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Relevant bibliographies by topics / Seismic reflectivity

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Academic literature on the topic 'Seismic reflectivity'

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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Journal articles on the topic "Seismic reflectivity"

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Dey, Ayon K., and Larry R. Lines. "Reflectivity randomness revisited." GEOPHYSICS 64, no. 5 (1999): 1630–36. http://dx.doi.org/10.1190/1.1444668.

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In seismic exploration, statistical wavelet estimation and deconvolution are standard tools. Both of these processes assume randomness in the seismic reflectivity sequence. The validity of this assumption is examined by using well‐log synthetic seismograms and by using a procedure for evaluating the resulting deconvolutions. With real data, we compare our wavelet estimations with the in‐situ recording of the wavelet from a vertical seismic profile (VSP). As a result of our examination of the randomness assumption, we present a fairly simple test that can be used to evaluate the validity of a r

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Li, Yanqin, and Guoshan Zhang. "A Seismic Blind Deconvolution Algorithm Based on Bayesian Compressive Sensing." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/427153.

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Compressive sensing in seismic signal processing is a construction of the unknown reflectivity sequence from the incoherent measurements of the seismic records. Blind seismic deconvolution is the recovery of reflectivity sequence from the seismic records, when the seismic wavelet is unknown. In this paper, a seismic blind deconvolution algorithm based on Bayesian compressive sensing is proposed. The proposed algorithm combines compressive sensing and blind seismic deconvolution to get the reflectivity sequence and the unknown seismic wavelet through the compressive sensing measurements of the

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Dai, Ronghuo, Cheng Yin, and Da Peng. "An Application of Elastic-Net Regularized Linear Inverse Problem in Seismic Data Inversion." Applied Sciences 13, no. 3 (2023): 1525. http://dx.doi.org/10.3390/app13031525.

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In exploration geophysics, seismic impedance is a physical characteristic parameter of underground formations. It can mark rock characteristics and help stratigraphic analysis. Hence, seismic data inversion for impedance is a key technology in oil and gas reservoir prediction. To invert impedance from seismic data, one can perform reflectivity series inversion first. Then, under a simple exponential integration transformation, the inverted reflectivity series can give the final inverted impedance. The quality of the inverted reflectivity series directly affects the quality of impedance. Sparse

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Ursin, Bjørn. "Methods for estimating the seismic reflection response." GEOPHYSICS 62, no. 6 (1997): 1990–95. http://dx.doi.org/10.1190/1.1444299.

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The convolutional model of the seismic trace consists of a seismic pulse convolved with a reflectivity series plus measurement noise. The seismic deconvolution problem is to estimate the reflectivity series, given the data and an estimate of the seismic pulse. The classical solution to this problem is a weighted least‐squares estimate of the reflectivity series, which is optimal when the noise covariance matrix is known and there are no errors in the pulse. The seismic convolutional model has been reformulated, taking into account errors in the pulse and measurement noise, which is taken to be

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Chen, Fubin, Zhaoyun Zong, and Man Jiang. "Seismic reflectivity and transmissivity parametrization with the effect of normal in situ stress." Geophysical Journal International 226, no. 3 (2021): 1599–614. http://dx.doi.org/10.1093/gji/ggab179.

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SUMMARY In situ stress has a significant effect on the properties of underground formations, including seismic wave velocity, porosity and permeability, and further affects seismic reflectivity and transmissivity. Research works on the effect of in situ stress are helpful to construct more precise seismic reflection and transmission coefficient equations. However, previous studies on seismic reflectivity equations did not take the effect of normal in situ stress into consideration. The mechanism of stress on seismic reflectivity and transmissivity is still ambiguous. In this study, we propose

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Liang, Chen, John Castagna, and Marcelo Benabentos. "Reflectivity decomposition: Theory and application." Interpretation 9, no. 2 (2021): B7—B23. http://dx.doi.org/10.1190/int-2020-0203.1.

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Sparse reflectivity inversion of processed reflection seismic data is intended to produce reflection coefficients that represent boundaries between geologic layers. However, the objective function for sparse inversion is usually dominated by large reflection coefficients, which may result in unstable inversion for weak events, especially those interfering with strong reflections. We have determined that any seismogram can be decomposed according to the characteristics of the inverted reflection coefficients that can be sorted and subset by magnitude, sign, and sequence, and new seismic traces

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Wang, Lingling, Qian Zhao, Jinghuai Gao, Zongben Xu, Michael Fehler, and Xiudi Jiang. "Seismic sparse-spike deconvolution via Toeplitz-sparse matrix factorization." GEOPHYSICS 81, no. 3 (2016): V169—V182. http://dx.doi.org/10.1190/geo2015-0151.1.

