"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:00af288f-acaf-48ec-ac90-812f1c8c4988","http://resolver.tudelft.nl/uuid:00af288f-acaf-48ec-ac90-812f1c8c4988","Seismoelectric interface response: Experimental results and forward model","Schakel, M.D.; Smeulders, D.M.J.; Slob, E.C.; Heller, H.K.J.","","2011","Understanding the seismoelectric interface response is important for developing seismoelectric field methods for oil exploration and environmental/engineering geophysics. The existing seismoelectric theory has never been validated systematically by controlled experiments. We have designed and developed an experimental setup in which acoustic-to-electromagnetic wave conversions at interfaces are measured. An acoustic source emits a pressure wave that impinges upon a porous sample. The reflected electric-wave potential is recorded by a wire electrode. We have also developed a full-waveform electrokinetic theoretical model based on the Sommerfeld approach and have compared it with measurements at positions perpendicular and parallel to the fluid/porous-medium interface. We performed experiments at several salinities. For 10-3 and 10-2 M sodium chloride (NaCl) solutions, both waveforms and amplitudes agree. For 10-4 M NaCl, however, amplitude deviations occur. We found that a single amplitude field scaling factor describes these discrepancies. We also checked the repeatability of experiments. The amplitudes are constant for the duration of an experiment (1–4 hours) but decrease on longer time scales (~24 hours). However, the waveforms and spatial amplitude pattern of the electric wavefield are preserved over time. Our results validate electrokinetic theory for the seismic-to-electromagnetic-wave conversion at interfaces for subsurface exploration purposes.","acoustoelectric effects; geophysical prospecting; geophysical signal processing; seismic waves; seismology","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geoscience & Engineering","","","",""
"uuid:55fabcd8-0435-48ba-aba0-a0bad1e05033","http://resolver.tudelft.nl/uuid:55fabcd8-0435-48ba-aba0-a0bad1e05033","Controlled-source interferometric redatuming by crosscorrelation and multidimensional deconvolution in elastic media","Van der Neut, J.R.; Thorbecke, J.W.; Mehta, K.; Slob, E.C.; Wapenaar, C.P.A.","","2011","Various researchers have shown that accurate redatuming of controlled seismic sources to downhole receiver locations can be achieved without requiring a velocity model. By placing receivers in a horizontal or deviated well and turning them into virtual sources, accurate images can be obtained even below a complex near-subsurface. Examples include controlled-source interferometry and the virtual-source method, both based on crosscorrelated signals at two downhole receiver locations, stacked over source locations at the surface. Because the required redatuming operators are taken directly from the data, even multiple scattered waveforms can be focused at the virtual-source location, and accurate redatuming can be achieved. To reach such precision in a solid earth, representations for elastic wave propagation that require multicomponent sources and receivers must be implemented. Wavefield decomposition prior to crosscorrelation allows us to enforce virtual sources to radiate only downward or only upward. Virtual-source focusing and undesired multiples from the overburden can be diagnosed with the interferometric point-spread function (PSF), which can be obtained directly from the data if an array of subsurface receivers is deployed. The quality of retrieved responses can be improved by filtering with the inverse of the PSF, a methodology referred to as multidimensional deconvolution.","acoustic wave interferometry; correlation methods; deconvolution; filtering theory; geophysical signal processing; geophysical techniques; seismic waves; seismology","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geoscience & Engineering","","","",""
"uuid:1d6d4d70-a458-4e3b-954c-969cef7dc2e8","http://resolver.tudelft.nl/uuid:1d6d4d70-a458-4e3b-954c-969cef7dc2e8","Separation of blended data by iterative estimation and subtraction of blending interference noise","Mahdad, A.; Doulgeris, P.; Blacquiere, G.","","2011","Seismic acquisition is a trade-off between economy and quality. In conventional acquisition the time intervals between successive records are large enough to avoid interference in time. To obtain an efficient survey, the spatial source sampling is therefore often (too) large. However, in blending, or simultaneous acquisition, temporal overlap between shot records is allowed. This additional degree of freedom in survey design significantly improves the quality or the economics or both. Deblending is the procedure of recovering the data as if they were acquired in the conventional, unblended way. A simple least-squares procedure, however, does not remove the interference due to other sources, or blending noise. Fortunately, the character of this noise is different in different domains, e.g., it is coherent in the common source domain, but incoherent in the common receiver domain. This property is used to obtain a considerable improvement. We propose to estimate the blending noise and subtract it from the blended data. The estimate does not need to be perfect because our procedure is iterative. Starting with the