Diffraction tomography applied to crosshole and VSP seismic data

1989 ◽  
Vol 20 (2) ◽  
pp. 169
Author(s):  
J.A. Young
Keyword(s):  
Seismic Data ◽  
A Priori ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Scattered Wave ◽  
Seismic Inversion ◽  
Image Resolution ◽  
Fourier Domain ◽  
Diffraction Tomography ◽  
Data Coverage

Diffraction tomography is an approach to seismic inversion which is analogous to f-k migration. It differs from f-k migration in that it attempts to obtain a more quantitative rather than qualitative image of the Earth's subsurface. Diffraction tomography is based on the generalized projection-slice theorem which relates the scattered wave field to the Fourier spectrum of the scatterer. Factors such as the survey geometry and the source bandwidth determine the data coverage in the spatial Fourier domain which in turn determines the image resolution. Limited view-angles result in regions of the spatial Fourier domain with no data coverage, causing the solution to the tomographic reconstruction problem to be nonunique. The simplistic approach is to assume the missing samples are zero and perform a standard reconstruction but this can result in images with severe artefacts. Additional a priori information can be introduced to the problem in order to reduce the nonuniqueness and increase the stability of the reconstruction. This is the standard approach used in ray tomography but it is not commonly used in diffraction tomography applied to seismic data.This paper shows the application of diffraction tomography to crosshole and VSP seismic data. Using synthetic data, the effects on image resolution of the survey geometry and the finite source bandwidth are examined and techniques for improving image quality are discussed.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

2021 ◽  
Vol 40 (10) ◽  
pp. 751-758
Author(s):  
Fabien Allo ◽  
Jean-Philippe Coulon ◽  
Jean-Luc Formento ◽  
Romain Reboul ◽  
Laure Capar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Hybrid Techniques ◽  
Geothermal Wells

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

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.

Tomographic filtering of mantle circulation models via the generalised inverse: A way to account for seismic data uncertainty

2020 ◽  
Author(s):  
Bernhard S.A. Schuberth ◽  
Roman Freissler ◽  
Christophe Zaroli ◽  
Sophie Lambotte
Keyword(s):  
Seismic Data ◽  
Inverse Operator ◽  
Synthetic Data ◽  
Circulation Model ◽  
Model Space ◽  
Seismic Inversion ◽  
Data Uncertainty ◽  
Earth Model ◽  
Model Parameters ◽  
Noise Component

<p>For a comprehensive link between seismic tomography and geodynamic models, uncertainties in the seismic model space play a non-negligible role. More specifically, knowledge of the tomographic uncertainties is important for obtaining meaningful estimates of the present-day thermodynamic state of Earth's mantle, which form the basis of retrodictions of past mantle evolution using the geodynamic adjoint method. A standard tool in tomographic-geodynamic model comparisons nowadays is tomographic filtering of mantle circulation models using the resolution operator <em><strong>R</strong></em> associated with the particular seismic inversion of interest. However, in this classical approach it is not possible to consider tomographic uncertainties and their impact on the geodynamic interpretation. </p><p>Here, we present a new method for 'filtering' synthetic Earth models, which makes use of the generalised inverse operator <strong>G</strong><sup>†</sup>, instead of using <em><strong>R</strong></em>. In our case, <strong>G</strong><sup>†</sup> is taken from a recent global SOLA Backus–Gilbert <em>S</em>-wave tomography. In contrast to classical tomographic filtering, the 'imaged' model is constructed by computing the <em>Generalised-Inverse Projection</em> (GIP) of synthetic data calculated in an Earth model of choice. This way, it is possible to include the effects of noise in the seismic data and thus to analyse uncertainties in the resulting model parameters. In order to demonstrate the viability of the method, we compute a set of travel times in an existing mantle circulation model, add specific realisations of Gaussian, zero-mean seismic noise to the synthetic data and apply <strong>G</strong><sup>†</sup>. <br> <br>Our results show that the resulting GIP model without noise is equivalent to the mean model of all GIP realisations from the suite of synthetic 'noisy' data and also closely resembles the model tomographically filtered using <em><strong>R</strong></em>. Most important, GIP models that include noise in the data show a significant variability of the shape and amplitude of seismic anomalies in the mantle. The significant differences between the various GIP realisations highlight the importance of interpreting and assessing tomographic images in a prudent and cautious manner. With the GIP approach, we can moreover investigate the effect of systematic errors in the data, which we demonstrate by adding an extra term to the noise component that aims at mimicking the effects of uncertain crustal corrections. In our presentation, we will finally discuss ways to construct the model covariance matrix based on the GIP approach and point out possible research directions on how to make use of this information in future geodynamic modelling efforts.</p>