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We have developed a new sparse-spike deconvolution (SSD) method based on Toeplitz-sparse matrix factorization (TSMF), a bilinear decomposition of a matrix into the product of a Toeplitz matrix and a sparse matrix, to address the problems of lateral continuity, effects of noise, and wavelet estimation error in SSD. Assuming the convolution model, a constant source wavelet, and the sparse reflectivity, a seismic profile can be considered as a matrix that is the product of a Toeplitz wavelet matrix and a sparse reflectivity matrix. Thus, we have developed an algorithm of TSMF to simultaneously de

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Wang, Ruo, and Yanghua Wang. "Multichannel algorithms for seismic reflectivity inversion." Journal of Geophysics and Engineering 14, no. 1 (2016): 41–50. http://dx.doi.org/10.1088/1742-2132/14/1/41.

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Barnes, Arthur E. "Moho reflectivity and seismic signal penetration." Tectonophysics 232, no. 1-4 (1994): 299–307. http://dx.doi.org/10.1016/0040-1951(94)90091-4.

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Ker, S., Y. Le Gonidec, L. Marié, Y. Thomas, and D. Gibert. "Multiscale seismic reflectivity of shallow thermoclines." Journal of Geophysical Research: Oceans 120, no. 3 (2015): 1872–86. http://dx.doi.org/10.1002/2014jc010478.

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Dissertations / Theses on the topic "Seismic reflectivity"

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Al-Moqbel, Abdulrahman Mohammad Saleh 1974. "Reservoir characterization using seismic reflectivity and attributes." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/51665.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2002.<br>Includes bibliographical references (leaves 81-82).<br>The primary objective of this thesis is to obtain reservoir properties, such as porosity from surface seismic data complemented by available well logs. To accomplish this a two-step procedure is followed. First, reflectivity and acoustic impedance profiles are obtained from the inversion of post-stack seismic data. Second, a multi-attribute analysis, calibrated using well logs, is used to obtain porosity. This procedure is ap

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Wang, Ruo. "Seismic reflectivity and impedance inversion in multichannel fashion." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/45497.

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Seismic reflectivity inversion is an important step in both signal processing and quantitative interpretation since reflectivity contains the information of impedance and other elastic parameters. Conventional methods assume stratified media and perform deconvolution on seismic data trace by trace. However, when using these single-channel methods, the lateral coherency of the result may be affected when the input seismic traces have low signal-to-noise ratios (SNRs) or complex structures. In this thesis, the development of multichannel inversion algorithms will be investigated to improve the c

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Okure, Maxwell Sunday. "Upper mantle reflectivity beneath an intracratonic basin : insights into the behavior of the mantle beneath Illinois basin /." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd865.pdf.

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Ay, Erkan. "Origin of crustal reflectivity and influence of fluids and fractures on velocity at the Kola superdeep borehole." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1453231711&sid=4&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Tango, Gerard Joseph. "Applications of a Direct Fast Field/Reflectivity Method to Wave Propagation Modeling in Underwater Acoustic and Solid Earth Seismic Environments." ScholarWorks@UNO, 1985. https://scholarworks.uno.edu/td/2684.

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A new method is discussed for exact rapid computation of the depth-dependent Green's function occuring in full integral solutions to the acoustic and elastic Helmholtz wave equation, allowing calculations of underwater acoustic propagation loss and full wavefield synthetic seismograms, in range-independent plane stratified media.

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Ehsan, Jamali Hondori. "Full waveform inversion of supershot-gathered data for optimization of turnaround time in seismic reflection survey." Kyoto University, 2016. http://hdl.handle.net/2433/217744.

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Herrmann, Felix J., and Peyman P. Moghaddam. "Curvelet-domain preconditioned "wave-equation" depth-migration with sparseness and illumination constraints." Society of Exploration Geophysicists, 2004. http://hdl.handle.net/2429/430.

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A non-linear edge-preserving solution to the least-squares migration problem with sparseness & illumination constraints is proposed. The applied formalism explores Curvelets as basis functions. By virtue of their sparseness and locality, Curvelets not only reduce the dimensionality of the imaging problem but they also naturally lead to a dense preconditioning that almost diagonalizes the normal/Hessian operator. This almost diagonalization allows us to recast the imaging problem into a ’simple’ denoising problem. As such, we are in the position to use non-linear estimators based on thresholdi

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Lumley, David Edward. "A generalized Kirchhoff-WKBJ depth migration theory for multi-offset seismic reflection data : reflectivity model construction by wavefield imaging and amplitude estimation." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/27588.