least-squares deblended data, the estimate of the blending noise is obtained via the following steps: sort the data to a domain where the blending noise is incoherent; apply a noise suppression filter; apply a threshold to remove the remaining noise, ending up with (part of) the signal; compute an estimate of the blending noise from this signal. At each iteration, the threshold can be lowered and more of the signal is recovered. Promising results were obtained with a simple implementation of this method for both impulsive and vibratory sources. Undoubtedly, in the future algorithms will be developed for the direct processing of blended data. However, currently a high-quality deblending procedure is an important step allowing the application of contemporary processing flows","data acquisition; geophysical signal processing; iterative methods; least squares approximations; seismology; signal denoising","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geoscience & Engineering","","","",""
"uuid:dc1eb372-1686-416c-89df-29bd007da4df","http://resolver.tudelft.nl/uuid:dc1eb372-1686-416c-89df-29bd007da4df","Source depopulation potential and surface-wave tomography using a crosscorrelation method in a scattering medium","Gouedard, P.; Roux, P.; Campillo, M.; Verdel, A.R.; Yao, H.; Van der Hilst, R.D.","","2011","We use seismic prospecting data on a 40 × 40 regular grid of sources and receivers deployed on a 1 km × 1 km area to assess the feasibility and advantages of velocity analysis of the shallow subsurface by means of surface-wave tomography with Green's functions estimated from crosscorrelation. In a first application we measure Rayleigh-wave dispersion curves in a 1D equivalent medium. The assumption that the medium is laterally homogeneous allows using a simple projection scheme and averaging of crosscorrelation functions over the whole network. Because averaging suppresses noise, this method yields better signal-to-noise ratio than traditional active-source approaches, and the improvement can be estimated a priori from acquisition parameters. We find that high-quality dispersion curves can be obtained even when we reduce the number of active sources used as input for the correlations. Such source depopulation can achieve significant reduction in the cost of active source acquisition. In a second application we compare Rayleigh-wave group velocity tomography from raw and reconstructed data. We can demonstrate that the crosscorrelation approach yields group velocity maps that are similar to active source maps. Scattering has an importance here as it may enhance the crosscorrelation performance. We quantify the scattering properties of the medium using mean free path measurements from coherent and incoherent parts of the signal. We conclude that for first-order velocity analysis of the shallow subsurface, the use of crosscorrelation offers a cost-effective alternative to methods that rely exclusively on active sources.","correlation methods; geophysical prospecting; geophysical signal processing; Green's function methods; Rayleigh waves; seismology; tomography","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geoscience & Engineering","","","",""
"uuid:f8d8b93e-d90c-4d44-addf-1873edf600ff","http://resolver.tudelft.nl/uuid:f8d8b93e-d90c-4d44-addf-1873edf600ff","A perspective on 3D surface-related multiple elimination","Dragoset, B.; Verschuur, D.J.; Moore, I.; Bisley, R.","","2010","Surface-related multiple elimination (SRME) is an algorithm that predicts all surface multiples by a convolutional process applied to seismic field data. Only minimal preprocessing is required. Once predicted, the multiples are removed from the data by adaptive subtraction. Unlike other methods of multiple attenuation, SRME does not rely on assumptions or knowledge about the subsurface, nor does it use event properties to discriminate between multiples and primaries. In exchange for this “freedom from the subsurface,” SRME requires knowledge of the acquisition wavelet and a dense spatial distribution of sources and receivers. Although a 2D version of SRME sometimes suffices, most field data sets require 3D SRME for accurate multiple prediction. All implementations of 3D SRME face a serious challenge: The sparse spatial distribution of sources and receivers available in typical seismic field data sets does not conform to the algorithmic requirements. There are several approaches to implementing 3D SRME that address the data sparseness problem. Among those approaches are pre-SRME data interpolation, on-the-fly data interpolation, zero-azimuth SRME, and true-azimuth SRME. Field data examples confirm that (1) multiples predicted using true-azimuth 3D SRME are more accurate than those using zero-azimuth 3D SRME and (2) on-the-fly interpolation produces excellent results.","geophysical signal processing; seismology","en","journal article","Society of Exploration Geophysicists","","","","","","","","Applied Sciences","Imaging Science and Technology","","","",""
