Geoscientist Artificial Intelligence

Geoscientist Artificial IntelligenceGeoscientist Artificial IntelligenceGeoscientist Artificial Intelligence

Geoscientist Artificial Intelligence

Geoscientist Artificial IntelligenceGeoscientist Artificial IntelligenceGeoscientist Artificial Intelligence
  • Home
  • AI Signal Processing
    • Deconvolution
    • Inverse Q Filtering
    • Noise Attenuation
    • Multiple Attenuation
  • AI Imaging
    • Velocity & NMO Analysis
    • Anisotropy Analysis
    • Time to Depth Convrsion
    • Residual Moveout
    • Tomographic Inversion
    • Stacking
    • Migration
    • Wave Equation Datuming
  • AI Inversion
    • Deterministic
    • Stochastic
    • Elastic
    • Petrophysical
    • Time-Lapse (4D)
    • Machine Learning
  • AI AVO Analysis
    • AVO Classification
    • AVO Inversion
    • Rock Physics Modeling
    • AVO Attributes
    • Multi-Component Analysis
    • Calibration & Validation
  • AI Depth Conversion
    • Time-Depth Relationships
    • Well Log Integration
    • Seismic Interpretation
    • Uncertainty Analysis
    • Advanced Computaion Tech
  • AI Data Integration
    • Gravity and Magnetic Data
    • Electromagnetic (EM)
    • Advaned Data Fusion
  • AI FWI
    • Modeling and Simulation
    • Regularized & Constraints
    • Model Parameterization
    • Other Data Integration
    • Anisotropy & Attenuation
  • More
    • Home
    • AI Signal Processing
      • Deconvolution
      • Inverse Q Filtering
      • Noise Attenuation
      • Multiple Attenuation
    • AI Imaging
      • Velocity & NMO Analysis
      • Anisotropy Analysis
      • Time to Depth Convrsion
      • Residual Moveout
      • Tomographic Inversion
      • Stacking
      • Migration
      • Wave Equation Datuming
    • AI Inversion
      • Deterministic
      • Stochastic
      • Elastic
      • Petrophysical
      • Time-Lapse (4D)
      • Machine Learning
    • AI AVO Analysis
      • AVO Classification
      • AVO Inversion
      • Rock Physics Modeling
      • AVO Attributes
      • Multi-Component Analysis
      • Calibration & Validation
    • AI Depth Conversion
      • Time-Depth Relationships
      • Well Log Integration
      • Seismic Interpretation
      • Uncertainty Analysis
      • Advanced Computaion Tech
    • AI Data Integration
      • Gravity and Magnetic Data
      • Electromagnetic (EM)
      • Advaned Data Fusion
    • AI FWI
      • Modeling and Simulation
      • Regularized & Constraints
      • Model Parameterization
      • Other Data Integration
      • Anisotropy & Attenuation
  • Home
  • AI Signal Processing
    • Deconvolution
    • Inverse Q Filtering
    • Noise Attenuation
    • Multiple Attenuation
  • AI Imaging
    • Velocity & NMO Analysis
    • Anisotropy Analysis
    • Time to Depth Convrsion
    • Residual Moveout
    • Tomographic Inversion
    • Stacking
    • Migration
    • Wave Equation Datuming
  • AI Inversion
    • Deterministic
    • Stochastic
    • Elastic
    • Petrophysical
    • Time-Lapse (4D)
    • Machine Learning
  • AI AVO Analysis
    • AVO Classification
    • AVO Inversion
    • Rock Physics Modeling
    • AVO Attributes
    • Multi-Component Analysis
    • Calibration & Validation
  • AI Depth Conversion
    • Time-Depth Relationships
    • Well Log Integration
    • Seismic Interpretation
    • Uncertainty Analysis
    • Advanced Computaion Tech
  • AI Data Integration
    • Gravity and Magnetic Data
    • Electromagnetic (EM)
    • Advaned Data Fusion
  • AI FWI
    • Modeling and Simulation
    • Regularized & Constraints
    • Model Parameterization
    • Other Data Integration
    • Anisotropy & Attenuation

Anisotropy Analysis in Seismic Processing

 Anisotropy analysis in seismic processing involves studying the directional dependence of seismic wave velocities in the Earth's subsurface. In an anisotropic medium, seismic waves travel at different speeds depending on their direction relative to the rock's internal structure, such as aligned fractures, bedding planes, or stress fields. Understanding and analyzing this anisotropy is crucial for accurate subsurface imaging, as it can significantly affect seismic wave propagation and reflection characteristics.


