Velocity estimation by beam stack

Geophysics ◽  
1992 ◽  
Vol 57 (8) ◽  
pp. 1034-1047 ◽  
Author(s):  
Biondo Biondi
Keyword(s):  
Estimation Method ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Gradient Algorithm ◽  
Stratified Medium ◽  
Detailed Knowledge ◽  
Low Velocity ◽  
Velocity Anomaly ◽  
Interval Velocity ◽  
Lateral Variations

Imaging seismic data requires detailed knowledge of the propagation velocity of compressional waves in the subsurface. In conventional seismic processing, the interval velocity model is usually derived from stacking velocities. Stacking velocities are determined by measuring the coherency of the reflections along hyperbolic moveout trajectories in offset. This conventional method becomes inaccurate in geologically complex areas because the conversion of stacking velocities to interval velocities assumes a horizontally stratified medium and mild lateral variations in velocity. The tomographic velocity estimation proposed in this paper can be applied when there are dipping reflectors and strong lateral variations. The method is based on the measurements of moveouts by beam stacks. A beam stack measures local coherency of reflections along hyperbolic trajectories. Because it is a local operator, the beam stack can provide information on nonhyperbolic moveouts in the data. This information is more reliable than traveltimes of reflections picked directly from the data because many seismic traces are used for computing beam stacks. To estimate interval velocity, I iteratively search for the velocity model that best predicts the events in beam‐stacked data. My estimation method does not require a preliminary picking of the data because it directly maximizes the beam‐stack’s energy at the traveltimes and surface locations predicted by ray tracing. The advantage of this formulation is that detection of the events in the beam‐stacked data can be guided by the imposition of smoothness constraints on the velocity model. The optimization problem of maximizing beam‐stack energy is solved by a gradient algorithm. To compute the derivatives of the objective function with respect to the velocity model, I derive a linear operator that relates perturbations in velocity to the observed changes in the beam‐stack kinematics. The method has been successfully applied to a marine survey for estimating a low‐velocity anomaly. The estimated velocity function correctly predicts the nonhyperbolic moveouts in the data caused by the velocity anomaly.

scholarly journals Fast time-to-depth conversion and interval velocity estimation in the case of weak lateral variations

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. S227-S235 ◽  
Author(s):  
Yanadet Sripanich ◽  
Sergey Fomel
Keyword(s):  
Velocity Model ◽  
Velocity Estimation ◽  
Fast Time ◽  
Lateral Velocity ◽  
First Order ◽  
Interval Velocity ◽  
Order Of Magnitude ◽  
Efficient Alternative ◽  
Homogeneous Media ◽  
Lateral Variations

Time-domain processing has a long history in seismic imaging and has always been a powerful workhorse that is routinely used. It generally leads to an expeditious construction of the subsurface velocity model in time, which can later be expressed in the Cartesian depth coordinates via a subsequent time-to-depth conversion. The conventional practice of such a conversion is done using Dix inversion, which is exact in the case of laterally homogeneous media. For other media with lateral heterogeneity, the time-to-depth conversion involves solving a more complex system of partial differential equations (PDEs). We have developed an efficient alternative for time-to-depth conversion and interval velocity estimation based on the assumption of weak lateral velocity variations. By considering only first-order perturbative effects from lateral variations, the exact system of PDEs required to accomplish the exact conversion reduces to a simpler system that can be solved efficiently in a layer-stripping (downward-stepping) fashion. Numerical synthetic and field data examples show that our method can achieve reasonable accuracy and is significantly more efficient than previously proposed methods with a speedup by an order of magnitude.