"uuid:84703eac-a050-4e85-bf32-68bbae218732","http://resolver.tudelft.nl/uuid:84703eac-a050-4e85-bf32-68bbae218732","Reflection images from ambient seismic noise","Draganov, D.S.; Campman, X.; Thorbecke, J.W.; Verdel, A.; Wapenaar, C.P.A.","","2009","One application of seismic interferometry is to retrieve the impulse response (Green's function) from crosscorrelation of ambient seismic noise. Various researchers show results for retrieving the surface-wave part of the Green's function. However, reflection retrieval has proven more challenging. We crosscorrelate ambient seismic noise, recorded along eight parallel lines in the Sirte basin east of Ajdabeya, Libya, to obtain shot gathers that contain reflections. We take advantage of geophone groups to suppress part of the undesired surface-wave noise and apply frequency-wavenumber filtering before crosscorrelation to suppress surface waves further. After comparing the retrieved results with data from an active seismic exploration survey along the same lines, we use the retrieved reflection data to obtain a migrated reflection image of the subsurface.","geophysical signal processing; interference suppression; seismic waves; seismology; signal denoising","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geotechnology","","","",""
"uuid:14eeb991-c4fc-4959-b8d9-e15371864dc6","http://resolver.tudelft.nl/uuid:14eeb991-c4fc-4959-b8d9-e15371864dc6","Stochastic joint inversion of 2D seismic and seismoelectric signals in linear poroelastic materials: A numerical investigation","Jardani, A.; Revil, A.; Slob, E.C.; Söllner, W.","","2009","The interpretation of seismoelectrical signals is a difficult task because coseismic and seismoelectric converted signals are recorded simultaneously and the seismoelectric conversions are typically several orders of magnitude smaller than the coseismic electrical signals. The seismic and seismoelectric signals are modeled using a finite-element code with perfectly matched layer boundary conditions assuming a linear poroelastic body. We present a stochastic joint inversion of the seismic and seismoelectrical data based on the adaptive Metropolis algorithm, to obtain the posterior probability density functions of the material properties of each geologic unit. This includes the permeability, porosity, electrical conductivity, bulk modulus of the dry porous frame, bulk modulus of the fluid, bulk modulus of the solid phase, and shear modulus of the formations. A test of this approach is performed with a synthetic model comprising two horizontal layers and a reservoir partially saturated with oil, which is embedded in the second layer. The result of the joint inversion shows that we can invert the permeability of the reservoir and its mechanical properties.","elastic moduli; finite element analysis; geophysical prospecting; geophysical signal processing; hydrocarbon reservoirs; permeability; porosity; seismology; terrestrial electricity","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geotechnology","","","",""
"uuid:4ce33f95-c280-47f2-b443-d39eb24f7eea","http://resolver.tudelft.nl/uuid:4ce33f95-c280-47f2-b443-d39eb24f7eea","The spatial data-adaptive minimum-variance distortionless-response beamformer on seismic single-sensor data","Panea, I.; Drijkoningen, G.G.","","2008","Coherent noise generated by surface waves or ground roll within a heterogeneous near surface is a major problem in land seismic data. Array forming based on single-sensor recordings might reduce such noise more robustly than conventional hardwired arrays. We use the minimum-variance distortionless-response (MVDR) beamformer to remove (aliased) surface-wave energy from single-sensor data. This beamformer is data adaptive and robust when the presumed and actual desired signals are mismatched. We compute the intertrace covariance for the desired signal, and then for the total signal (desired signal+noise) to obtain optimal weights. We use the raw data of only one array for the covariance of the total signal, and the wavenumber-filtered version of a full seismic single-sensor record for the covariance of the desired signal. In the determination of optimal weights, a parameter that controls the robustness of the beamformer against an arbitrary desired signal mismatch has to be chosen so that the results are optimal. This is similar to stabilization in deconvolution problems. This parameter needs to be smaller than the largest eigenvalue provided by the singular value decomposition of the presumed desired signal covariance. We compare results of MVDR beamforming with standard array forming on single-sensor synthetic and field seismic data. We apply 2D and 3D beamforming and show prestack and poststack results. MVDR beamformers are superior to conventional hardwired arrays for all examples.","array signal processing; covariance analysis; geophysical prospecting; geophysical signal processing; seismology; signal denoising; singularalue decomposition","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geotechnology","","","",""