Importance of Anisotropy Analysis: 

 Accurate anisotropy analysis is essential for improving the precision of seismic velocity models, which in turn enhances the quality of seismic imaging and interpretation. By accounting for anisotropy, geoscientists can better resolve subsurface features, optimize well placement, and improve reservoir management. Anisotropy analysis is particularly valuable in complex geological settings, such as fractured reservoirs or areas with significant tectonic stress, where conventional isotropic models might fail to capture the true subsurface conditions.

Anisotropy medium

 Vertical Transverse Isotropy (VTI):

  • Description: VTI occurs when the subsurface layers have horizontal symmetry, often due to layered sedimentary rocks. In this case, seismic waves travel faster vertically than horizontally.
  • Applications: VTI analysis helps correct for time distortions in seismic data and improve imaging in areas with layered geology.

Horizontal Transverse Isotropy (HTI):

  • Description: HTI occurs when the symmetry axis is horizontal, often due to aligned vertical fractures or stress fields. In HTI media, seismic waves travel faster along the fractures than perpendicular to them.
  • Applications: HTI analysis is crucial for characterizing fractured reservoirs, understanding stress orientations, and optimizing drilling directions.

Eta Seismic Anisotropy and Its Role in Seismic Imaging

Eta (η) seismic anisotropy refers to a parameter used in anisotropic seismic imaging to account for weak vertical transverse isotropy (VTI) or tilted transverse isotropy (TTI). It is defined in terms of Thomsen’s parameters and helps correct seismic velocity distortions caused by anisotropic subsurface materials. Eta seismic anisotropy plays a crucial role in modern seismic imaging by improving velocity models, migration accuracy, and reflector positioning. Proper application of η in seismic processing helps geophysicists obtain clearer subsurface images, reducing exploration risks in oil & gas or geotechnical studies.


Definition of Eta (η) in Seismic Anisotropy

Eta (η) is derived from Thomsen’s parameters (ε,δ), which describe the variation of seismic wave velocity with direction in anisotropic media. The η parameter is defined as:


η=(ε−δ)/(1+2δ)


where:

  • ε (epsilon): Measures the difference between horizontal and vertical P-wave velocities.
  • δ (delta):  Controls the shape of wavefronts and affects moveout corrections.

The eta parameter is crucial for improving seismic imaging in anisotropic media, particularly in sedimentary basins with shale layers or deep reservoirs.

Application of Eta (η) in Seismic Imaging

Eta seismic anisotropy is applied in seismic velocity models and migration algorithms to improve imaging accuracy. The key applications include:


A. Time and Depth Migration

  • Seismic waves in anisotropic media travel at different velocities depending on their direction. Ignoring anisotropy leads to mispositioning of reflectors.
  • Eta correction is used in pre-stack depth migration (PSDM) and reverse time migration (RTM) to ensure accurate reflector placement.

B. Moveout Corrections in Seismic Processing

  • In anisotropic media, conventional hyperbolic moveout equations fail.
  • Non-hyperbolic moveout corrections using eta help in proper velocity analysis and stacking.

C. Velocity Model Building

  • Velocity models need to incorporate η to correctly handle travel time distortions in seismic imaging.
  • Tomographic inversion techniques use eta to refine velocity models and improve subsurface interpretation.

Seismic anisotropy analysis and automatic Eta (η) picking

Comparison of isotropy Vs anisotropy NMO correction Gathers

 Azimuthal Anisotropy involves analyzing how seismic wave velocities change with azimuth, or direction of wave propagation relative to the Earth's surface. 

Azimuthal anisotropy is often used to identify and characterize fracture networks and stress fields.

Azimuthal anisotropy analysis helps in fracture detection, reservoir characterization, and assessing the impact of stress fields on reservoir behavior.

Shear-Wave Splitting occurs when a shear wave enters an anisotropic medium and splits into two polarized waves traveling at different velocities. 

The analysis of shear-wave splitting provides valuable information about the orientation and intensity of anisotropy.

Shear-wave splitting is used to map fracture orientations, identify stress regimes, and improve subsurface models.

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