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This thesis embodies a mathematical, physical, and quantitative investigation into the imaging and amplitude estimation of subsurface earth reflectivity structure within the framework of pre stack wave-equation depth migration of multi-offset seismic reflection data. Analysis is performed on five prestack depth migration reflectivity "imaging conditions" with respect to image quality and quantitative accuracy of recovered reflectivity amplitudes. A new computationally efficient and stable prestack depth migration imaging method is proposed which is based upon a geometric approximation to the

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Adedeji, Elijah A. "3D Post-stack Seismic Inversion using Global Optimization Techniques: Gulf of Mexico Example." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2231.

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Seismic inversion using a global optimization algorithm is a non-linear, model-driven process. It yields an optimal solution of the cost function – reflectivity/acoustic impedance, when prior information is sparse. The inversion result offers detailed interpretations of thin layers, internal stratigraphy, and lateral continuity and connectivity of sand bodies. This study compared two stable and robust global optimization techniques, Simulated Annealing (SA) and Basis Pursuit Inversion (BPI) as applied to post-stack seismic data from the Gulf of Mexico. Both methods use different routines and c

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Merrett, H. D. "2D lithospheric imaging of the Delamerian and Lachlan Orogens, southwestern Victoria, Australia from Broadband Magnetotellurics." Thesis, 2016. http://hdl.handle.net/2440/121124.

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This item is only available electronically.<br>A geophysical study utilising the method of magnetotellurics (MT) was carried out across southwestern Victoria, Australia, imaging the electrical resistivity structure of the lithosphere beneath the Delamerian and Lachlan Orogens. Broadband MT (0.001-1000 Hz) data were collected along a 160 km west-southwest to east-northeast transect adjacent to crustal seismic profiling. Phase tensor analyses from MT responses reveal a distinct change in electrical resistivity structure and continuation further southwards of the Glenelg and Grampians-Stavely g

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Books on the topic "Seismic reflectivity"

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Lee, Myung W. Statistical property of the earth reflectivity and fractal seismic deconvolution. U.S. Geological Survey, 1995.

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Book chapters on the topic "Seismic reflectivity"

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Sen, Mrinal K. "Seismic, Reflectivity Method." In Encyclopedia of Solid Earth Geophysics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-10475-7_50-1.

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Sen, Mrinal K. "Seismic, Reflectivity Method." In Encyclopedia of Solid Earth Geophysics. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-8702-7_50.

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Sen, Mrinal K. "Seismic, Reflectivity Method." In Encyclopedia of Solid Earth Geophysics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58631-7_50.

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Bittner, R., and Th Wever. "Reflectivity variations of Variscan terranes in Germany." In Continental Lithosphere: Deep Seismic Reflections. American Geophysical Union, 1991. http://dx.doi.org/10.1029/gd022p0087.

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Wu, Jianjun, and Robert F. Mereu. "Seismic reflectivity patterns of the Kapuskasing structural zone." In Continental Lithosphere: Deep Seismic Reflections. American Geophysical Union, 1991. http://dx.doi.org/10.1029/gd022p0047.

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Siegesmund, S., M. Fritzsche, and G. Braun. "Reflectivity caused by texture-induced anisitropy in mylonites." In Continental Lithosphere: Deep Seismic Reflections. American Geophysical Union, 1991. http://dx.doi.org/10.1029/gd022p0291.

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Sadowiak, Petra, Rolf Meissner, and Larry Brown. "Seismic reflectivity patterns: Comparative investigations of Europe and North America." In Continental Lithosphere: Deep Seismic Reflections. American Geophysical Union, 1991. http://dx.doi.org/10.1029/gd022p0363.

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Picetti, Francesco. "How Deep Learning Can Help Solving Geophysical Inverse Problems." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_12.

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AbstractThis brief summarizes some of the main results I obtained during my Ph.D. studies at Politecnico di Milano, under the supervision of Professor Stefano Tubaro. The thesis provides contributions to understanding the advantages, and limitations, of data-driven deep learning approaches to geophysical inverse problems, with a special focus on Convolutional Neural Networks (CNNs). Exploration Geophysics aims at estimating accurate physical properties of the Earth subsurface from seismic data acquired close to the surface. Seismic data show a great variety of statistically relevant and independent patterns. I devise Deep Learning methods to solve several geophysical tasks by learning such patterns. First, I devise generative networks as a post-processing operator for refining reflectivity images. When trained on pure image datasets, these networks suffer from the lack of physical knowledge. Then, I show a different approach named Deep Priors, which are CNNs that precondition the inverse problem. In particular, I develop a scheme to interpolate seismic data. Finally, I leverage the features extraction ability of CNNs for buried landmine detection on Ground Penetrating Radar (GPR) acquisitions. While the presented methods are effective compared to the state of the art, improvements can be achieved by integrating pure data-driven algorithms within general inverse problems theory through a-priori information derived from domain knowledge.