Depth-domain seismic reflectivity inversion with compressed sensing technique

2017 ◽  
Vol 5 (1) ◽  
pp. T1-T9 ◽  
Author(s):  
Rui Zhang ◽  
Kui Zhang ◽  
Jude E. Alekhue
Keyword(s):  
Compressed Sensing ◽  
Time Domain ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Synthetic Data ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Depth Migration ◽  
Band Limited ◽  
The Time Domain

More and more seismic surveys produce 3D seismic images in the depth domain by using prestack depth migration methods, which can present a direct subsurface structure in the depth domain rather than in the time domain. This leads to the increasing need for applications of seismic inversion on the depth-imaged seismic data for reservoir characterization. To address this issue, we have developed a depth-domain seismic inversion method by using the compressed sensing technique with output of reflectivity and band-limited impedance without conversion to the time domain. The formulations of the seismic inversion in the depth domain are similar to time-domain methods, but they implement all the elements in depth domain, for example, a depth-domain seismic well tie. The developed method was first tested on synthetic data, showing great improvement of the resolution on inverted reflectivity. We later applied the method on a depth-migrated field data with well-log data validated, showing a great fit between them and also improved resolution on the inversion results, which demonstrates the feasibility and reliability of the proposed method on depth-domain seismic data.

Robust deep learning seismic inversion with a priori initial model constraint

2021 ◽  
Author(s):  
Jian Zhang ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Yuanqiang Li ◽  
Guangtan Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
A Priori ◽  
Seismic Inversion ◽  
Training Dataset ◽  
Initial Model ◽  
Data Set ◽  
The Real ◽  
Model Constraint ◽  
Trained Network

Summary Seismic inversion is one of the most commonly used methods in the oil and gas industry for reservoir characterization from observed seismic data. Deep learning (DL) is emerging as a data-driven approach that can effectively solve the inverse problem. However, existing deep learning-based methods for seismic inversion utilize only seismic data as input, which often leads to poor stability of the inversion results. Besides, it has always been challenging to train a robust network since the real survey has limited labeled data pairs. To partially overcome these issues, we develop a neural network framework with a priori initial model constraint to perform seismic inversion. Our network uses two parts as one input for training. One is the seismic data, and the other is the subsurface background model. The labels for each input are the actual model. The proposed method is performed by log-to-log strategy. The training dataset is firstly generated based on forward modeling. The network is then pre-trained using the synthetic training dataset, which is further validated using synthetic data that has not been used in the training step. After obtaining the pre-trained network, we introduce the transfer learning strategy to fine-tune the pre-trained network using labeled data pairs from a real survey to acquire better inversion results in the real survey. The validity of the proposed framework is demonstrated using synthetic 2D data including both post-stack and pre-stack examples, as well as a real 3D post-stack seismic data set from the western Canadian sedimentary basin.

Fourier reconstruction of nonuniformly sampled, aliased seismic data

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. V21-V32 ◽  
Author(s):  
P. M. Zwartjes ◽  
M. D. Sacchi
Keyword(s):  
Seismic Data ◽  
Fourier Coefficients ◽  
A Priori ◽  
Nonuniform Sampling ◽  
Second Step ◽  
Fourier Domain ◽  
Sampled Data ◽  
Underlying Assumption ◽  
Fourier Reconstruction ◽  
Step Algorithm

There are numerous methods for interpolating uniformly sampled, aliased seismic data, but few can handle the combination of nonuniform sampling and aliasing. We combine the principles of Fourier reconstruction of nonaliased, nonuniformly sampled data with the ideas of frequency-wavenumber [Formula: see text] interpolation of aliased, uniformly sampled data in a new two-step algorithm. In the first step, we estimate the Fourier coefficients at the lower nonaliased temporal frequencies from the nonuniformly sampled data. The coefficients are then used in the second step as an a priori model to distinguish between aliased and nonaliased energy at the higher, aliased temporal frequencies. By using a nonquadratic model penalty in the inversion, both the artifacts in the Fourier domain from nonuniform sampling and the aliased energy are suppressed. The underlying assumption is that events are planar; therefore, the algorithm is applied to seismic data in overlapping spatiotemporal windows.

