033.
79(5),
s. S217–S230.
doi:
10.1190/GEO2013-0346.1.
Vis sammendrag
Migration of primary and multiple reflections leads to enhanced subsurface illumination and to increased image resolution. A joint migration approach using the complete wavefield requires properly imaged primaries and multiples of all orders. In recent works, primaries and multiples have therefore been imaged separately, using the upgoing and downgoing pressure wavefields obtained by decomposing dual-sensor streamer data. The matches between the corresponding depth images are still not found to be sufficiently accurate, so new and more appropriate imaging conditions need to be found. We reviewed the classical imaging approach used in one-way wave-equation migration, with the aim of extending it for simultaneous migration of primaries and all orders of multiples. Based on Rayleigh’s reciprocity theorem and well-known theoretical developments, we derived a new imaging condition described in terms of the upgoing pressure wavefield and a filtered version of the downgoing vertical-velocity wavefield. To evaluate the efficiency of this new imaging approach, primaries and multiples were separately migrated using synthetic and real data examples. These results were compared to those obtained using a conventional imaging condition. We found that the use of the new imaging condition led to a better match between the depth images and spectra of the migrated primaries and migrated multiples.
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Orji, Okwundili Chuks; Sollner, Walter & Gelius, Leiv-J.
(2013).
Sea Surface Reflection Coefficient Estimation.
I SEG, . (Red.),
SEG Technical Program Expanded Abstracts 2013.
SEGEAB.
ISSN 1052-3812.
doi:
10.1190/segam2013-0944.1.
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Gelius, Leiv-J.; Tygel, Martin; Takahata, Andre Kazue; Asgedom, Endrias Getachew & Serrano, Dany R.
(2013).
High-resolution imaging of diffractions - A window-steered MUSIC approach.
Geophysics.
ISSN 0016-8033.
78(6),
s. S255–S264.
doi:
10.1190/GEO2013-0047.1.
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Faccipieri, J. H.; Gelius, Leiv-J. & Tygel, Martin
(2013).
Improved CMP and CRS Stacking by a Combined Use of Conventional and Automatic Velocity Estimation.
I EAGE, 2013 (Red.),
Extended abstracts book: 75th EAGE Conference and Technical Exhibition, London, UK.
European Association of Geoscientists and Engineers (EAGE).
ISSN 978-90-73834-48-4.
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Takahata, Andre Kazue; Gelius, Leiv-J.; Lopes, Renato R.; Tygel, Martin & Lecomte, Isabelle
(2013).
2D Spiking Deconvolution Approach to Resolution Enhancement of Prestack Depth Migrated Seismic Images.
I EAGE, 2013 (Red.),
Extended abstracts book: 75th EAGE Conference and Technical Exhibition, London, UK.
European Association of Geoscientists and Engineers (EAGE).
ISSN 978-90-73834-48-4.
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Faccipieri, J. H.; Serrano, D. R.; Gelius, Leiv-J. & Tygel, M
(2013).
Recovering diffractions in CRS stacked sections.
First Break.
ISSN 0263-5046.
31,
s. 27–31.
doi:
10.3997/1365-2397.2013014.
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Jensen, Erling Hugo; Gelius, Leiv-J.; Johansen, Tor Arne & Wang, Zhong
(2013).
Consistent joint elastic-electrical differential effective-medium modelling of compacting reservoir sandstones.
Geophysical Prospecting.
ISSN 0016-8025.
61(4),
s. 788–802.
doi:
10.1111/1365-2478.12014.
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Solberg, Anne H Schistad; Jensen, Are Charles & Gelius, Leiv-J.
(2012).
Robust 2D Hilbert Atributes of Local Amplitude and Phase.
I EAGE, 2012 (Red.),
74th EAGE Conference & Exhibition incorporating SPE EUROPEC 2012.
European Association of Geoscientists and Engineers (EAGE).
ISSN 978-90-73834-27-9.
