scholarly journals Dip‐moveout processing for depth‐variable velocity

Geophysics ◽  
1994 ◽  
Vol 59 (4) ◽  
pp. 610-622 ◽  
Author(s):  
Craig Artley ◽  
Dave Hale
Keyword(s):  
Vertical Velocity ◽  
Seismic Data ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Variation ◽  
System Of Nonlinear Equations ◽  
Newton Raphson ◽  
Zero Offset ◽  
Improved Accuracy ◽  
Raphson Iteration

In dip‐moveout (DMO) processing, the seismic velocity is often assumed to be constant. While several approximate techniques for handling vertical velocity variation have recently become available, here we propose a method for computing the kinematically exact DMO correction when velocity is an arbitrary function of the depth. While not known in advance, the raypath from source to receiver and the corresponding zero‐offset raypath satisfy several relationships, which are used to form a system of nonlinear equations. By simultaneously solving the equations via Newton‐Raphson iteration, we determine the mapping that transforms nonzero‐offset data to zero‐offset. Unlike previous schemes that approximately handle vertical velocity variation, this method makes no assumptions about the offset, dip, velocity function, or hyperbolic moveout. Tests using both synthetic and recorded seismic data demonstrate the effectiveness of this variablevelocity DMO. These tests show this method accurately handles vertical velocity variation, while use of constant‐velocity DMO can lead to significant errors. Comparing this technique to a formulation that approximately handles velocity variation, however, suggests that the improved accuracy of the exact technique may not be justified because of uncertainty in the velocity model and increased cost. While improved accuracy alone may not justify the use of this method in 2-D, its flexibility may in other cases. Changes could be made to handle 3-D DMO, DMO for mode‐converted waves, DMO in anisotropic media, or prestack divergence correction.

Super-Resolution of Seismic Velocity Model Guided by Seismic Data

2021 ◽  
pp. 1-12
Author(s):  
Yinshuo Li ◽  
Jianyong Song ◽  
Wenkai Lu ◽  
Patrice Monkam ◽  
Yile Ao
Keyword(s):  
Seismic Data ◽  
Seismic Velocity ◽  
Super Resolution ◽  
Velocity Model

2-D continuation operators and their applications

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1846-1858 ◽  
Author(s):  
Claudio Bagaini ◽  
Umberto Spagnolini
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Integral Form ◽  
Amplitude Distribution ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Seismic Data Processing ◽  
Analysis Method ◽  
Standard Tool ◽  
Zero Offset

Continuation to zero offset [better known as dip moveout (DMO)] is a standard tool for seismic data processing. In this paper, the concept of DMO is extended by introducing a set of operators: the continuation operators. These operators, which are implemented in integral form with a defined amplitude distribution, perform the mapping between common shot or common offset gathers for a given velocity model. The application of the shot continuation operator for dip‐independent velocity analysis allows a direct implementation in the acquisition domain by exploiting the comparison between real data and data continued in the shot domain. Shot and offset continuation allow the restoration of missing shot or missing offset by using a velocity model provided by common shot velocity analysis or another dip‐independent velocity analysis method.

scholarly journals An Investigation of Seismic Velocity Variation through a Tectonic Boundaries-Case Study in Central Iraq

2021 ◽  
pp. 2614-2626
Author(s):  
Ahmed S. AL-Banna ◽  
Hassan E. Al-Assady
Keyword(s):  
High Velocity ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Seismic Inversion ◽  
Velocity Variation ◽  
Time Model ◽  
3D Velocity Model ◽  
Western Side ◽  
Well Data ◽  
Seismic Lines

      A 3D velocity model was created by using stacking velocity of 9 seismic lines and average velocity of 6 wells drilled in Iraq. The model was achieved by creating a time model to 25 surfaces with an interval time between each two successive surfaces of about 100 msec.  The summation time of all surfaces reached about 2400 msec, that was adopted according to West Kifl-1 well, which penetrated to a depth of 6000 m, representing the deepest well in the study area. The seismic lines and well data were converted to build a 3D cube time model and the velocity was spread on the model. The seismic inversion modeling of the elastic properties of the horizon and well data was applied to achieve a corrected velocity cube. Then, the velocity cube was converted to a time model and, finally, a corrected 3D depth model was obtained. This model shows that the western side of the study area, which is a part of the stable shelf, is characterized by relatively low thickness and high velocity layers. While the eastern side of the study area, which is a part of the Mesopotamian, is characterized by high thickness and low velocity of the Cretaceous succession. The Abu Jir fault is considered as a boundary between the stable and unstable shelves in Iraq, situated at the extreme west part of the study area. The area of relatively high velocity gradient is considered as the limit of the western side of the Mesopotamian basin. This area extends from Najaf-Karbala axis in the west to the Euphrates River in the east. It is found that the 3D stacking velocity model can be used to obtain good results concerning the tectonic boundary.  

