The Geophysical Analysis of Energy Resources Project (also called the Geophysical Analysis Project) meets the need within the USGS and the Energy Resources Program to conduct advanced theoretical and applied research in reflection seismology to improve the delineation and characterization of both conventional and unconventional hydrocarbon resources. The scientists of the project fulfill these objectives by providing 2-D and 3-D reflection seismic processing and interpretation expertise in support of various projects. Results from this project provide subsurface structural, stratigraphic, and reservoir-fluid characterization information to resolve unique geologic issues related to hydrocarbon assessment, geologic frameworks, petroleum system delineation, reservoir-fluid identification, and reservoir compartmentalization. Through the National Energy Research Seismic Library (NERSL), the project rescues and preserves digital seismic reflection and related data in danger of being lost or destroyed and provides a mechanism to deliver these data within the USGS and, if non-proprietary, to the public. NPR-A Data Archive and IPOD-1 are two public data catalogs with nearly 100GB of data.

• Seismic Data Processing Steps
• Seismic Data Processing Yilmaz Pdf
• Seismic Data Processing Ppt
• Seismic Data Acquisition Processing And Interpretation Pdf
• Seismic Data Processing Jobs
• Seismic Data Interpretation Using Digital Image Processing Pdf

PDF This modern introduction to seismic data processing in both exploration and global geophysics demonstrates practical applications through real data and tutorial examples. The underlying.

Seismic Processing and Interpretation

The Geophysical Analysis Project is responsible for the operation and maintenance of the USGS Energy Resources Program’s 2-D and 3-D seismic reflection data processing facility located in Denver, Colorado. The scientists of the Geophysical Analysis Project process both newly acquired seismic data, as well as older legacy data. These data include vertical seismic, surface seismic, ocean bottom cable (OBC), ocean bottom seismometer (OBS) data and can be either single- or multi-channel 2-D seismic lines or 3-D seismic volumes.

National Petroleum Reserve-Alaska Seismic Legacy Data Archive is an application that allows users to find and download seismic and well log data. This collection holds nearly 90 GB of information collected from the 1940's to 1970's. Run first by the U.S. Navy and later the USGS, this exploration program collected over 12,000 line miles of seismic data and drilled 28 exploratory wells.

In addition to seismic processing, the Geophysical Analysis Project also provides seismic interpretation capabilities to the USGS Energy Resources Program. The project’s interpreters complete processes such as interpretation of 2-D seismic lines and 3-D seismic volumes (both modern and legacy), synthetic seismogram generation, well log display, correlation, and interpretation, velocity analysis, and 3-D visualization. The interpreters also assist others with the creation of seismic interpretation projects and provide guidance for seismic interpretation-related matters. Project scientists also complete various analyses of geophysical well logs and integrate those with their seismic processing and interpretation.

The processed seismic reflection lines and volumes, their interpretation and visualization, and the corresponding digital databases produced by this project provide valuable continuous subsurface information to support decisions being made concerning resource management at the local, State, and Federal levels and to various international collaborators. This project also provides expertise to other USGS Programs and projects dealing with issues such as deep crustal imaging studies, mapping of the extended continental shelf, and the determination and mapping of potential geologic hazards

This project also hosts the National Energy Research Seismic Library (NERSL), which rescues and preserves digital seismic reflection and related data in danger of being lost or destroyed and provides a mechanism to deliver these data within the USGS and, if non-proprietary, to the public. National Petroleum Reserve-Alaska Data Archive from the Alaska North Slope and IPOD-1 Data Archive from Cape Hatteras to the mid-Atlantic Ridge are two public data catalogs maintaned by the project and contain nearly 100GB of data.

Status - Active

Contacts

Explore More Science

Multidimensional seismic data processing forms a major component of seismic profiling, a technique used in geophysical exploration. The technique itself has various applications, including mapping ocean floors, determining the structure of sediments, mapping subsurface currents and hydrocarbon exploration. Since geophysical data obtained in such techniques is a function of both space and time, multidimensional signal processing techniques may be better suited for processing such data.

• 2Data processing
  • 2.1Multichannel filtering

Data acquisition[edit]

There are a number of data acquisition techniques used to generate seismic profiles, all of which involve measuring acoustic waves by means of a source and receivers. These techniques may be further classified into various categories,^[1] depending on the configuration and type of sources and receivers used. For example, zero-offset vertical seismic profiling (ZVSP), walk-away VSP etc.

The source (which is typically on the surface) produces a wave travelling downwards. The receivers are positioned in an appropriate configuration at known depths. For example, in case of vertical seismic profiling, the receivers are aligned vertically, spaced approximately 15 meters apart. The vertical travel time of the wave to each of the receivers is measured and each such measurement is referred to as a “check-shot” record. Multiple sources may be added or a single source may be moved along predetermined paths, generating seismic waves periodically in order to sample different points in the sub-surface. The result is a series of check-shot records, where each check-shot is typically a two or three-dimensional array representing a spatial dimension (the source-receiver offset) and a temporal dimension (the vertical travel time).

