Prestack depth imaging of ocean-bottom node data

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCA57-WCA63 ◽  
Author(s):  
Mathias Alerini ◽  
Bärbel Traub ◽  
Céline Ravaut ◽  
Eric Duveneck
Keyword(s):  
Real Data ◽  
Velocity Model ◽  
Quality Data ◽  
Ocean Bottom ◽  
Model Estimation ◽  
Data Set ◽  
Depth Imaging ◽  
The North ◽  
Multiple Attenuation ◽  
The Common

Ocean-bottom node acquisitions provide high-quality data but usually have large distances between the nodes because of cost. This makes the use of conventional processing difficult and has led to relatively little interest in such data for industrial purposes. We have considered a three-step workflow specifically designed for prestack depth imaging of P-waves recorded by ocean-bottom nodes. It consists of multiple attenuation, velocity model estimation, and prestack depth migration. Whereas multiple attenuation and tomography use data in the common-receiver domain, migration is performed in the common-angle domain. One of the main features is the handling of the sparse receiver geometry during velocity model estimation: the reciprocity of the PP-Green’s functions is used to obtain the required tomographic input using only the common-receiver gathers. The tomographic method also provides an estimate of the geologic dip, which is used to limit the migration operator. This provides migrated images free of migration smiles. The workflow contains no additional assumptions compared to those used to process ocean-bottom cable data. We validate the workflow on a 2D line extracted from a 3D real data set acquired in the North Sea. The results show that it is possible to use ocean-bottom data efficiently for prestack depth imaging.

Velocity model estimation for depth imaging: Comparison of three tomography methods on a 2D real data set

2004 ◽  
Vol 52 (5) ◽  
pp. 427-438 ◽  
Author(s):  
Soazig Le Begat ◽  
Herve Chauris ◽  
Vincent Devaux ◽  
Sylvain Nguyen ◽  
Mark Noble
Keyword(s):  
Real Data ◽  
Velocity Model ◽  
Model Estimation ◽  
Data Set ◽  
Depth Imaging

scholarly journals STEREOTOMOGRAPHY FOR VELOCITY MODEL ESTIMATION IN SEISMIC IMAGING: APPLICATION TO REAL DATA FROM JEQUITINHONHA SEDIMENTARY BASIN

2012 ◽  
Vol 30 (4) ◽  
pp. 473 ◽  
Author(s):  
Felipe A. Terra ◽  
Jessé C. Costa ◽  
Amin Bassrei
Keyword(s):  
Model Building ◽  
Seismic Imaging ◽  
Seismic Velocity ◽  
Synthetic Data ◽  
Structural Complexity ◽  
Real Data ◽  
Velocity Model ◽  
Common Source ◽  
Model Estimation ◽  
Data Set

O imageamento sísmico em profundidade é um desafio em áreas geologicamente complexas, onde a velocidade sísmica apresenta variação lateral. Porém, para se obter sucesso no imageamento sísmico em profundidade é necessário que se tenha uma estimativa confiável do modelo de velocidade. A estereotomografia é uma ferramenta efetiva para se alcançar esse propósito. Também denominada de tomografia de inclinação, ela utiliza as vagarosidades e os tempos de trânsito selecionados de famílias de fonte comum e de receptor comum. Nós avaliamos uma alternativa da estereotomografia para a construção do modelo de velocidades. O algoritmo foi validado no conjunto de dados sintéticos Marmousoft e também em dados reais provenientes da Bacia do Jequitinhonha, Brasil, numa região de talude continental. Este conjunto de dados com complexidade estrutural demandou um controle de alta qualidade na seleção de eventos, numa escolha criteriosa dos parâmetros de regularização, e a atenuação de múltiplas de superfície livre. Os resultados tanto para os dados sintéticos como para os reais mostraram a viabilidade computacional e precisão do método. ABSTRACT. Seismic imaging in depth is a challenge in geologically complex areas, where the seismic velocity varies laterally. The estimation of a reliable velocity model is necessary in order to succeed in seismic depth imaging. Stereotomography is an effective tool to achieve this purpose. Also called slope tomography, it uses the slowness and picked traveltimes from reflection events picked in common source and common receiver gathers. We evaluate an alternative implementation of stereotomography for velocity model building. The algorithm was validated in the Marmousoft synthetic data set and also used for velocity model estimation in acontinental slope region, using real data from Jequitinhonha Basin, Brazil. This data set of structural complexity demanded a high quality control of event selection forpicking, judicious choice of regularization parameters and free surface multiple attenuation. The results for both the synthetic and real data have shown the computational feasibility and accuracy of this method.Keywords: stereotomography, regularization, Jequitinhonha Basin