Vis sammendrag
Instantaneous amplitude and phase are based on the 1D Hilbert transform of seismic traces. We present
alternative attributes with improved localization properties by applying a combination of orientationadaptive
Hilbert transforms and bandpass filtering. The concept of local amplitude and phase are first
explained for the 1D case, before extensions to 2D are given. In the 2D case, the process consists
conceptually of two steps: First, the dominant local orientation is estimated, and then the local amplitude
and phase are estimated along that orientation. The proposed attributes are compared to the classical
instantaneous attributes both for a thin-bed resolution image with a classical wedge model, and for a
seismic data set. They show better localization properties for the thin-bed wedge model, and a higher
robustness and lateral continuity on the real seismic data.
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Gelius, Leiv-J.
(2012).
Image resolution beyond the classical limit.
I Wang, Y; Yagola, A. G. & Yang, C (Red.),
Computational methods for applied inverse problems (inverse and ill-posed problems).
Walter de Gruyter (De Gruyter).
ISSN 978-3110259049.
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Orji, Okwundili Chuks; Sollner, Walter & Gelius, Leiv-J.
(2012).
Effects of time-varying sea surface in marine seismic data.
Geophysics.
ISSN 0016-8033.
77(3),
s. P33–P43.
doi:
10.1190/GEO2011-0361.1.
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Gelius, Leiv-J.
(2012).
3-D acoustic modelling of edge diffractions - revisited.
Studia Geophysica et Geodaetica.
ISSN 0039-3169.
56(2),
s. 433–456.
doi:
10.1007/s11200-011-9050-4.
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Orji, Okwundili Chuks; Sollner, Walter & Gelius, Leiv-J.
(2011).
Imaging time-varying sea surface using dual sensor data.
I Koster, Klaas (Red.),
Society of Exploration Geophysicists International Exposition and 81st Annual Meeting 2011 (SEG San Antonio 2011).
Curran Associates, Inc..
ISSN 9781618391841.
doi:
10.1190/1.3627901.
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Se alle arbeider i Cristin
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Xue, Guoqiang; Yin, Changchun; Macnae, James; Gelius, Leiv-J. & Hu, Xiangyun
(2024).
Geophysics for critical minerals - Introduction.
Geophysics.
ISSN 0016-8033.
89(1).
doi:
10.1190/geo2023-1117-spseintro.1.
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Greiner, Thomas Larsen; Kolbjørnsen, Odd; Lie, Jan Erik; Nilsen, Espen Harris; Evensen, Andreas Kjelsrud & Gelius, Leiv-J.
(2019).
Cross-streamer wavefield interpolation using deep convolutional neural network.
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Slang, Sigmund; Sun, Jing; Elboth, Thomas; McDonald, Steven & Gelius, Leiv-J.
(2019).
Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data.
EAGE Expanded Abstracts.
doi:
10.3997/2214-4609.201900844.
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Tømmerbakke, Ronny; Johansen, Tor Arne; Ruud, Bent Ole & Gelius, Leiv-J.
(2018).
Flexural Wave Attenuation in X-T
Domain by Complementary Ensemble Empirical Mode Decomposition.
Vis sammendrag
Introduction
Seismic surveying in polar regions are obstructed by a number factors and acquiring seismic data on frozen surface is commonly thought to be the best option with a stable surface to operate on. Acquiring on frozen sea ice also enables you to obtain a continuous seismic profile from land to sea. When acquiring on sea ice we get data contaminated by the flexural wave, masking the underlying reflections by both high amplitude and dispersive nature. Many methods to suppress the flexural wave have been proposed, in both acquisition and processing of the data. We propose a methodology based on ensemble empirical mode decomposition (EEMD) in space-time (x-t) domain to separate the flexural wave from the reflections.
EEMD
Empirical mode decomposition (EMD) is a data driven and adaptive decomposition of data to split the data into a series of intrinsic mode functions (IMF) representing different oscillations within the dataset. Originally, EMD struggled with mode mixing where frequency content split in various IMFs. EEMD was developed to improve performance with various levels of white noise added to create ensembles of IMFs and means of the ensembles are used as a final IMF output. The introduction of white noise means weak reflections can be masked and thorough testing is needed to avoid too high levels of white noise.