scholarly journals The shallow structure of Mars at the InSight landing site from inversion of ambient vibrations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
M. Hobiger ◽  
M. Hallo ◽  
C. Schmelzbach ◽  
S. C. Stähler ◽  
D. Fäh ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Velocity ◽  
Landing Site ◽  
Velocity Model ◽  
Low Velocity ◽  
Shallow Subsurface ◽  
Seismic Vibrations ◽  
First Time ◽  
Velocity Channel ◽  
Velocity Zone

AbstractOrbital and surface observations can shed light on the internal structure of Mars. NASA’s InSight mission allows mapping the shallow subsurface of Elysium Planitia using seismic data. In this work, we apply a classical seismological technique of inverting Rayleigh wave ellipticity curves extracted from ambient seismic vibrations to resolve, for the first time on Mars, the shallow subsurface to around 200 m depth. While our seismic velocity model is largely consistent with the expected layered subsurface consisting of a thin regolith layer above stacks of lava flows, we find a seismic low-velocity zone at about 30 to 75 m depth that we interpret as a sedimentary layer sandwiched somewhere within the underlying Hesperian and Amazonian aged basalt layers. A prominent amplitude peak observed in the seismic data at 2.4 Hz is interpreted as an Airy phase related to surface wave energy trapped in this local low-velocity channel.

Diffraction imaging and depth-velocity inversion with 3D P-Cable seismic data

2021 ◽  
Author(s):  
Alexander Bauer ◽  
Benjamin Schwarz ◽  
Dirk Gajewski
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Computational Cost ◽  
Velocity Model ◽  
Data Cube ◽  
Velocity Inversion ◽  
Velocity Models ◽  
North East ◽  
3D Velocity Model ◽  
Zero Offset

<p>Most established methods for the estimation of subsurface velocity models rely on the measurements of reflected or diving waves and therefore require data with sufficiently large source-receiver offsets. For seismic data that lacks these offsets, such as vintage data, low-fold academic data or near zero-offset P-Cable data, these methods fail. Building on recent studies, we apply a workflow that exploits the diffracted wavefield for depth-velocity-model building. This workflow consists of three principal steps: (1) revealing the diffracted wavefield by modeling and adaptively subtracting reflections from the raw data, (2) characterizing the diffractions with physically meaningful wavefront attributes, (3) estimating depth-velocity models with wavefront tomography. We propose a hybrid 2D/3D approach, in which we apply the well-established and automated 2D workflow to numerous inlines of a high-resolution 3D P-Cable dataset acquired near Ritter Island, a small volcanic island located north-east of New Guinea known for a catastrophic flank collapse in 1888. We use the obtained set of parallel 2D velocity models to interpolate a 3D velocity model for the whole data cube, thus overcoming possible issues such as varying data quality in inline and crossline direction and the high computational cost of 3D data analysis. Even though the 2D workflow may suffer from out-of-plane effects, we obtain a smooth 3D velocity model that is consistent with the data.</p>

Seismic Velocity Model Building Using Neural Networks: Training Data Design and Learning Generalization

Geophysics ◽  
2021 ◽  
pp. 1-73
Author(s):  
Hani Alzahrani ◽  
Jeffrey Shragge
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Training Data ◽  
Training Process ◽  
Training Models ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Layer Velocity