Data processing[edit]

The acquired data has to be rearranged and processed to generate a meaningful seismic profile: a two-dimensional picture of the cross section along a vertical plane passing through the source and receivers. This consists of a series of processes: filtering, deconvolution, stacking and migration.

Multichannel filtering[edit]

Multichannel filters may be applied to each individual record or to the final seismic profile. This may be done to separate different types of waves and to improve the signal-to-noise ratio. There are two well-known methods of designing velocity filters for seismic data processing applications.^[2]

Two-dimensional Fourier transform design[edit]

The two-dimensional Fourier transform is defined as:

${\displaystyle F(\underset{\_}{k},\omega )={\int }_{-\mathrm{\infty }}^{\mathrm{\infty }}{\int }_{-\mathrm{\infty }}^{\mathrm{\infty }}f(\underset{\_}{x},t)e^{-j(\omega t-\underset{\_}{k}\underset{\_}{x})}d\underset{\_}{x}dt}$

where ${\displaystyle \underset{\_}{k}}$ is the spatial frequency (also known as wavenumber) and ${\displaystyle \omega }$ is the temporal frequency. The two-dimensional equivalent of the frequency domain is also referred to as the ${\displaystyle \underset{\_}{k}-\omega }$ domain. There are various techniques to design two-dimensional filters based on the Fourier transform, such as the minimax design method and design by transformation. One disadvantage of Fourier transform design is its global nature; it may filter out some desired components as well.

τ-p transform design[edit]

The τ-p transform is a special case of the Radon transform, and is simpler to apply than the Fourier transform. It allows one to study different wave modes as a function of their slowness values, ${\displaystyle p}$.^[3] Application of this transform involves summing (stacking) all traces in a record along a slope (slant), which results in a single trace (called the p value, slowness or the ray parameter). It transforms the input data from the space-time domain to intercept time-slowness domain.

${\displaystyle p=\frac{1}{v}=\frac{dt}{dx}}$

Each value on the trace p is the sum of all the samples along the line

${\displaystyle t=\tau +px}$

The transform is defined by:

${\displaystyle F(p,\tau )={\int }_{-\mathrm{\infty }}^{\mathrm{\infty }}f(x,\tau +px)dx={\int }_{-\mathrm{\infty }}^{\mathrm{\infty }}{\int }_{-\mathrm{\infty }}^{\mathrm{\infty }}f(x,t)\delta (t-\tau -px)dxdt}$

The τ-p transform converts seismic records into a domain where all these events are separated. Simply put, each point in the τ-p domain is the sum of all the points in the x-t plane lying across a straight line with a slope p and intercept τ.^[4] That also means a point in the x-t domain transforms into a line in the τ-p domain, hyperbolae transform into ellipses and so on. Similar to the Fourier transform, a signal in the τ-p domain can also be transformed back into the x-t domain.

Seismic Data Processing Steps

Deconvolution[edit]

Seismic Data Processing Yilmaz Pdf

During data acquisition, various effects have to be accounted for, such as near-surface structure around the source, noise, wavefront divergence and reverbations. It has to be ensured that a change in the seismic trace reflects a change in the geology and not one of the effects mentioned above. Deconvolution negates these effects to an extent and thus increases the resolution of the seismic data.

Seismic Data Processing Ppt

Seismic data, or a seismogram, may be considered as a convolution of the source wavelet, the reflectivity and noise.^[5] Its deconvolution is usually implemented as a convolution with an inverse filter. Various well-known deconvolution techniques already exist for one dimension, such as predictive deconvolution, Kalman filtering and deterministic deconvolution. In multiple dimensions, however, the deconvolution process is iterative due to the difficulty of defining an inverse operator. The output data sample may be represented as:

${\displaystyle y(\underset{\_}{x},t)=f(\underset{\_}{x},t)\ast \ast r(\underset{\_}{x},t)}$

where ${\displaystyle f(\underset{\_}{x},t)}$ represents the source wavelet, ${\displaystyle r(\underset{\_}{x},t)}$ is the reflectivity function, ${\displaystyle \underset{\_}{x}}$ is the space vector and ${\displaystyle t}$ is the time variable. The iterative equation for deconvolution is of the form:

${\displaystyle f_0(\underset{\_}{x},t)=\lambda y(\underset{\_}{x},t)}$and

${\displaystyle f_{n+1}(\underset{\_}{x},t)=\lambda y(\underset{\_}{x},t)+q(x,t)\ast \ast f_n(\underset{\_}{x},t)}$, where${\displaystyle q(x,t)=\delta (\underset{\_}{x},t)-r(\underset{\_}{x},t)}$

Taking the Fourier transform of the iterative equation gives:

${\displaystyle F_{n+1}(\underset{\_}{k},\omega )=\lambda Y(\underset{\_}{k},\omega )+F_n(\underset{\_}{k},\omega )-\lambda F_n(\underset{\_}{k},\omega )R(\underset{\_}{k},\omega )}$