Time-to-depth conversion and velocity estimation by image-wavefront propagation

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. U75-U85 ◽  
Author(s):  
Leandro da S. Sadala Valente ◽  
Henrique B. Santos ◽  
Jessé C. Costa ◽  
Jörg Schleicher
Keyword(s):  
Ray Tracing ◽  
Model Building ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Time Step ◽  
Velocity Anomaly ◽  
Interval Velocity ◽  
Velocity Models ◽  
Wavefront Propagation ◽  
Velocity Model Building

A new strategy for time-to-depth conversion and interval-velocity estimation is based entirely on image-wavefront propagation without the need to follow individual image rays. The procedure has three main features: (1) It computes the velocity field and the traveltime directly, allowing us to dispense with dynamic ray tracing; (2) it requires only the knowledge of the image wavefront at the previous time step; and (3) it inherently smooths the image wavefront, inhibiting the formation of caustics. As a consequence, the method tends to be faster than the usual techniques and does not carry the constraints and limitations inherent to common ray-tracing strategies. Synthetic tests using a Gaussian velocity anomaly as well as the Marmousi velocity model, and two smoothed versions of it show the feasibility of the method. A field-data example demonstrates the use of different numerical procedures. Our results indicate that the present strategy can be used to construct reasonable depth-velocity models that can be used as reliable starting models for velocity-model building in depth migration or for tomographic methods.

scholarly journals Seismic velocity estimation: A deep recurrent neural-network approach

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Interval Velocity ◽  
Velocity Model Building ◽  
3D Velocity Model

Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than [Formula: see text] for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building.

Practical aspects and applications of 2D stereotomography

Geophysics ◽  
2003 ◽  
Vol 68 (3) ◽  
pp. 1008-1021 ◽  
Author(s):  
Frederic Billette ◽  
Soazig Le Bégat ◽  
Pascal Podvin ◽  
Gilles Lambaré
Keyword(s):  
Estimation Method ◽  
Synthetic Data ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Common Source ◽  
Depth Migration ◽  
Reflection Seismic ◽  
Velocity Models ◽  
Automatic Picking ◽  
Iterative Inversion

Stereotomography is a new velocity estimation method. This tomographic approach aims at retrieving subsurface velocities from prestack seismic data. In addition to traveltimes, the slope of locally coherent events are picked simultaneously in common offset, common source, common receiver, and common midpoint gathers. As the picking is realized on locally coherent events, they do not need to be interpreted in terms of reflection on given interfaces, but may represent diffractions or reflections from anywhere in the image. In the high‐frequency approximation, each one of these events corresponds to a ray trajectory in the subsurface. Stereotomography consists of picking and analyzing these events to update both the associated ray paths and velocity model. In this paper, we describe the implementation of two critical features needed to put stereotomography into practice: an automatic picking tool and a robust multiscale iterative inversion technique. Applications to 2D reflection seismic are presented on synthetic data and on a 2D line extracted from a 3D towed streamer survey shot in West Africa for TotalFinaElf. The examples demonstrate that the method requires only minor human intervention and rapidly converges to a geologically plausible velocity model in these two very different and complex velocity regimes. The quality of the velocity models is verified by prestack depth migration results.

scholarly journals The Puffin Field ? a geophysical enigma

1989 ◽  
Vol 20 (2) ◽  
pp. 301
Author(s):  
P.D. Grant
Keyword(s):  
Joint Venture ◽  
Source Area ◽  
Velocity Model ◽  
Minimum Size ◽  
Velocity Variation ◽  
Velocity Analysis ◽  
Low Velocity ◽  
Interval Velocity ◽  
Oil Water