"uuid:b2b33664-6da1-418f-9af4-5ab98fdd439d","http://resolver.tudelft.nl/uuid:b2b33664-6da1-418f-9af4-5ab98fdd439d","Acquisition geometry analysis in complex 3D media","Van Veldhuizen, E.J.; Blacquiere, G.; Berkhout, A.J.","","2008","Increasingly, we must deal with complex subsurface structures in seismic exploration, often resulting in poor illumination and, therefore, poor image quality. Consequently, it is desirable to take into consideration the effects of wave propagation in the subsurface structure when designing an acquisition geometry. We developed a new, model-based implementation of the previously introduced focal-beam analysis method. The method's objective is to provide quantitative insight into the combined influence of acquisition geometry, overburden structure, and migration operators on image resolution and angle-dependent amplitude accuracy. This is achieved by simulation of migrated grid-point responses using focal beams. Note that the seismic response of any subsurface can be composed of a linear sum of grid-point responses. The focal beams have been chosen because any migration process represents double focusing. In addition, the focal source beam and focal detector beam relate migration quality to illumination properties of the source geometry and sensing properties of the detector geometry, respectively. Wave-equation modeling ensures that frequency-dependent effects in the seismic-frequency range are incorporated. We tested our method by application to a 3D salt model in the Gulf of Mexico. Investigation of well-sampled, all-azimuth, long-offset acquisition geometries revealed fundamental illumination and sensing limitations. Further results exposed the shortcomings of narrow-azimuth data acquisition. The method also demonstrates how acquisition-related amplitude errors affect seismic inversion results.","data acquisition; geophysical prospecting; geophysical signal processing; seismology","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geotechnology","","","",""
"uuid:389fad21-46c3-4368-bf75-ea6f7b1f8586","http://resolver.tudelft.nl/uuid:389fad21-46c3-4368-bf75-ea6f7b1f8586","The dynamics of statics","Turhan Taner, M.; Berkhout, A.J.; Treitel, S.; Kelamis, P.G.","","2007","The statics problem, whether short wavelength, long wavelength, residual, or trim, has always been one of the more time-consuming and problematic steps in seismic data processing. We routinely struggle with issues such as poor signal-to-noise (S/N) ratio, cycle skipping, truncated refractors, wavelets with ambiguous first arrival times, etc. Elevation variations create their own problems and impact the choice of datum—floating, phantom or recourse to a zero-velocity layer. Even if we can overcome some of these problems, we still have a “catch 22” situation in which accurate velocity estimation requires good statics, while good statics estimation requires accurate velocities. To characterize these ambiguities, we have come up the oxymoron “time-varying statics.”","geophysical techniques; seismology; seismic waves; geophysical signal processing; statics","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geotechnology","","","",""
"uuid:a3b3a766-c921-4ac9-8262-76ce9b68cf6f","http://resolver.tudelft.nl/uuid:a3b3a766-c921-4ac9-8262-76ce9b68cf6f","Focal transformation, an imaging concept for signal restoration and noise removal","Berkhout, A.J.; Verschuur, D.J.","","2006","Interpolation of data beyond aliasing limits and removal of noise that occurs within the seismic bandwidth are still important problems in seismic processing. The focal transform is introduced as a promising tool in data interpolation and noise removal, allowing the incorporation of macroinformation about the involved wavefields. From a physical point of view, the principal action of the forward focal operator is removing the spatial phase of the signal content from the input data, and the inverse focal operator restores what the forward operator has removed. The strength of the method is that in the transformed domain, the focused signals at the focal area can be separated from the dispersed noise away from the focal area. Applications of particular interest in preprocessing are interpolation of missing offsets and reconstruction of signal beyond aliasing. The latter can be seen as the removal of aliasing noise.","geophysical signal processing; signal reconstruction; signal restoration; imaging; seismology; interference suppression","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geotechnology","","","",""
"uuid:85b14457-8770-463a-af0c-ff2243ac1b01","http://resolver.tudelft.nl/uuid:85b14457-8770-463a-af0c-ff2243ac1b01","Seismic processing in the inverse data space","Berkhout, A.J.","","2006","Until now, seismic processing has been carried out by applying inverse filters in the forward data space. Because the acquired data of a seismic survey is always discrete, seismic measurements in the forward data space can be arranged conveniently in a data matrix (P). Each column in the data matrix represents one shot record. If we represent seismic data in the temporal frequency domain, then each matrix element consists of a complex-valued number. Considering the dominant role of multiple scattering in seismic data, it is proposed to replace data matrix P by its inverse P–1 before starting seismic processing. Making use of the feedback model for seismic data, multiple scattered energy is mapped onto the zero time axis of the inverse data space. The practical consequence of this remarkable property may be significant: multiple elimination in the inverse data space simplifies to removing data at zero time only. Moving to the inverse data space may cause a fundamental change in the way we preprocess and image seismic data.","seismology; inverse problems; geophysical techniques; geophysical signal processing; matrix inversion","en","journal article","Society of Exploration Geophysicists","","","","","","","","Civil Engineering and Geosciences","Geotechnology","","","",""