Results
Synthetic data containing flexural wave noise and reflections show good separation of flexural wave and reflections when reflections are nmo-corrected and in space-time (x-t) domain. Field data with geophones on sea ice surface and detonating cord as source with water depths ranging from zero to 50 m show similar separation of reflections and flexural wave. The field data contain shallow high velocity layers reverberating as multiples in the seismic record, which EEMD can similarly remove. In order to guide the velocity analysis needed to nmo-correct records pre-EEMD, sparse hydrophone recordings below sea ice where data is less affected by the flexural wave are used. Comparison with other methods of flexural wave removal show a better attenuation.
Conclusion
Implementing EEMD in x-t domain on nmo-corrected data we obtain a good separation of the wanted reflection signal and unwanted flexural wave. The EEMD methodology is easy to implement and improve the reliability of seismic data acquired on sea ice.
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Tømmerbakke, Ronny; Johansen, Tor Arne; Gelius, Leiv-J. & Ruud, Bent Ole
(2018).
Ensemble Empirical Mode Decomposition in X-T domain: A novel
approach to improve data quality in seismic acquisition on sea-ice.
-
Shantsev, Daniil; Babakhani, A.; Gelius, Leiv-J. & Nerland, E.A.
(2018).
How Repeatable 4D CSEM Surveys Need To Be?
-
Gelius, Leiv-J.
(2018).
Presentation of geophysical research activities at the University of Oslo.
-
Zhao, Hao; Gelius, Leiv-J. & Tygel, Martin
(2017).
Pre-stack Fourier Mixed-Domain (FMD) Depth Migration.
-
Zhao, Hao; Gelius, Leiv-J. & Tygel, Martin
(2015).
A New Generalized Screen Propagator for Wave Equation Depth Migration.
EAGE Expanded Abstracts.
doi:
10.3997/2214-4609.201412786.
-
Faccipieri, J. H.; Gelius, Leiv-J. & Tygel, Martin
(2013).
Diffraction separation based on perturbed CRS attributes.
-
Gelius, Leiv-J. & Tygel, Martin
(2013).
The cost function used in 3D mCSEM inversion – is the Born approximation valid?
-
Gelius, Leiv-J. & Tygel, Martin
(2013).
The 2D incremental ray-propagator.
-
Berthelot, Angélique; Solberg, Anne H Schistad; Morisbak, Erlend & Gelius, Leiv-J.
(2012).
3D segmentation of salt using texture attributes.
-
Asgedom, Endrias Getachew; Gelius, Leiv-J.; Faccipierri, Jorge H. & Tygel, Martin
(2012).
2-D pre- and post-stack diffraction separation and imaging.
-
Solberg, Anne H Schistad; Gelius, Leiv-J.; Jensen, Are Charles & Heeremans, Michel
(2012).
Phase attributes by considering local 3D structure orientation.
-
Gelius, Leiv-J. & Rathy, Sanchya
(2020).
Separation of Diffractions from Reflections by the Double Square Root Operator.
7Letras.
-
Thorkildsen, Vemund Stenbekk; Gelius, Leiv-J.; Harris Nilsen, Espen; Kjelsrud Evensen, Andreas & Lie, Jan Erik
(2019).
Separation of diffractions by diffraction-stacking and Plane-Wave Destruction filtering.
7Letras.
-
Losnedahl, Sander Wågønes; Gelius, Leiv-J.; Sollner, Walter; Asgedom, Endrias Getachew & Orji, Okwudili Chuks
(2019).
Marine Vibrators: Synthetic Data Study.
7Letras.
-
Slang, Sigmund; Gelius, Leiv-J. & Elboth, Thomas
(2019).
Attenuation of Seismic Interference Noise with Convolutional Neural Networks.
7Letras.
-
Turquais, Pierre; Sollner, Walter; Asgedom, Endrias Getachew; Gelius, Leiv-J. & Maupin, Valerie
(2018).
Dictionary learning and sparse representations for denoising and reconstruction of marine seismic data.
7Letras.
-
Waldeland, Anders U.; Solberg, Anne H Schistad & Gelius, Leiv-J.
(2018).
Seismic image analysis for applications related to iterative 3D velocity model building.
7Letras.
-
Grønhaug, Elise; Gelius, Leiv-J. & Elboth, Thomas
(2018).
Investigate noise attenuation by slope-preserving filtering.
7Letras.
