scholarly journals Seismic velocity estimation from time migration

2007 ◽  
Author(s):  
Maria Kourkina Cameron
Keyword(s):  
Seismic Velocity ◽  
Velocity Estimation ◽  
Time Migration

scholarly journals Seismic velocity estimation from time migration

2007 ◽  
Vol 23 (4) ◽  
pp. 1329-1369 ◽  
Author(s):  
M K Cameron ◽  
S B Fomel ◽  
J A Sethian
Keyword(s):  
Seismic Velocity ◽  
Velocity Estimation ◽  
Time Migration

scholarly journals Time-to-depth conversion and seismic velocity estimation using time-migration velocity

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE205-VE210 ◽  
Author(s):  
Maria Cameron ◽  
Sergey Fomel ◽  
James Sethian
Keyword(s):  
Time Domain ◽  
Seismic Velocity ◽  
Migration Velocity ◽  
Two Dimensions ◽  
Velocity Estimation ◽  
Three Dimensions ◽  
Velocity Models ◽  
Time Migration ◽  
Geometric Spreading ◽  
Paraxial Ray

The objective was to build an efficient algorithm (1) to estimate seismic velocity from time-migration velocity, and (2) to convert time-migrated images to depth. We established theoretical relations between the time-migration velocity and seismic velocity in two and three dimensions using paraxial ray-tracing theory. The relation in two dimensions implies that the conventional Dix velocity is the ratio of the interval seismic velocity and the geometric spreading of image rays. We formulated an inverse problem of finding seismic velocity from the Dix velocity and developed a numerical procedure for solving it. The procedure consists of two steps: (1) computation of the geometric spreading of image rays and the true seismic velocity in time-domain coordinates from the Dix velocity; (2) conversion of the true seismic velocity from the time domain to the depth domain and computation of the transition matrices from time-domain coordinates todepth. For step 1, we derived a partial differential equation (PDE) in two and three dimensions relating the Dix velocity and the geometric spreading of image rays to be found. This is a nonlinear elliptic PDE. The physical setting allows us to pose a Cauchy problem for it. This problem is ill posed, but we can solve it numerically in two ways on the required interval of time, if it is sufficiently short. One way is a finite-difference scheme inspired by the Lax-Friedrichs method. The second way is a spectral Chebyshev method. For step 2, we developed an efficient Dijkstra-like solver motivated by Sethian’s fast marching method. We tested numerical procedures on a synthetic data example and applied them to a field data example. We demonstrated that the algorithms produce a significantly more accurate estimate of seismic velocity than the conventional Dix inversion. This velocity estimate can be used as a reasonable first guess in building velocity models for depth imaging.

Image-ray tracing for joint 3D seismic velocity estimation and time-to-depth conversion

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. S99-S114 ◽  
Author(s):  
Einar Iversen ◽  
Martin Tygel
Keyword(s):  
Velocity Field ◽  
Seismic Velocity ◽  
A Priori ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Estimation ◽  
P Waves ◽  
Data Set ◽  
Time Migration ◽  
Elementary Waves

Seismic time migration is known for its ability to generate well-focused and interpretable images, based on a velocity field specified in the time domain. A fundamental requirement of this time-migration velocity field is that lateral variations are small. In the case of 3D time migration for symmetric elementary waves (e.g., primary PP reflections/diffractions, for which the incident and departing elementary waves at the reflection/diffraction point are pressure [P] waves), the time-migration velocity is a function depending on four variables: three coordinates specifying a trace point location in the time-migration domain and one angle, the so-called migration azimuth. Based on a time-migration velocity field available for a single azimuth, we have developed a method providing an image-ray transformation between the time-migration domain and the depth domain. The transformation is obtained by a process in which image rays and isotropic depth-domain velocity parameters for their propagation are esti-mated simultaneously. The depth-domain velocity field and image-ray transformation generated by the process have useful applications. The estimated velocity field can be used, for example, as an initial macrovelocity model for depth migration and tomographic inversion. The image-ray transformation provides a basis for time-to-depth conversion of a complete time-migrated seismic data set or horizons interpreted in the time-migration domain. This time-to-depth conversion can be performed without the need of an a priori known velocity model in the depth domain. Our approach has similarities as well as differences compared with a recently published method based on knowledge of time-migration velocity fields for at least three migration azimuths. We show that it is sufficient, as a minimum, to give as input a time-migration velocity field for one azimuth only. A practical consequence of this simplified input is that the image-ray transformation and its corresponding depth-domain velocity field can be generated more easily.

