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

Velocity analysis

 Velocity analysis is critical in seismic processing to determine the seismic wave velocities within the Earth's subsurface. Accurate velocity models are essential for converting seismic reflection times into depths, which is crucial for creating precise subsurface images. The process involves analyzing the travel times of seismic waves across different source-receiver offsets to estimate the velocity of the layers through which the waves have traveled. 

Semblance Velocity analysis

 Semblance analysis is a seismic processing technique used to evaluate the coherence of seismic reflections across multiple traces in a common midpoint (CMP) gather. It helps identify the best-fitting velocity model by measuring how well seismic events align when different velocities are applied. High semblance values indicate strong alignment, suggesting an accurate velocity estimate for the subsurface layers. This method is particularly valuable in velocity analysis and velocity picking, as it provides a quantitative measure to guide the selection of the most appropriate seismic velocities, ultimately leading to clearer and more accurate subsurface images.

  Velocity picking is a key step in seismic processing where geoscientists manually or automatically select seismic wave velocities that best align reflection events across different offsets in seismic data. This process is crucial for building accurate velocity models, which are used to convert seismic reflection times into depths. Correct velocity picking ensures that subsurface images are clear and accurately represent geological structures, making it an essential component in exploration and subsurface mapping.   This  function takes a semblance matrix, velocity range, and times as input, and selects the best velocity-time points by finding the maximum semblance at each time step.This function scans through each time step, finds the velocity corresponding to the maximum semblance, and outputs a time-velocity pick vector. 

Constant Velocity Stacking Analysis

  Constant velocity stacking (CVS) performs for a range of velocities given a CDP gather (seismic data as a 2D matrix), offsets, velocity range, and sample rate. The output is a 2D matrix where each column corresponds to the stacked trace for a specific velocity.

AI NMO Correction with stretching

Normal Moveout (NMO) analysis is a seismic processing technique used to correct the time differences in seismic reflections caused by varying distances (offsets) between the seismic source and receivers. As seismic waves travel further to reach receivers positioned at greater offsets, they take longer to return, causing a "moveout" in the recorded seismic data.


NMO analysis adjusts these time differences by applying a correction based on an estimated subsurface velocity model. This correction aligns the reflection events across different offsets, allowing them to be summed (or stacked) more effectively. Accurate NMO correction is crucial for building coherent and accurate seismic images, as it directly influences the quality of subsequent processes like stacking and migration. NMO analysis is particularly important in velocity analysis, as it helps refine velocity estimates by showing how well reflections align after correction.

Copyright © 2025 Geoscientist Artificial Intelligent - All Rights Reserved.

Powered by

This website uses cookies.

We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.

Accept