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

Migration moveout analysis and depth focusing

Geophysics ◽  
1993 ◽  
Vol 58 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Claude F. Lafond ◽  
Alan R. Levander
Keyword(s):  
Heterogeneous Media ◽  
A Priori ◽  
Complex Structure ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Real Data ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Data Set ◽  
Velocity Models

Prestack depth migration still suffers from the problems associated with building appropriate velocity models. The two main after‐migration, before‐stack velocity analysis techniques currently used, depth focusing and residual moveout correction, have found good use in many applications but have also shown their limitations in the case of very complex structures. To address this issue, we have extended the residual moveout analysis technique to the general case of heterogeneous velocity fields and steep dips, while keeping the algorithm robust enough to be of practical use on real data. Our method is not based on analytic expressions for the moveouts and requires no a priori knowledge of the model, but instead uses geometrical ray tracing in heterogeneous media, layer‐stripping migration, and local wavefront analysis to compute residual velocity corrections. These corrections are back projected into the velocity model along raypaths in a way that is similar to tomographic reconstruction. While this approach is more general than existing migration velocity analysis implementations, it is also much more computer intensive and is best used locally around a particularly complex structure. We demonstrate the technique using synthetic data from a model with strong velocity gradients and then apply it to a marine data set to improve the positioning of a major fault.

Noise removal by migration of time-shift images

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. S105-S111 ◽  
Author(s):  
Sheng Xu ◽  
Feng Chen ◽  
Bing Tang ◽  
Gilles Lambare
Keyword(s):  
Time Lag ◽  
Real Data ◽  
Velocity Model ◽  
Noise Removal ◽  
Time Shift ◽  
Reverse Time ◽  
Coherent Noise ◽  
Data Set ◽  
Depth Migration ◽  
Time Migration

When using seismic data to image complex structures, the reverse time migration (RTM) algorithm generally provides the best results when the velocity model is accurate. With an inexact model, moveouts appear in common image gathers (CIGs), which are either in the surface offset domain or in subsurface angle domain; thus, the stacked image is not well focused. In extended image gathers, the strongest energy of a seismic event may occur at non-zero-lag in time-shift or offset-shift gathers. Based on the operation of RTM images produced by the time-shift imaging condition, the non-zero-lag time-shift images exhibit a spatial shift; we propose an approach to correct them by a second pass of migration similar to zero-offset depth migration; the proposed approach is based on the local poststack depth migration assumption. After the proposed second-pass migration, the time-shift CIGs appear to be flat and can be stacked. The stack enhances the energy of seismic events that are defocused at zero time lag due to the inaccuracy of the model, even though the new focused events stay at the previous positions, which might deviate from the true positions of seismic reflection. With the stack, our proposed approach is also able to attenuate the long-wavelength RTM artifacts. In the case of tilted transverse isotropic migration, we propose a scheme to defocus the coherent noise, such as migration artifacts from residual multiples, by applying the original migration velocity model along the symmetry axis but with different anisotropic parameters in the second pass of migration. We demonstrate that our approach is effective to attenuate the coherent noise at subsalt area with two synthetic data sets and one real data set from the Gulf of Mexico.

Interpolating wide-aperture ground-penetrating radar beyond aliasing

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. H13-H22 ◽  
Author(s):  
Saulo S. Martins ◽  
Jandyr M. Travassos
Keyword(s):  
Ground Penetrating Radar ◽  
Real Data ◽  
Velocity Model ◽  
Data Sets ◽  
Inversion Problem ◽  
Data Set ◽  
Isolated Points ◽  
Antarctic Continent ◽  
Ground Penetrating ◽  
The Antarctic

