Velocity‐depth ambiguity of reflection traveltimes

Geophysics ◽  
1990 ◽  
Vol 55 (3) ◽  
pp. 266-276 ◽  
Author(s):  
Samuel H. Bickel

The conversion of time horizons to depth is fundamental to exploration geophysics. The interval velocity used in the conversion is often estimated from the stacking velocity, assuming that each layer’s interval velocity is homogeneous. However, even for one laterally inhomogeneous layer above a flat reflector the stacking velocity can swing violently about its average and conventional methods of velocity estimation fail. I show that violent swings in the stacking velocity are a symptom of a long‐wavelength ambiguity between the burial depth to an interface and interval velocity. Lateral variations in seismic velocity with a spatial wavelength of about 2.7 D, where D is the depth to the reflecting horizon, cannot be unambiguously resolved from traveltime measurements. The spatial wavelength of this ambiguous component varies from 2.57 D, for very small source‐receiver separations, to 2.86 D for source‐receiver separations equal to D. Spectral components of the stacking velocity at wavelengths shorter than this ambiguous value are amplified in size and reversed in polarity relative to the interval velocity. A practical inverse filter that corrects for these distortions produces an interval velocity that is almost totally lacking in low‐frequency components, giving a very distorted picture of the interval velocity. Since the wavelength of total ambiguity changes with offset, a complete description of the velocity and depth fields can, in theory, be extracted from a combination of multiple‐offset traveltime measurements. However, the wavelength of total ambiguity is such a weak function of source‐receiver separation that multiple offset processing, in practice, does little to resolve the ambiguity. In fact, the Rayleigh resolution limit implies that three or more offset measurements are more effective than two only if the seismic‐line length is at least 20 D. In a series of numerical experiments with the line set to 100 D and a spatial noise level of .01% in each channel I used a two‐channel Wiener filter to successfully extract the full‐band response for a simultaneous step change in velocity and in depth. The method fails for lines shorter than 20 D because of the transients that arise when the data are shorter than the filter. Stability was achieved by increasing the noise level to 1% in the design of the Wiener filter, but low spatial frequencies were lost and the estimated velocity‐depth model was distorted. If the results of this single flat‐layer analysis apply to practical situations, the velocity‐depth ambiguity may continue to plague exploration seismologists for some time to come.

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar

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.


Geophysics ◽  
1992 ◽  
Vol 57 (8) ◽  
pp. 1034-1047 ◽  
Author(s):  
Biondo Biondi

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.


2020 ◽  
Author(s):  
Zack Spica ◽  
Takeshi Akuhara ◽  
Gregory Beroza ◽  
Biondo Biondi ◽  
William Ellsworth ◽  
...  

<p>Our understanding of subsurface processes suffers from a profound observation bias: ground-motion sensors are rare, sparse, clustered on continents and not available where they are most needed. A new seismic recording technology called distributed acoustic sensing (DAS), can transform existing telecommunication fiber-optic cables into arrays of thousands of sensors, enabling meter-scale recording over tens of kilometers of linear fiber length. DAS works in high-pressure and high-temperature environments, enabling long-term recordings of seismic signals inside reservoirs, fault zones, near active volcanoes, in deep seas or in highly urbanized areas.</p><p>In this talk, we will introduce this laser-based technology and present three recent cases of study. The first experiment is in the city of Stanford, California, where DAS measurements are used to provide geotechnical information at a scale normally unattainable (i.e., for each building) with traditional geophone instrumentation. In the second study, we will show how downhole DAS passive recordings from the San Andreas Fault Observatory at Depth can be used for seismic velocity estimation. In the third research, we use DAS (in collaboration with Fujitec) to understand the ocean physics and infer seismic properties of the seafloor under a 100 km telecommunication cable.</p>


Geophysics ◽  
1994 ◽  
Vol 59 (2) ◽  
pp. 297-308 ◽  
Author(s):  
Pierre D. Thore ◽  
Eric de Bazelaire ◽  
Marisha P. Rays

We compare the three‐term equation to the normal moveout (NMO) equation for several synthetic data sets to analyze whether or not it is worth making the additional computational effort in the stacking process within various exploration contexts. In our evaluation we have selected two criteria: 1)The quality of the stacked image. 2) The reliability of the stacking parameters and their usefulness for further computation such as interval velocity estimation. We have simulated the stacking process very precisely, despite using only the traveltimes and not the full waveform data. The procedure searches for maximum coherency along the traveltime curve rather than a least‐square regression to it. This technique, which we call the Gaussian‐weighted least square, avoids most of the shortcomings of the least‐square method. The following are our conclusions: 1) The three term equation gives a better stack than the regular NMO. The increase in stacking energy can be more than 30 percent. 2)The calculation of interval velocities using a DIX formula rewritten for the three‐parameter equation is much more stable and accurate than the standard DIX formula. 3) The search for the three parameters is feasible in an efficient way since the shifted hyperbola requires only static corrections rather than dy namic ones. 4) Noise alters the parameters of the maximum energy stack in a way that depends on the noise type. The estimates obtained remain accurate enough for interval velocity estimation (where only two parameters are needed), but the use of the three parameters in direct inversion may be hazardous because of noise corruption. These conclusions should, however, be verified on real data examples.


Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. U19-U27 ◽  
Author(s):  
Paul C. Sava ◽  
Biondo Biondi ◽  
John Etgen

We propose a method for estimating interval velocity using the kinematic information in defocused diffractions and reflections. We extract velocity information from defocused migrated events by analyzing their residual focusing in physical space (depth and midpoint) using prestack residual migration. The results of this residual-focusing analysis are fed to a linearized inversion procedure that produces interval velocity updates. Our inversion procedure uses a wavefield-continuation operator linking perturbations of interval velocities to perturbations of migrated images, based on the principles of wave-equation migration velocity analysis introduced in recent years. We measure the accuracy of the migration velocity using a diffraction-focusing criterion instead of the criterion of flatness of migrated common-image gathers that is commonly used in migration velocity analysis. This new criterion enables us to extract velocity information from events that would be challenging to use with conventional velocity analysis methods; thus, our method is a powerful complement to those conventional techniques. We demonstrate the effectiveness of the proposed methodology using two examples. In the first example, we estimate interval velocity above a rugose salt top interface by using only the information contained in defocused diffracted and reflected events present in zero-offset data. By comparing the results of full prestack depth migration before and after the velocity updating, we confirm that our analysis of the diffracted events improves the velocity model. In the second example, we estimate the migration velocity function for a 2D, zero-offset, ground-penetrating radar data set. Depth migration after the velocity estimation improves the continuity of reflectors while focusing the diffracted energy.


Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. R29-R36 ◽  
Author(s):  
Sergey Fomel

Regularization is a required component of geophysical-estimation problems that operate with insufficient data. The goal of regularization is to impose additional constraints on the estimated model. I introduce shaping regularization, a general method for imposing constraints by explicit mapping of the estimated model to the space of admissible models. Shaping regularization is integrated in a conjugate-gradient algorithm for iterative least-squares estimation. It provides the advantage of better control on the estimated model in comparison with traditional regularization methods and, in some cases, leads to a faster iterative convergence. Simple data interpolation and seismic-velocity estimation examples illustrate the concept.


Geophysics ◽  
2010 ◽  
Vol 75 (2) ◽  
pp. C1-C6 ◽  
Author(s):  
Maheswar Ojha ◽  
Kalachand Sain ◽  
Timothy A. Minshull

We estimate the saturations of gas hydrate and free gas based on measurements of seismic-reflection amplitude variation with offset (AVO) for a bottom-simulating reflector coupled with rock-physics modeling. When we apply the approach to data from a seismic line in the Makran accretionary prism in the Arabian Sea, the results reveal lateral variations of gas-hydrate and free-gas saturations of 4–29% and 1–7.5%, respectively, depending on the rock-physics model used to relate seismic velocity to saturation. Our approach is simple and easy to implement.


2000 ◽  
Vol 40 (1) ◽  
pp. 293 ◽  
Author(s):  
G.R. Holdgate ◽  
M.W. Wallace ◽  
J. Daniels S.J. Gallagher ◽  
J.B. Keene ◽  
A.J. Smith

Seaspray Group carbonate sediments of Oligocene to Recent age overlie the main hydrocarbon producing Upper Cretaceous to Eocene Latrobe Group in the offshore Gippsland Basin. Their sonic complexity creates major difficulties for hydrocarbon exploration. Carbonate facies are divisible into three subgroups based on seismic character, sonic logs, velocity profiles, carbonate content, petrologic character and age. The oldest unit is the Angler Subgroup that consists of carbonate pelagic marls (CaC03 70%) with interbedded clastic-rich units.Zones of high velocity (>3,000m/s) are restricted to the deeply buried parts of the Albacore Subgroup, at TWT's greater than 0.8 seconds. The characteristics of this high velocity facies are: a composition of fine grained bioclast-rich packstones and wackestones with less than 10% silt sized quartz; the carbonate content exceeds 60%; the intervals are prone to cementation and are stylolitised; they are diachronous (i.e. cut across seismic boundaries); velocities progressively increase with depth; highest velocities occur where the unit is thickest towards the centre of the basin; velocity increases laterally with steepness of angle on downlap surfaces due to coarser grain sizes and inferred greater initial porosity; and velocities increase with stratigraphic age in the Albacore Subgroup. Regardless of burial depth the Angler and Hapuku Subgroups contain no significantly high velocity zones.An empirical relationship derived from this data set provides a basis for re-interpreting average velocity to the top of the Latrobe Group in areas underlying high velocity canyon-fill sediments.


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