This is a first-order one-dimensional difference equation with index ${\displaystyle n}$, input ${\displaystyle \lambda Y(\underset{\_}{k},\omega )u(n)}$, and coefficients that are functions of ${\displaystyle (\underset{\_}{k},\omega )}$. The impulse response is ${\displaystyle [1-\lambda R(\underset{\_}{k},\omega ){]}^{n}u(n)}$, where ${\displaystyle u(n)}$ represents the one-dimensional unit step function. The output then becomes:

${\displaystyle F_n(\underset{\_}{k},\omega )=\frac{Y(\underset{\_}{k},\omega )}{R(\underset{\_}{k},\omega )}\{1-[1-\lambda R(\underset{\_}{k},\omega ){]}^{n+1}\}u(n)}$

The above equation can be approximated as

${\displaystyle F_n(\underset{\_}{k},\omega )=\frac{Y(\underset{\_}{k},\omega )}{R(\underset{\_}{k},\omega )}}$,if ${\displaystyle n\to \mathrm{\infty }}$ and

Note that the output is the same as the output of an inverse filter. An inverse filter does not actually have to be realized and the iterative procedure can be easily implemented on a computer.^[6]

Stacking[edit]

Stacking is another process used to improve the signal-to-noise ratio of the seismic profile. This involves gathering seismic traces from points at the same depth and summing them. This is referred to as 'Common depth-point stacking' or 'Common midpoint stacking'. Simply speaking, when these traces are merged, the background noise cancels itself out and the seismic signal add up, thus improving the SNR.

Migration[edit]

Assuming a seismic wave ${\displaystyle s(x,z,t)}$ travelling upwards towards the surface, where ${\displaystyle x}$ is the position on the surface and ${\displaystyle z}$ is the depth. The wave's propagation is described by:

Seismic Data Acquisition Processing And Interpretation Pdf

${\displaystyle \frac{{\mathrm{\partial }}^{2}s}{\mathrm{\partial }{x}^2}+\frac{{\mathrm{\partial }}^{2}s}{\mathrm{\partial }{z}^2}=\frac{1}{c^2}\frac{{\mathrm{\partial }}^{2}s}{\mathrm{\partial }{t}^2}}$

Migration refers to this wave's backward propagation. The two-dimensional Fourier transform of the wave at depth ${\displaystyle z_0}$ is given by:

${\displaystyle S(k_x,z_0,\mathrm{\Omega })=\int \int s(x,z_0,t)e^{-j(\mathrm{\Omega }t-k_{x}x)}dxdt}$

To obtain the wave profile at ${\displaystyle z=z_0}$, the wave field ${\displaystyle s(x,z,t)}$ can be extrapolated to ${\displaystyle s(x,z_0+\mathrm{\Delta }z,t)}$ using a linear filter with an ideal response given by:

where ${\displaystyle {\omega }_1}$ is the x component of the wavenumber, ${\displaystyle k_x}$, ${\displaystyle {\omega }_2}$ is the temporal frequency ${\displaystyle \mathrm{\Omega }}$ and

${\displaystyle \alpha =\frac{1}{c}\frac{\mathrm{\Delta }z}{\mathrm{\Delta }t}}$

For implementation, a complex fan filter is used to approximate the ideal filter described above. It must allow propagation in the region ${\displaystyle |\alpha {\omega }_2|>|{\omega }_1|}$ (called the propagating region) and attenuate waves in the region (called the evanescent region). The ideal frequency response is shown in the figure.^[7]

References[edit]

Seismic Data Processing Jobs

1. ^Rector, James; Mangriotis, M. D. (2010). 'Vertical Seismic Profiling'. Encyclopedia of Solid Earth Geophysics. Springer. pp. 430–433. ISBN978-90-481-8702-7.
2. ^Tatham, R; Mangriotis, M (Oct 1984). 'Multidimensional Filtering of Seismic Data'. Proceedings of the IEEE. 72 (10): 1357–1369. doi:10.1109/PROC.1984.13023.
3. ^Donati, Maria (1995). 'Seismic reconstruction using a 3D tau-p transform'(PDF). CREWES Research Report. 7.
4. ^McMechan, G. A.; Clayton, R. W.; Mooney, W. D. (10 February 1982). 'Application of Wave Field Continuation to the Inversion of Refraction Data'(PDF). Journal of Geophysical Research. 87: 927–935. doi:10.1029/JB087iB02p00927.
5. ^Arya, V (April 1984). 'Deconvolution of Seismic Data - An Overview'. IEEE Transactions on Geoscience Electronics. 16 (2): 95–98. doi:10.1109/TGE.1978.294570.
6. ^Mersereau, Russell; Dudgeon, Dan. Multidimensional Digital Signal Processing. Prentice-Hall. pp. 350–352.
7. ^Mersereau, Russell; Dudgeon, Dan. Multidimensional Digital Signal Processing. Prentice-Hall. pp. 359–363.

External links[edit]

Seismic Data Interpretation Using Digital Image Processing Pdf