Seismic velocity estimation using time-reversal focusing

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. U43-U50 ◽  
Author(s):  
Ariel Lellouch ◽  
Evgeny Landa
Keyword(s):  
Wave Equation ◽  
Time Reversal ◽  
Seismic Velocity ◽  
Signal To Noise Ratio ◽  
Onset Time ◽  
Velocity Model ◽  
Source Location ◽  
Velocity Estimation ◽  
Adequate Model ◽  
Traveltime Tomography

Seismic velocity estimation is a challenging task, especially when no initial model is present. In most cases, a traveltime tomography approach is used as a significant part of the workflow. However, it requires noise-sensitive, time-consuming picking and uses a ray approximation of the wave equation. Time reversal (TR) is a fundamental physical concept, based on the wave equation’s invariance under TR operation. If the recorded wavefield is reversed and back-propagated into the medium, it will focus at its original source location regardless of the complexity of the medium. We use this property for seismic velocity analysis, formulated as an inversion problem with focusing at the known source location and onset time as the objective function. It is globally solved using competitive particle swarm optimization and an adequate model parameterization. This approach has the advantages of using the wave equation, being picking-free, handling low signal-to-noise ratio and requiring neither information on the source wavelet nor an initial velocity model. Although the method is discussed in the framework of direct source-receiver path acquisition, the foundations for its use with conventional reflection data are laid. We have determined the method’s usefulness and limitations using synthetic and field crosshole acquisition examples. In both cases, inversion results are compared with a standard traveltime tomography approach and illustrate the advantages of using TR focusing.

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.

Crosscoherence-based interferometry for the retrieval of first arrivals and subsequent tomographic imaging of differential weathering

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. Q37-Q48 ◽  
Author(s):  
Joachim Place ◽  
Deyan Draganov ◽  
Alireza Malehmir ◽  
Christopher Juhlin ◽  
Chris Wijns
Keyword(s):  
Seismic Waves ◽  
Seismic Velocity ◽  
Signal To Noise Ratio ◽  
Seismic Profile ◽  
Seismic Interferometry ◽  
Near Surface ◽  
Tomographic Images ◽  
Traveltime Tomography ◽  
Seismic Methods ◽  
Source Signal

Exhumation of crust exposes rocks to weathering agents that weaken the rocks’ mechanical strength. Weakened rocks will have lower seismic velocity than intact rocks and can therefore be mapped using seismic methods. However, if the rocks are heavily weathered, they will attenuate controlled-source seismic waves to such a degree that the recorded wavefield would become dominated by ambient noise and/or surface waves. Therefore, we have examined the structure of differential weathering by first-break traveltime tomography over a seismic profile extending approximately 3.5 km and acquired at a mining site in Zambia using explosive sources and a source based on the swept-impact seismic technique (SIST). Seismic interferometry has been tested for the retrieval of supervirtual first arrivals masked by uncorrelated noise. However, use of crosscorrelation in the retrieval process makes the method vulnerable to changes in the source signal (explosives and SIST). Thus, we have developed a crosscoherence-based seismic-interferometry method to tackle this shortcoming. We investigate the method’s efficiency in retrieving first arrivals and, simultaneously, correctly handling variations in the source signal. Our results illustrate the superiority of the crosscoherence- over crosscorrelation-based method for retrieval of the first arrivals, especially in alleviating spurious ringyness and in terms of the signal-to-noise ratio. These benefits are observable in the greater penetration depth and the improved resolution of the tomography sections. The tomographic images indicate isolated bodies of higher velocities, which may be interpreted as fresh rocks embedded into a heavily weathered regolith, providing a conspicuous example of differential weathering. Our study advances the potential of seismic methods for providing better images of the near surface (the critical zone).

scholarly journals Ray-tracing traveltime tomography versus wave-equation traveltime inversion for near-surface seismic land data

2017 ◽  
Vol 5 (3) ◽  
pp. SO11-SO19
Author(s):  
Lei Fu ◽  
Sherif M. Hanafy
Keyword(s):  
Wave Equation ◽  
Ray Tracing ◽  
Water Table ◽  
Seismic Data ◽  
Velocity Model ◽  
Water Table Depth ◽  
Seismic Survey ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Initial Starting

Full-waveform inversion of land seismic data tends to get stuck in a local minimum associated with the waveform misfit function. This problem can be partly mitigated by using an initial velocity model that is close to the true velocity model. This initial starting model can be obtained by inverting traveltimes with ray-tracing traveltime tomography (RT) or wave-equation traveltime (WT) inversion. We have found that WT can provide a more accurate tomogram than RT by inverting the first-arrival traveltimes, and empirical tests suggest that RT is more sensitive to the additive noise in the input data than WT. We present two examples of applying WT and RT to land seismic data acquired in western Saudi Arabia. One of the seismic experiments investigated the water-table depth, and the other one attempted to detect the location of a buried fault. The seismic land data were inverted by WT and RT to generate the P-velocity tomograms, from which we can clearly identify the water table depth along the seismic survey line in the first example and the fault location in the second example.