The Puffin Field is located within the Vulcan Sub-basin of the Timor sea, off the Northwest Coast of Australia. It lies within the offshore exploration permit AC/P2, operated by BHP Petroleum and its co-venturers. It is situated on the Ashmore Platform, an old Triassic horst which is normal faulted against the Swan Graben, a major Mesozoic depocentre and the regional source area. Three wells were drilled in the 1970's. Puffin-1 and Puffin-3 encountered oil in "FIT" tests from within the Maastrichtian 100 ft sand, and Puffin-2 flowed over 4000 barrels of oil per day from a slightly younger 4 m sand. On examination of the results of the Puffin wells, it was evident that there were severe velocity anomalies and differing oil water contacts in the Puffin field. The top of the 100 ft reservoir sand is at 2031.4 m subsea in Puffin-1, 2045 m subsea at Puffin-2 and 2074 m subsea at Puffin-3. The two way times to these events were 1392 ms, 1328 ms and 1398 ms respectively. The interpreted oil water contacts in Puffin-1 and Puffin-3 were 2033 and 2077 ms subsea respectively with no contact seen at Puffin-2. In an attempt to resolve these anomalies the AC/P2 joint venture undertook a detailed seismic reprocessing project of the 1980 data with special emphasis on detailed velocity analysis. This 1987 reprocessing effort involved two passes of velocity filtering and velocity analysis at every 600 m. Velocity analyses were picked on a horizon-consistent basis, such that variations in interval velocity for key horizons could be established for later use in depth conversion. Although sceptical in using stacking functions as the input velocities to depth conversion, they were used, as no viable alternative was feasible. Data quality was reliable to the top of the Palaeocene Calcilutite, and six horizons were picked with their respective velocities to this level. Analysis of the data indicated that the two major units exhibiting interval velocity variation were the Pliocene "low velocity layer" and the Eocene carbonates. Using the smoothed stacking velocity down to the Top Palaeocene Calcilutite the three wells tied the depth conversion with an accuracy of 0.5%. Below this horizon two constant interval velocities were used from well data as the quality of the seismic pick were not as reliable. To verify this model BHPP also undertook a "layer-cake" velocity approach which, although confirming the anomalous zones, could not be used laterally away from the three wells, which unfortunately all lay in a straight line. Two wells, Puffin-4 and Parry-1 were drilled in 1988 to test the resultant interpretation. The wells intersected the Top Palaeocene Calcilutite within 1% of prognosis at Puffin-4 and within 2.2% of prognosis at Parry-1, therefore confirming the stacking velocity model used in depth conversion. However, both wells came in deep to prognosis at the deeper, objective level as a result, in the case of Puffin-4, of being on the downthrown side of a small fault, and at Parry-1 due to a thickening of the Paleocene section and seismic mispicking of the Top Palaeocene Calcilutite. Had the mispick at Parry-1 been avoided then the tie would have been less than 1.0%. Both these mis-interpretations were made in the part of the section where the quality of seismic was poorest. These two results suggest that even though the depth conversion to the Top Paleocene Calcilutite is accurate to within 1%, the magnitude of the velocity variation is larger than the magnitude of the independent depth closure. The Puffin Field requires both better quality seismic below the Base Palaeocene Calcilutite, or the means to resolve the lateral extent and possible thickness of a 4 m sand away from Puffin-2. Until such a method of obtaining either better quality seismic to the objective level, or to be able to define the seismic resolution of the differing sand bodies of a minimum size of 4 m, the Puffin Field will remain a Geophysical enigma.

Interval velocity estimation via NMO-based differential semblance

Geophysics ◽  
2007 ◽  
Vol 72 (6) ◽  
pp. U75-U88 ◽  
Author(s):  
Jintan Li ◽  
William W. Symes
Keyword(s):  
Velocity Model ◽  
Velocity Estimation ◽  
Imaging Method ◽  
Velocity Analysis ◽  
Multiple Reflections ◽  
Coherent Noise ◽  
Interval Velocity ◽  
Velocity Models ◽  
Mean Square Difference ◽  
Rich Data

The differential semblance method of velocity analysis flattens image gathers automatically by updating interval velocity to minimize the mean square difference of neighboring traces. We detail an implementation using hyperbolic normal moveout correction as the imaging method. The algorithm is fully automatic, accommodates arbitrary acquisition geometry, and outputs 1D, 2D, or 3D interval velocity models. This variant of differential semblance velocity analysis is effective within the limits of its imaging methodology: mild lateral heterogeneity and data dominated by primary events. Coherent noise events such as multiple reflections tend to degrade the quality of the velocity model estimated by differential semblance. We show how to combine differential semblance velocity analysis with dip filtering to suppress multiple reflections and thus improve considerably the accuracy of the velocity estimate. We illustrate this possibility using multiple-rich data from a 2D marine survey.

scholarly journals Ambient seismic noise tomography to build up a 3D shear-wave velocity model