-
Ordonez, Alba; Söllner, Walter; Klüver, Tilman & Gelius, Leiv-J.
(2016).
Subsurface imaging using multiples from amplitude-normalized separated wavefields: Application to marine towed-streamer data.
7Letras.
Vis sammendrag
POPULAR SCIENCE SUMMARY:
Imaging the subsurface is part of the process used in the interpretation of geological
structures that may potentially contain natural resources, such as oil and gas. These
subsurface images are mainly generated from seismic data acquired in marine
environment. Imaging is traditionally applied on seismic data only composed of primary
reflections, which are events reflected once in the subsurface and then returned to the
acquisition level. Multiple reflections, which bounce at least twice in the subsurface, are
commonly treated as noise and suppressed before imaging. However, multiple reflections
have the capability of illuminating subsurface points that primaries cannot reach.
Multiples also contain smaller reflection angles than primaries. These features can
therefore be exploited to improve the image quality and the information content of the
subsurface.
Based on seismic data acquired with multicomponent streamers comprising pressure and
vertical velocity sensors, the PhD student Alba Ordoñez Adellach proposes a new tool for
simultaneously imaging primary and multiple reflections. The main prerequisite for the
approach is the ability to separate the pressure and vertical velocity recordings into their
up- and downgoing components. The upgoing component of the pressure and the
downgoing component of the vertical velocity, both of them composed of primary and
multiple reflections, are combined to retrieve the reflection response of the subsurface.
This is mathematically accomplished by solving an integral equation for every image
point. The new imaging approach was successfully applied on several synthetic and field
data examples.
This doctoral thesis was carried out at the Department of Geosciences, University of Oslo
in collaboration with PGS Geophysical AS.
-
Gebregergs, Hagos G.; Gelius, Leiv-J. & Maupin, Valerie
(2016).
Compensation of absorption effects in seismic data.
7Letras.
-
Magnussen, Fredrik; Gelius, Leiv-J. & Sanches, Charlotte
(2015).
Deblending of seismic multi-component data.
7Letras.
-
Babakhani, Amir; Gelius, Leiv-J.; Shantsev, Daniil & Maupin, Valerie
(2015).
Repeatabiliy and Detectability Requirements for 4D CSEM surveys.
7Letras.
-
Grendaite, Milda; Gelius, Leiv-J. & Elboth, Thomas
(2014).
Identifying Seismic Interference Noise based on Local Dip Filtering.
7Letras.
-
Fetene, Deneke Admasu; Gelius, Leiv-J. & Vinje, Vetle
(2014).
A noise model for shallow water North Sea.
7Letras.
-
Umar, Muhammad; Gelius, Leiv-J. & Øverås, Rune
(2014).
Separation and attenuation of diffractions and their multiples.
7Letras.
-
Haghighi, Elyas; Gelius, Leiv-J.; Elboth, Thomas & Sanches, Charlotte
(2014).
Characterizing Seismic Interference from Seismic Multi-component data.
7Letras.
-
Asgedom, Endrias Getachew; Gelius, Leiv-J.; Austeng, Andreas & Holm, Sverre
(2012).
On the use of super-resolution algorithms in seismic: Application within diffraction separation and imaging.
7Letras.
-
Orji, Okwudili Chuks; Sollner, Walter & Gelius, Leiv-J.
(2012).
Sea surface height estimation for dual-sensor towed streamer.
7Letras.
-
Mahmoud, Uzma; Gelius, Leiv-J. & Elboth, Thomas
(2012).
Investigating different approaches of denoising seismic data.
7Letras.
-
Lindstrøm, Pia M.; Gelius, Leiv-J. & Zheng, Haishan
(2012).
Diffracted noise attenuation in seismic data.
7Letras.
-
Javed, Muhammad Waqas & Gelius, Leiv-J.
(2012).
Separation of reflections from diffractions using the CRS-technique.
7Letras.
-
Storbakk, Steffen; Gelius, Leiv-J.; Sanches, Charlotte & Rieder, Mark
(2012).
De-noising of marine seismic data.
7Letras.
Se alle arbeider i Cristin
Publisert
10. des. 2013 21:26
- Sist endret
17. jan. 2023 18:08