Data-driven artificial neural networks (ANNs) offer a number of advantages over conventional deterministic methods in a wide range of geophysical problems. For seismic velocity model building, judiciously trained ANNs offer the possibility of estimating high-resolution subsurface velocity models. However, a significant challenge of ANNs is training generalization, which is the ability of an ANN to apply the learning from the training process to test data not previously encountered. In the context of velocity model building, this means learning the relationship between velocity models and the corresponding seismic data from a set of training data, and then using acquired seismic data to accurately estimate unknown velocity models. We ask the following question: what type of velocity model structures need be included in the training process so that the trained ANN can invert seismic data from a different (hypothetical) geological setting? To address this question, we create four sets of training models: geologically inspired and purely geometrical, both with and without background velocity gradients. We find that using geologically inspired training data produce models with well-delineated layer interfaces and fewer intra-layer velocity variations. The absence of a certain geological structure in training models, though, hinders the ANN's ability to recover it in the testing data. We use purely geometric training models consisting of square blocks of varying size to demonstrate the ability of ANNs to recover reasonable approximations of flat, dipping, and curved interfaces. However, the predicted models suffer from intra-layer velocity variations and non-physical artifacts. Overall, the results successfully demonstrate the use of ANNs in recovering accurate velocity model estimates, and highlight the possibility of using such an approach for the generalized seismic velocity inversion problem.

Unified imaging of multichannel seismic data: Combining traveltime inversion and prestack depth migration

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE269-VE280 ◽  
Author(s):  
Priyank Jaiswal ◽  
Colin A. Zelt
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Velocity Model ◽  
Depth Image ◽  
Prestack Depth Migration ◽  
Traveltime Inversion ◽  
Depth Migration ◽  
Interval Velocity ◽  
Wide Aperture ◽  
Zero Offset

Imaging 2D multichannel land seismic data can be accomplished effectively by a combination of traveltime inversion and prestack depth migration (PSDM), referred to as unified imaging. Unified imaging begins by inverting the direct-arrival times to estimate a velocity model that is used in static corrections and stacking velocity analysis. The interval velocity model (from stacking velocities) is used for PSDM. The stacked data and the PSDM image are interpreted for common horizons, and the corresponding wide-aperture reflections are identified in the shot gathers. Using the interval velocity model, the stack interpretations are inverted as zero-offset reflections to constrain the corresponding interfaces in depth; the interval velocity model remains stationary. We define a coefficient of congruence [Formula: see text] that measures the discrepancy between horizons from the PSDM image andtheir counterparts from the zero-offset inversion. A value of unity for [Formula: see text] implies that the interpreted and inverted horizons are consistent to within the interpretational uncertainties, and the unified imaging is said to have converged. For [Formula: see text] greater than unity, the interval velocity model and the horizon depths are updated by jointly inverting the direct arrivals with the zero-offset and wide-aperture reflections. The updated interval velocity model is used again for both PSDM and a zero-offset inversion. Interpretations of the new PSDM image are the updated horizon depths. The unified imaging is applied to seismic data from the Naga Thrust and Fold Belt in India. Wide-aperture and zero-offset data from three geologically significant horizons are used. Three runs of joint inversion and PSDM are required in a cyclic manner for [Formula: see text] to converge to unity. A joint interpretation of the final velocity model and depth image reveals the presence of a triangle zone that could be promising for exploration.

scholarly journals Deep-learning inversion: A next-generation seismic velocity model building method

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. R583-R599 ◽  
Author(s):  
Fangshu Yang ◽  
Jianwei Ma
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Computational Time ◽  
Learning Methods ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Training Stage

Seismic velocity is one of the most important parameters used in seismic exploration. Accurate velocity models are the key prerequisites for reverse time migration and other high-resolution seismic imaging techniques. Such velocity information has traditionally been derived by tomography or full-waveform inversion (FWI), which are time consuming and computationally expensive, and they rely heavily on human interaction and quality control. We have investigated a novel method based on the supervised deep fully convolutional neural network for velocity-model building directly from raw seismograms. Unlike the conventional inversion method based on physical models, supervised deep-learning methods are based on big-data training rather than prior-knowledge assumptions. During the training stage, the network establishes a nonlinear projection from the multishot seismic data to the corresponding velocity models. During the prediction stage, the trained network can be used to estimate the velocity models from the new input seismic data. One key characteristic of the deep-learning method is that it can automatically extract multilayer useful features without the need for human-curated activities and an initial velocity setup. The data-driven method usually requires more time during the training stage, and actual predictions take less time, with only seconds needed. Therefore, the computational time of geophysical inversions, including real-time inversions, can be dramatically reduced once a good generalized network is built. By using numerical experiments on synthetic models, the promising performance of our proposed method is shown in comparison with conventional FWI even when the input data are in more realistic scenarios. We have also evaluated deep-learning methods, the training data set, the lack of low frequencies, and the advantages and disadvantages of our method.