2021 ◽  
Vol 22 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Martín Cárdenas Soto ◽  
José Piña Flores ◽  
David Escobedo Zenil ◽  
Jesús Sánchez González ◽  
José Antonio Martínez González
Keyword(s):  
Rayleigh Waves ◽  
Seismic Noise ◽  
Velocity Model ◽  
Ambient Seismic Noise ◽  
Lava Tube ◽  
Low Velocity ◽  
Rectangular Array ◽  
Velocity Anomaly ◽  
Adjacent Building ◽  
Tomography Method

To explore the usefulness of the ambient seismic noise tomography method for characterizing the subsoil surface structure, in this study, we apply this method to contribute to geotechnical decision-making in the construction of a school building. We used a rectangular array (36x56 m) of 48-4.5 Hz vertical geophones and produce surface wave tomographies from the travel times of Rayleigh waves extracted by cross-correlation of seismic noise. We determined a final 3D Vs model using 1D models derived from the inversion of dispersion curves obtained from the tomography maps for different frequencies. The 3D model shows an excellent resolution (vertical and lateral); we observe critical velocity contrasts in the range of 2 to 15 m deep. At depths higher than 15 m, the velocity has values close to 900 m/s; however, we observe a low-velocity anomaly associated with a lava tube or crack that seems to continue under an adjacent building.

Vertical seismic profiling depth migration of a salt dome flank

Geophysics ◽  
1986 ◽  
Vol 51 (5) ◽  
pp. 1087-1109 ◽  
Author(s):  
N. D. Whitmore ◽  
Larry R. Lines
Keyword(s):  
Data Processing ◽  
Survey Design ◽  
Velocity Model ◽  
Salt Dome ◽  
Velocity Estimation ◽  
Seismic Profiles ◽  
Depth Migration ◽  
Vertical Seismic Profiles ◽  
Interval Velocity ◽  
Vsp Data

Vertical seismic profiles (VSPs) can supply information about both velocity and subsurface interface locations. Properly designed VSPs can be used to map steeply dipping interfaces such as salt dome flanks. Mapping subsurface interfaces with VSP data requires careful survey design, appropriate data processing, interval velocity estimation, and reflector mapping. The first of these four ingredients is satisfied, in most cases, by preacquisition modeling. The second is accomplished by careful data processing. Initial velocity estimates are provided by seismic tomography. Velocity‐model refinement is accomplished by a combination of iterative modeling and iterative least‐squares inversion. Finally, the resultant interval velocities are used in depth migration of the processed VSP. These four ingredients have been combined to map a salt dome flank.

scholarly journals Seismic Imaging and Seismicity Analysis in Beijing-Tianjin-Tangshan Region

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Xiangwei Yu ◽  
Wenbo Zhang ◽  
Yun-tai Chen
Keyword(s):  
Focal Depth ◽  
Velocity Model ◽  
Double Difference ◽  
Low Velocity ◽  
Arrival Times ◽  
Velocity Anomaly ◽  
The North ◽  
Tomographic Method ◽  
Vertical Slice ◽  
Tomography Method

In this study a new tomographic method is applied to over 43,400 high-quality absolute direct P arrival times and 200,660 relative P arrival times to determine detailed 3D crustal velocity structures as well as the absolute and relative hypocenter parameters of 2809 seismic events under the Beijing-Tianjin-Tangshan region. The inferred velocity model of the upper crust correlates well with the surface geological and topographic features in the BTT region. In the North China Basin, the depression and uplift areas are imaged as slow and fast velocities, respectively. After relocation, the double-difference tomography method provides a sharp picture of the seismicity in the BTT region, which is concentrated along with the major faults. A broad low-velocity anomaly exists in Tangshan and surrounding area from 20 km down to 30 km depth. Our results suggest that the top boundary of low-velocity anomalies is at about 25.4 km depth. The event relocations inverted from double-difference tomography are clusted tightly along the Tangshan-Dacheng Fault and form three clusters on the vertical slice. The maximum focal depth after relocation is about 25 km depth in the Tangshan area.

Sign in / Sign up

Export Citation Format

Share Document