Seismic inversion with adaptive edge-preserving smoothing preconditioning on impedance model

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R11-R19 ◽  
Author(s):  
Ronghuo Dai ◽  
Cheng Yin ◽  
Nueraili Zaman ◽  
Fanchang Zhang
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
A Priori ◽  
Window Size ◽  
Seismic Inversion ◽  
Small Scale ◽  
Inversion Method ◽  
Reservoir Prediction ◽  
Edge Preserving ◽  
Impedance Model

Poststack seismic impedance inversion is an effective approach for reservoir prediction. Due to the sensitivity to noise and the oscillation near the bed boundary, Gaussian distribution constrained seismic inversion is unfavorable to delineate the subtle-reservoir and small-scale geologic features. To overcome this shortcoming, we have developed a new method that incorporates a priori knowledge in the seismic inversion through a preconditioning impedance model using the adaptive edge-preserving smoothing (Ad-EPS) filter. The Ad-EPS filter preconditioned impedance model for a blocky solution makes the formation interfaces and geologic edges more precise and sharper in the inverted impedance results and keeps the inversion procedure robust even if random noise exists in the seismic data. Furthermore, compared with the conventional EPS filter, the Ad-EPS filter is able to resolve thick and thin geologic features through window size scanning, which is used to find the best-fitting window size for each sample to be filtered. The results of numerical examples and real seismic data test indicate that our inversion method can suppress noise to obtain a “blocky” inversion result and preserve small geologic features.

scholarly journals Semisupervised sequence modeling for elastic impedance inversion

2019 ◽  
Vol 7 (3) ◽  
pp. SE237-SE249 ◽  
Author(s):  
Motaz Alfarraj ◽  
Ghassan AlRegib
Keyword(s):  
Seismic Data ◽  
Synthetic Data ◽  
Seismic Inversion ◽  
Machine Learning Algorithms ◽  
Modeling Framework ◽  
Data Set ◽  
Sequence Modeling ◽  
Impedance Inversion ◽  
Applications Of Machine Learning ◽  
Elastic Impedance

Recent applications of machine learning algorithms in the seismic domain have shown great potential in different areas such as seismic inversion and interpretation. However, such algorithms rarely enforce geophysical constraints — the lack of which might lead to undesirable results. To overcome this issue, we have developed a semisupervised sequence modeling framework based on recurrent neural networks for elastic impedance inversion from multiangle seismic data. Specifically, seismic traces and elastic impedance (EI) traces are modeled as a time series. Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for EI. The proposed workflow uses well-log data to guide the inversion. In addition, it uses seismic forward modeling to regularize the training and to serve as a geophysical constraint for the inversion. The proposed workflow achieves an average correlation of 98% between the estimated and target EI using 10 well logs for training on a synthetic data set.

Towards 1-Ångstrom-resolution STEM

1993 ◽  
Vol 51 ◽  
pp. 996-997
Author(s):  
H.S. von Harrach ◽  
D.E. Jesson ◽  
S.J. Pennycook
Keyword(s):  
High Resolution ◽  
Field Emission ◽  
Phase Contrast ◽  
Spherical Aberration ◽  
A Priori ◽  
Dark Field ◽  
Image Resolution ◽  
Objective Lens ◽  
Annular Dark Field ◽  
First Results

Phase contrast TEM has been the leading technique for high resolution imaging of materials for many years, whilst STEM has been the principal method for high-resolution microanalysis. However, it was demonstrated many years ago that low angle dark-field STEM imaging is a priori capable of almost 50% higher point resolution than coherent bright-field imaging (i.e. phase contrast TEM or STEM). This advantage was not exploited until Pennycook developed the high-angle annular dark-field (ADF) technique which can provide an incoherent image showing both high image resolution and atomic number contrast.This paper describes the design and first results of a 300kV field-emission STEM (VG Microscopes HB603U) which has improved ADF STEM image resolution towards the 1 angstrom target. The instrument uses a cold field-emission gun, generating a 300 kV beam of up to 1 μA from an 11-stage accelerator. The beam is focussed on to the specimen by two condensers and a condenser-objective lens with a spherical aberration coefficient of 1.0 mm.

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