Application of diffraction wavefront tomography to GPR data from a glacier

2020 ◽  
Author(s):  
Alexander Bauer ◽  
Benjamin Schwarz ◽  
Richard Delf ◽  
Dirk Gajewski
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
High Amplitude ◽  
Small Scale ◽  
Coherence Analysis ◽  
Raw Data ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Passive Seismic ◽  
Zero Offset

<p>In the recent years, the diffracted wavefield has gained increasing attention in the field of applied seismics. While classical seismic imaging and inversion schemes mainly focus on high-amplitude reflected measurements, the faint and often masked diffracted wavefield is neglected or even treated as noise. In order to be able to extract depth-velocity models from seismic reflection data, sufficiently large source-receiver offsets are needed. However, the acquisition of such multi-channel seismic data is expensive and often only feasible for the hydrocarbon industry, while academia has to cope with low-fold or zero-offset data. The diffracted wavefield is the key for extracting depth velocities from such data, as the moveout of diffractions – in contrast to reflections – can be measured in the zero-offset domain. Recently, we have demonstrated on multi-channel, single-channel and passive seismic data that by means of wavefront tomography depth-velocity models can be retrieved solely based on diffractions or passive seismic events along with the localizations of these scatterers. The input for wavefront tomography are so-called wavefront attributes, which can be extracted from the data in an unsupervised fashion by means of coherence analysis. In order to obtain the required diffraction-only data, we use a recently proposed scheme that adaptively subtracts the high-amplitude reflected wavefield from the raw data. Due to their most common acquisition geometry, most ground-penetrating-radar (GPR) data inherently lack offsets. In addition, GPR data generally contain a rich diffracted wavefield, which in turn contains information about sought-after structures, as diffractions are caused by small-scale heterogeneities such as faults, tips or edges. In this work, we show an application of the suggested workflow – coherence analysis, diffraction separation and diffraction wavefront tomography – to GPR data acquired at a glacier, resulting in a depth-velocity model and the localizations of the scatterers, both obtained in a fully unsupervised fashion. While the resulting  velocity model may be used for depth migration of the raw data, the localizations of the scatterers may in addition provide important information on the inner structure of the glacier in order to, for instance, localize water intrusions or fractures.</p>

A constrained parametric inversion for velocity analysis based on CFP technology

Geophysics ◽  
2000 ◽  
Vol 65 (4) ◽  
pp. 1210-1222 ◽  
Author(s):  
M. M. Nurul Kabir ◽  
D. J. Verschuur
Keyword(s):  
Vertical Velocity ◽  
Velocity Gradient ◽  
Fluid Pressure ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Velocity Variation ◽  
Velocity Analysis ◽  
Parametric Inversion ◽  
Layer Velocity ◽  
Vertical Velocity Gradient

A method of velocity analysis based on the common focusing point (CFP) method is presented. The two important aspects of the method are the use of the CFP domain and the use of a new parameterization—a vertical velocity gradient to describe the lateral velocity variation within a layer. The layer velocity is defined with only two parameters: an average velocity [Formula: see text]and a vertical velocity gradient (β). Layer velocity parameterization using [Formula: see text] and β assumes that the lithology of the layer is constant and that the overburden and fluid pressure increase linearly with depth. This type of parameterization is suitable for areas with gross changes in lithology (clastic‐carbonate‐salt) and for rock in hydrostatic equilibrium. A layer‐based model is required for these areas. The salt dome data example presented belongs to this type of area, so the layer‐based model with the defined parameterization produced a very good subsurface velocity model. The method is based on the principle of equal traveltime between the focusing operator and the corresponding focus point response. The velocity estimation problem is formulated as a constrained parametric inversion process. The method of perturbation is applied where linear assumptions are made; the velocity inversion, however, is a nonlinear problem, and the model parameter updates are computed iteratively using Newton’s method. The velocity model is built by layers in a top‐down approach, which makes the problem quasi‐linear.

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