Interpretive evaluation of migrated data

1989 ◽  
Vol 20 (2) ◽  
pp. 17 ◽  
Author(s):  
Ö. Yilmaz
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Field Data ◽  
Weak Link ◽  
Main Role ◽  
Step Size ◽  
Subsurface Geology ◽  
Spatial Aliasing ◽  
Migration Strategies ◽  
Aperture Width

In practice, migration of seismic data requires decision making with regard to: (1) Different migration strategies ? 2-D/3-D, post-stack/prestack, and time/depth migrations; (2) different migration algorithms for a given strategy ? integral, finite-difference and frequency-wavenumber methods; (3) different parameters for a given algorithm ? aperture width, depth-step size, stretch factor; (4) the input data ? profile length, noise content, spatial aliasing and boundary effects; (5) and finally, migration velocities ? the weak link between the seismic method and the subsurface geology that the former tries to image.The seismic interpreter, whose main role is to infer subsurface geology from the migrated data, normally should not be burdened with the decisions concerning the above factors. Fortunately, migration results often are self-evident; a feature considered geologically implausible on a migrated section can be associated with one or more of the above factors. Based on large number of field data cases, I will discuss each of these factors and provide some generally applicable guidelines for migration that an interpreter can invoke in practice.

Deep learning spectral enhancement considering features of seismic field data

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Yonggyu Choi ◽  
Yeonghwa Jo ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Input Data ◽  
Field Data ◽  
A Priori ◽  
Resolution Enhancement ◽  
Training Data ◽  
A Priori Information ◽  
Spectral Enhancement ◽  
Priori Information

The resolution of seismic data dictates the ability to identify individual features or details in a given image, and the temporal (vertical) resolution is a function of the frequency content of a signal. To improve thin-bed resolution, broadening of the frequency spectrum is required; this has been one of the major objectives in seismic data processing. Recently, many researchers have proposed machine learning based resolution enhancement and showed their applicability. However, since the performance of machine learning depends on what the model has learned, output from training data with features different from the target field data may be poor. Thus, we present a machine learning based spectral enhancement technique considering features of seismic field data. We used a convolutional U-Net model, which preserves the temporal connectivity and resolution of the input data, and generated numerous synthetic input traces and their corresponding spectrally broadened traces for training the model. A priori information from field data, such as the estimated source wavelet and reflectivity distribution, was considered when generating the input data for complementing the field features. Using synthetic tests and field post-stack seismic data examples, we showed that the trained model with a priori information outperforms the models trained without a priori information in terms of the accuracy of enhanced signals. In addition, our new spectral enhancing method was verified through the application to the high-cut filtered data and its promising features were presented through the comparison with well log data.

Wave‐equation trace interpolation

Geophysics ◽  
1987 ◽  
Vol 52 (7) ◽  
pp. 973-984 ◽  
Author(s):  
Joshua Ronen
Keyword(s):  
Wave Equation ◽  
Conjugate Gradient ◽  
Seismic Data ◽  
Linear Relation ◽  
Field Data ◽  
Iterative Scheme ◽  
Partial Migration ◽  
Spatial Aliasing ◽  
Zero Offset ◽  
Multichannel Seismic Data

Spatial aliasing in multichannel seismic data can be overcome by solving an inversion in which the model is the section that would be recorded in a well sampled zero‐offset experiment, and the data are seismic data after normal moveout (NMO). The formulation of the (linear) relation between the data and the model is based on the wave equation and on Fourier analysis of aliasing. A processing sequence in which one treats missing data as zero data and performs partial migration before stacking is equivalent to application of the transpose of the operator that actually needs to be inverted. The inverse of that operator cannot be uniquely determined, but it can be estimated using spatial spectral balancing in a conjugate‐gradient iterative scheme. The first iteration is conventional processing (including prestack partial migration). As shown in a field data example in which severe spatial aliasing was simulated, a few more iterations are necessary to achieve significantly better results.

Effects of incomplete parameterization on full-wavefield viscoelastic seismic data for petrophysical reservoir properties

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. O9-O17 ◽  
Author(s):  
Upendra K. Tiwari ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Elastic Model ◽  
Quality Factors ◽  
Noise Levels ◽  
Reservoir Properties ◽  
Estimated Parameters ◽  
Model Parameterization ◽  
The Earth ◽  
Incomplete Model

In inversion of viscoelastic full-wavefield seismic data, the choice of model parameterization influences the uncertainties and biases in estimating seismic and petrophysical parameters. Using an incomplete model parameterization results in solutions in which the effects of missing parameters are attributed erroneously to the parameters that are included. Incompleteness in this context means assuming the earth is elastic rather than viscoelastic. The inclusion of compressional and shear-wave quality factors [Formula: see text] and [Formula: see text] in inversion gives better estimates of reservoir properties than the less complete (elastic) model parameterization. [Formula: see text] and [Formula: see text] are sensitive primarily to fluid types and saturations. The parameter correlations are sensitive also to the model parameterization. As noise increases in the viscoelastic input data, the resolution of the estimated parameters decreases, but the parameter correlations are relatively unaffected by modest noise levels.