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Howie, John M., Tom Parsons, and George A. Thompson. "High-resolution P- and S-wave deep crustal imaging across the edge of the Colorado Plateau, USA : Increased reflectivity caused by initiating extension." In Continental Lithosphere: Deep Seismic Reflections. American Geophysical Union, 1991. http://dx.doi.org/10.1029/gd022p0021.

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Nowack, Robert L., and Stephen M. Stacy. "Synthetic Seismograms and Wide-angle Seismic Attributes from the Gaussian Beam and Reflectivity Methods for Models with Interfaces and Velocity Gradients." In Seismic Waves in Laterally Inhomogeneous Media. Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8146-3_4.

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Conference papers on the topic "Seismic reflectivity"

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Zhang, Rui, and Bo Zhang. "Seismic reflectivity attributes." In SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, 2015. http://dx.doi.org/10.1190/segam2015-5746900.1.

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Yang, Z., and H. Cao. "Reflectivity Dispersion for Gas Detection." In EAGE Workshop on Seismic Attenuation. EAGE Publications BV, 2013. http://dx.doi.org/10.3997/2214-4609.20131860.

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Whitmore, N. D., J. Ramos-Martinez, Y. Yang, and A. A. Valenciano. "Seismic modeling with vector reflectivity." In SEG Technical Program Expanded Abstracts 2020. Society of Exploration Geophysicists, 2020. http://dx.doi.org/10.1190/segam2020-3424516.1.

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Wang, Y., and X. Lu. "Sparseness of Seismic Reflectivity Inversion." In 71st EAGE Conference and Exhibition incorporating SPE EUROPEC 2009. European Association of Geoscientists & Engineers, 2009. http://dx.doi.org/10.3997/2214-4609.201400067.

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Li, C., and X. Liu. "Seismic Reflectivity Inversion Using an Adaptive FISTA." In Second EAGE Conference on Seismic Inversion. European Association of Geoscientists & Engineers, 2022. http://dx.doi.org/10.3997/2214-4609.202229010.

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Portniaguine, Oleg, Yili Wang, and He Chen. "Building electromagnetic model using seismic reflectivity." In SEG Technical Program Expanded Abstracts 2006. Society of Exploration Geophysicists, 2006. http://dx.doi.org/10.1190/1.2370387.

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Jakobsen, A. F., and H. J. Hansen. "Direct Probabilistic Inversion for Facies Using Zoeppritz Reflectivity Model." In First EAGE Conference on Seismic Inversion. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202037034.

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Russell, B., J. Downton, and T. Colwell. "Sparse Layer Reflectivity with FISTA for Post-Stack Impedance Inversion." In First EAGE Conference on Seismic Inversion. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202037018.

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Kong, Dehui, and Zhenming Peng. "Seismic reflectivity inversion using spectral compressed sensing." In 2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2016. http://dx.doi.org/10.1109/compcomm.2016.7924859.

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Wang, R., and Y. Wang. "Seismic Reflectivity Inversion in a Multichannel Manner." In 75th EAGE Conference and Exhibition incorporating SPE EUROPEC 2013. EAGE Publications BV, 2013. http://dx.doi.org/10.3997/2214-4609.20130053.

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Reports on the topic "Seismic reflectivity"

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Schetselaar, E. M., G. Bellefleur, and P. Hunt. Integrated analyses of density, P-wave velocity, lithogeochemistry, and mineralogy to investigate effects of hydrothermal alteration and metamorphism on seismic reflectivity: a summary of results from the Lalor volcanogenic massive-sulfide deposit, Snow Lake, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/327999.

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We present herein a summary of integrated data analyses aimed at investigating the effects of hydrothermal alteration on seismic reflectivity in the footwall of the Lalor volcanogenic massive-sulfide (VMS) deposit, Manitoba. Multivariate analyses of seismic rock properties, lithofacies, and hydrothermal alteration indices show an increase in P-wave velocity for altered volcanic and volcaniclastic lithofacies with respect to their least-altered equivalents. Scanning electron microscopy-energy dispersive X-ray spectrometry analyses of drill-core samples suggest that this P-wave velocity increase

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