3D wave-equation interferometric migration of VSP free-surface multiples

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. S195-S203 ◽  
Author(s):  
Ruiqing He ◽  
Brian Hornby ◽  
Gerard Schuster
Keyword(s):  
Wave Equation ◽  
Free Surface ◽  
Signal To Noise Ratio ◽  
Time Lapse ◽  
Seismic Profile ◽  
Velocity Estimation ◽  
Seismic Survey ◽  
3D Surface ◽  
Vsp Data ◽  
Image Volume

Interferometric migration of free-surface multiples in vertical-seismic-profile (VSP) data has two significant advantages over standard VSP imaging: (1) a significantly larger imaging area compared to migrating VSP primaries and (2) less sensitivity to velocity-estimation and static errors than other methods for migration of multiples. In this paper, we present a 3D wave-equation interferometric migration method that efficiently images VSP free-surface multiples. Synthetic and field data results confirm that a reflectivity image volume, comparable in size to a 3D surface seismic survey around the well, can be computed economically. The reflectivity image volume has less fold density and lower signal-to-noise ratio than that obtained by a conventional 3D surface seismic survey because of the relatively weak energy of multiples and the limited number of geophones in the well. However, the efficiency of this method for migrating VSP multiples suggests that it might sometimes be a useful tool for 4D seismic monitoring where reflectivity images can be computed quickly for each time-lapse survey.

Moveout analysis of wave-equation extended images

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. S151-S161 ◽  
Author(s):  
Tongning Yang ◽  
Paul Sava
Keyword(s):  
Wave Equation ◽  
Model Building ◽  
Time Lag ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Estimation Methods ◽  
Future Application ◽  
Time Lags ◽  
Imaging Condition ◽  
Space And Time

Conventional velocity analysis applied to images produced by wave-equation migration with a crosscorrelation imaging condition uses moveout information from space lags or focusing information from time lag. However, more robust velocity-estimation methods can be designed to simultaneously take advantage of the semblance and focusing information provided by migrated images. Such a velocity estimation requires characterization of the moveout surfaces defined jointly for space- and time-lags extended images. The analytic solutions to the moveout surfaces can be derived by solving the system of equations representing the shifted source and receiver wavefields. The superposition of the surfaces from many experiments (shots) is equivalent to the envelope for the family of the individual surface. The envelopeforms a shape that can be characterized as a cone in the extended space of depth, space lag, and time lag. When imaged with the correct velocity, the apex of the cone is located at the correct reflection depth and at zero space and time lags. When imaged with the incorrect velocity, the apex of the cone shifts in the depth direction and along the time-lag axis. The characteristics of the cones are directly related to the quality of the velocity model. Thus, their analysis provides a rich source of information for velocity model-building. Synthetic examples verify the derived formulas characterizing the moveout surfaces. The analytic formulas match the numeric experiments well, demonstrating the accuracy of the formulas. Based on information provided by the extended imaging condition, future application for velocity updates can benefit from the robustness of the depth-focusing analysis and of the high resolution of the semblance analysis.

Acquisition/Processing

2021 ◽  
Vol 40 (6) ◽  
pp. 460-463
Author(s):  
Lionel J. Woog ◽  
Anthony Vassiliou ◽  
Rodney Stromberg
Keyword(s):  
Model Building ◽  
Seismic Velocity ◽  
Signal To Noise Ratio ◽  
Velocity Model ◽  
High Signal ◽  
Processing Unit ◽  
High Quality ◽  
Near Surface ◽  
Velocity Model Building ◽  
Static Corrections

In seismic data processing, static corrections for near-surface velocities are derived from first-break picking. The quality of the static corrections is paramount to developing an accurate shallow velocity model, a model that in turn greatly impacts the subsequent seismic processing steps. Because even small errors in first-break picking can greatly impact the seismic velocity model building, it is necessary to pick high-quality traveltimes. Whereas various artificial intelligence-based methods have been proposed to automate the process for data with medium to high signal-to-noise ratio (S/N), these methods are not applicable to low-S/N data, which still require intensive labor from skilled operators. We successfully replace 160 hours of skilled human work with 10 hours of processing by a single NVIDIA Quadro P6000 graphical processing unit by reducing the number of human picks from the usual 5%–10% to 0.19% of available gathers. High-quality inferred picks are generated by convolutional neural network-based machine learning trained from the human picks.