Application of fuzzy modelling to predict the earthquake damage degree of buildings based on field data

2021 ◽  
pp. 1-14
Author(s):  
Mohammad Reza Amiri Shahmirani ◽  
Abbas Akbarpour Nikghalb Rashti ◽  
Mohammad Reza Adib Ramezani ◽  
Emadaldin Mohammadi Golafshani
Keyword(s):  
Structural Damage ◽  
Input Data ◽  
Field Data ◽  
Earthquake Occurrence ◽  
Effective Parameters ◽  
Fuzzy Modelling ◽  
Damage Degree ◽  
Input And Output ◽  
The City ◽  
Slight Damage

Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data.

Topographic corrections to slowness measurements in parsimonious migration of land data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. S77-S80 ◽  
Author(s):  
Ibrahim Z. Basi ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Field Data

Parsimonious migration requires that incident and emergent angles be measured, via apparent slownesses, from the seismic data being migrated. When slownesses are measured from land data, it is the apparent slowness along the topography, not the horizontal slowness that is being measured. Thus, errors are introduced into the incident and emergent angle estimates, which are defined via horizontal slownesses. These errors can be corrected using the local topographic dips. A 2D field data example shows that, after correction, a migrated image has significantly improved coherency.

Laplace-domain full-waveform inversion of seismic data lacking low-frequency information

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. R199-R206 ◽  
Author(s):  
Wansoo Ha ◽  
Changsoo Shin
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Single Frequency ◽  
Frequency Content ◽  
Laplace Domain ◽  
Frequency Information ◽  
Velocity Models ◽  
Gaussian Source

The lack of the low-frequency information in field data prohibits the time- or frequency-domain waveform inversions from recovering large-scale background velocity models. On the other hand, Laplace-domain waveform inversion is less sensitive to the lack of the low frequencies than conventional inversions. In theory, frequency filtering of the seismic signal in the time domain is equivalent to a constant multiplication of the wavefield in the Laplace domain. Because the constant can be retrieved using the source estimation process, the frequency content of the seismic data does not affect the gradient direction of the Laplace-domain waveform inversion. We obtained inversion results of the frequency-filtered field data acquired in the Gulf of Mexico and two synthetic data sets obtained using a first-derivative Gaussian source wavelet and a single-frequency causal sine function. They demonstrated that Laplace-domain inversion yielded consistent results regardless of the frequency content within the seismic data.

Removal of surface-related multiples from marine seismic data: A field data example

1995 ◽  
Author(s):  
R.G. van Borselen ◽  
J.T. Fokkema ◽  
P.M. van den Berg
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Marine Seismic

Reconstruction of seismic field data with convolutional U-Net considering the optimal training input data

2019 ◽  
Author(s):  
Jiho Park ◽  
Daeung Yoon ◽  
Soon Jee Seol ◽  
Joongmoo Byun
Keyword(s):  
Input Data ◽  
Field Data

scholarly journals Force Budget: II. Application to Two-Dimensional Flow along Byrd Station Strain Network, Antarctica

1989 ◽  
Vol 35 (119) ◽  
pp. 61-67 ◽  
Author(s):  
C.J. Van Der Veen ◽  
I.M. Whillans
Keyword(s):  
Constitutive Relation ◽  
Input Data ◽  
Field Data ◽  
Two Dimensional ◽  
Water Drainage ◽  
Dimensional Flow ◽  
Basal Sliding ◽  
Longitudinal Variations ◽  
Two Dimensional Flow ◽  
Flow Law

AbstractResistive stresses and velocities at depth are calculated along the Byrd Station Strain Network, Antarctica, using field data. There are found to be large longitudinal variations in basal drag and this result is little affected by errors in the input data or by uncertainties in the constitutive relation for ice. Basal drag varies by a factor of about 2 along the strain network, and is usually equal to the driving stress to within 10–20%. Sites of high drag are not always correlated with basal topographic highs, indicating that some process such as basal water drainage is involved in controlling the friction at the bed. Basal sliding velocities are very sensitive to errors in measured surface velocities and the rate factor in Glen’s flow law. As a result, calculated sliding velocities are much less reliable than deep stresses, and need to be interpreted with caution.

Poststack velocity analysis by separation and imaging of seismic diffractions

Geophysics ◽  
2007 ◽  
Vol 72 (6) ◽  
pp. U89-U94 ◽  
Author(s):  
Sergey Fomel ◽  
Evgeny Landa ◽  
M. Turhan Taner
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Field Data ◽  
Small Scale ◽  
Velocity Analysis ◽  
High Resolution Imaging ◽  
Diffracted Waves ◽  
Resolution Imaging ◽  
Seismic Reflections ◽  
Optimal Focusing

Small geologic features manifest themselves in seismic data in the form of diffracted waves, which are fundamentally different from seismic reflections. Using two field-data examples and one synthetic example, we demonstrate the possibility of separating seismic diffractions in the data and imaging them with optimally chosen migration velocities. Our criteria for separating reflection and diffraction events are the smoothness and continuity of local event slopes that correspond to reflection events. For optimal focusing, we develop the local varimax measure. The objectives of this work are velocity analysis implemented in the poststack domain and high-resolution imaging of small-scale heterogeneities. Our examples demonstrate the effectiveness of the proposed method for high-resolution imaging of such geologic features as faults, channels, and salt boundaries.

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