Most of the data acquisition in ground-penetrating radar is done along fixed-offset profiles, in which velocity is known only at isolated points in the survey area, at the locations of variable offset gathers such as a common midpoint. We have constructed sparse, heavily aliased, variable offset gathers from several fixed-offset, collinear, profiles. We interpolated those gathers to produce properly sampled counterparts, thus pushing data beyond aliasing. The interpolation methodology estimated nonstationary, adaptive, filter coefficients at all trace locations, including at the missing traces’ corresponding positions, filled with zeroed traces. This is followed by an inversion problem that uses the previously estimated filter coefficients to insert the new, interpolated, traces between the original ones. We extended this two-step strategy to data interpolation by employing a device in which we used filter coefficients from a denser variable offset gather to interpolate the missing traces on a few independently constructed gathers. We applied the methodology on synthetic and real data sets, the latter acquired in the interior of the Antarctic continent. The variable-offset interpolated data opened the door to prestack processing, making feasible the production of a prestack time migrated section and a 2D velocity model for the entire profile. Notwithstanding, we have used a data set obtained in Antarctica; there is no reason the same methodology could not be used somewhere else.

scholarly journals A study of sub-basalt depth imaging using the local Radon attributes on the Erlend Tertiary volcanic complex - North of Shetland, UK

2008 ◽  
Vol 87 (2) ◽  
pp. 135-149
Author(s):  
A. Droujinine ◽  
J. Pajchel ◽  
K. Hitchen
Keyword(s):  
Model Building ◽  
Region Of Interest ◽  
Thick Layer ◽  
Velocity Model ◽  
Volcanic Complex ◽  
Depth Imaging ◽  
The North ◽  
Discrete Radon Transform ◽  
Imaging Results ◽  
And Migration

AbstractAcquiring conventional 3 km towed streamer data along a 2D profile in the North of Shetland (UK) enables us to use the local Radon-attributes within the context of depth processing methodology for accurate delineation of volcanic units and imaging beneath high-velocity layers. The objective is to map the radially-dipping structure of the Erlend pluton and to investigate the potential existence of relatively soft Cretaceous sediments underneath volcanic units. Success in the Erlend Volcano study requires strict attention to the separation between different groups of events. The crucial point is the generalized discrete Radon transform formulated in terms of local wavefront (dip and curvature) characteristics. This transform is utilized during pre-CMP processing and migration to minimize event-coupling artefacts. These artefacts represent cross-talk energy between various wave modes and include the unwanted part of the wavefield. We show how to produce detailed subsurface images within the region of interest (exploration prospect only) by applying the closely tied processes of prestack event enhancement and separation, well-driven time processing for velocity model building, and final event-based prestack depth imaging. Results show enhanced structural detail and good continuity of principal volcanic units and deeper reflections, suggesting a faulted 0.6 – 0.9 km thick layer of Cretaceous sediments in the proximity of well 209/09-1. Our interpretation complements existing low-resolution geophysical models inferred from gravity and wide-angle seismic data alone.

scholarly journals Converted-wave azimuth moveout

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. S99-S110
Author(s):  
Daniel A. Rosales ◽  
Biondo Biondi
Keyword(s):  
Seismic Data ◽  
Extreme Case ◽  
Ocean Bottom ◽  
Sampled Data ◽  
Computationally Efficient ◽  
Converted Wave ◽  
Data Set ◽  
Prestack Migration ◽  
Conversion Point ◽  
The Common

A new partial-prestack migration operator to manipulate multicomponent data, called converted-wave azimuth moveout (PS-AMO), transforms converted-wave prestack data with an arbitrary offset and azimuth to equivalent data with a new offset and azimuth position. This operator is a sequential application of converted-wave dip moveout and its inverse. As expected, PS-AMO reduces to the known expression of AMO for the extreme case when the P velocity is the same as the S velocity. Moreover, PS-AMO preserves the resolution of dipping events and internally applies a correction for the lateral shift between the common-midpoint and the common-reflection/conversion point. An implementation of PS-AMO in the log-stretch frequency-wavenumber domain is computationally efficient. The main applications for the PS-AMO operator are geometry regularization, data-reduction through partial stacking, and interpolation of unevenly sampled data. We test our PS-AMO operator by solving 3D acquisition geometry-regularization problems for multicomponent, ocean-bottom seismic data. The geometry-regularization problem is defined as a regularized least-squares-objective function. To preserve the resolution of dipping events, the regularization term uses the PS-AMO operator. Application of this methodology on a portion of the Alba 3D, multicomponent, ocean-bottom seismic data set shows that we can satisfactorily obtain an interpolated data set that honors the physics of converted waves.

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