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.

Surface-offset gathers from elastic reverse time migration and velocity analysis

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. S47-S64
Author(s):  
Yang Zhao ◽  
Tao Liu ◽  
Xueyi Jia ◽  
Hongwei Liu ◽  
Zhiguang Xue ◽  
...  
Keyword(s):  
Velocity Ratio ◽  
Computational Cost ◽  
Cost Effective ◽  
Velocity Estimation ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
S Wave ◽  
Time Migration ◽  
The Common ◽  
Wave Decomposition

Angle-domain common-image gathers (ADCIGs) from elastic reverse time migration (ERTM) are valuable tools for seismic elastic velocity estimation. Traditional ADCIGs are based on the concept of common-offset domains, but common-shot domain implementations are often favored for computational cost considerations. Surface-offset gathers (SOGs) built from common-offset migration may serve as an alternative to the common-shot ADCIGs. We have developed a theoretical kinematic framework between these two domains, and we determined that the common SOG gives an alternative measurement of kinematic correctness in the presence of incorrect velocity. Specifically, we exploit analytical expressions for the image misposition between these two domains, with respect to the traveltime perturbation caused by velocity errors. Four formulations of the PP and PS residual moveout functions are derived and provide insightful information of the velocity error, angle, and PS velocity ratio contained in ERTM gathers. The analytical solutions are validated with homogeneous examples with a series of varied parameters. We found that the SOGs may perform in the way of simplicity and linearity as an alternative to the common-shot migration. To make a full comparison with ADCIGs, we have developed a cost-effective workflow of ERTM SOGs. A fast vector P- and S-wave decomposition can be obtained via spatial gradients at selected time steps. A selected ERTM imaging condition is then modified in which the migration is done by offset groups between each source and receiver pair for each P- and S-wave decomposition. Two synthetic (marine and land) examples are used to demonstrate the feasibility of our methods.

Velocity-estimation improvements and migration/demigration using the common-reflection surface with continuing deconvolution in the time domain

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. S229-S238 ◽  
Author(s):  
Martina Glöckner ◽  
Sergius Dell ◽  
Benjamin Schwarz ◽  
Claudia Vanelle ◽  
Dirk Gajewski
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Initial Time ◽  
Model Errors ◽  
Imaging Methods ◽  
Time Migration ◽  
Common Reflection Surface ◽  
The Common ◽  
And Migration

To obtain an image of the earth’s subsurface, time-imaging methods can be applied because they are reasonably fast, are less sensitive to velocity model errors than depth-imaging methods, and are usually easy to parallelize. A powerful tool for time imaging consists of a series of prestack time migrations and demigrations. We have applied multiparameter stacking techniques to obtain an initial time-migration velocity model. The velocity model building proposed here is based on the kinematic wavefield attributes of the common-reflection surface (CRS) method. A subsequent refinement of the velocities uses a coherence filter that is based on a predetermined threshold, followed by an interpolation and smoothing. Then, we perform a migration deconvolution to obtain the final time-migrated image. The migration deconvolution consists of one iteration of least-squares migration with an estimated Hessian. We estimate the Hessian by nonstationary matching filters, i.e., in a data-driven fashion. The model building uses the framework of the CRS, and the migration deconvolution is fully automated. Therefore, minimal user interaction is required to carry out the velocity model refinement and the image update. We apply the velocity refinement and migration deconvolution approaches to complex synthetic and field data.

Distributed acoustic sensing for seismic monitoring in challenging environments

2020 ◽  
Author(s):  
Zack Spica ◽  
Takeshi Akuhara ◽  
Gregory Beroza ◽  
Biondo Biondi ◽  
William Ellsworth ◽  
...  
Keyword(s):  
Seismic Velocity ◽  
Velocity Estimation ◽  
Motion Sensors ◽  
Recording Technology ◽  
Acoustic Sensing ◽  
Seismic Properties ◽  
Seismic Recording ◽  
Distributed Acoustic Sensing ◽  
Geotechnical Information ◽  
Observation Bias

<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>

scholarly journals Shaping regularization in geophysical-estimation problems

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. R29-R36 ◽  
Author(s):  
Sergey Fomel
Keyword(s):  
Conjugate Gradient ◽  
Seismic Velocity ◽  
Velocity Estimation ◽  
Gradient Algorithm ◽  
Regularization Methods ◽  
Insufficient Data ◽  
Data Interpolation ◽  
Estimation Problems ◽  
Additional Constraints ◽  
General Method

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.

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