Tomographic modeling of a cross‐borehole data set

Geophysics ◽  
1989 ◽  
Vol 54 (10) ◽  
pp. 1249-1257 ◽  
Author(s):  
Larry R. Lines ◽  
Edward D. LaFehr
Keyword(s):  
Wave Equation ◽  
Ray Tracing ◽  
Velocity Model ◽  
Velocity Estimation ◽  
P Wave ◽  
Seismic Survey ◽  
Equation Modeling ◽  
Data Set ◽  
True Solution ◽  
Velocity Models

In this paper we describe a methodology for estimating P‐wave velocities from a cross‐borehole seismic survey that uses straight‐ray tomography, ray tracing, and finite‐difference wave‐equation modeling to produce velocity models that fit the first‐break traveltimes. After a starting model is established by straight‐ray tomography, the velocity model is checked by ray tracing and wave‐equation modeling. Since the models for each procedure show consistent results and the modeled traveltimes closely match those traveltimes from the actual data, we felt our interpretation was confirmed. However, the fitting of cross‐well first break traveltimes is only a necessary validity check and is not sufficient to guarantee that the true solution has been found. Two wells were drilled through the areas that were anomalous on the derived tomogram and check‐shot velocity surveys were run. Due primarily to a lateral ambiguity in velocity estimation caused by too few near‐vertical raypaths, the check‐shot surveys did not agree with the tomogram velocities. However, subsequently the check‐shot traveltimes were used to place bounds on velocity in a constrained least‐squares procedure; the combined modeling of uphole and cross‐well rays produced an optimum velocity model which satisfies all available data.

Subsalt velocity estimation by target-oriented wave-equation migration velocity analysis: A 3D field-data example

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. U19-U29 ◽  
Author(s):  
Yaxun Tang ◽  
Biondo Biondi
Keyword(s):  
Wave Equation ◽  
Field Data ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Estimation ◽  
Velocity Analysis ◽  
Data Set ◽  
Generalized Born ◽  
Migration Velocity Analysis ◽  
Recorded Data

We apply target-oriented wave-equation migration velocity analysis to a 3D field data set acquired from the Gulf of Mexico. Instead of using the original surface-recorded data set, we use a new data set synthesized specifically for velocity analysis to update subsalt velocities. The new data set is generated based on an initial unfocused target image and by a novel application of 3D generalized Born wavefield modeling, which correctly preserves velocity kinematics by modeling zero and nonzero subsurface-offset-domain images. The target-oriented inversion strategy drastically reduces the data size and the computation domain for 3D wave-equation migration velocity analysis, greatly improving its efficiency and flexibility. We apply differential semblance optimization (DSO) using the synthesized new data set to optimize subsalt velocities. The updated velocity model significantly improves the continuity of subsalt reflectors and yields flattened angle-domain common-image gathers.

scholarly journals Velocity-independent estimation of kinematic attributes in vertical transverse isotropy media using local slopes and predictive painting

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. U73-U85 ◽  
Author(s):  
M. Javad Khoshnavaz ◽  
Andrej Bóna ◽  
Milovan Urosevic
Keyword(s):  
Model Building ◽  
Seismic Velocity ◽  
Transverse Isotropy ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Computational Time ◽  
Sufficient Information ◽  
Zero Offset ◽  
Vertical Transverse Isotropy ◽  
Vti Media

Agood seismic velocity model is required for many routine seismic imaging techniques. Velocity model building from seismic data is often labor intensive and time consuming. The process becomes more complicated by taking nonhyperbolic traveltime estimations into account. An alternative to the conventional time-domain imaging algorithms is to use techniques based on the local event slopes, which contain sufficient information about the traveltime moveout for velocity estimation and characterization of the subsurface geologic structures. Given the local slopes, there is no need for a prior knowledge of a velocity model. That is why the term “velocity independent” is commonly used for such techniques. We improved upon and simplified the previous versions of velocity-independent nonhyperbolic approximations for horizontally layered vertical transverse isotropy (VTI) media by removing one order of differentiation with respect to offset from the imaging kinematic attributes. These kinematic attributes are derived in terms of the local event slopes and zero-offset two-way traveltime (TWTT). We proposed the use of predictive painting, which keeps all the attributes curvature independent, to estimate the zero-offset TWTT. The theoretical contents and performance of the proposed approach were evaluated on synthetic and field data examples. We also studied the accuracy of moveout attributes for shifted hyperbola, rational, three-parameter, and acceleration approximations on a synthetic example. Our results show that regardless of the approximation types, NMO velocity estimate has higher accuracy than the nonhyperbolicity attribute. Computational time and accuracy of the inversion of kinematic attributes in VTI media using our approach were compared with routine/conventional multiparameter semblance inversion and with the previous velocity-independent inversion techniques.

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