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
    • Static Correction
  • Tomographic Inversion
  • Residual Moveout
  • AI Imaging
    • Velocity & NMO Analysis
    • Anisotropy Analysis
    • Time to Depth Convrsion
    • 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
      • Static Correction
    • Tomographic Inversion
    • Residual Moveout
    • AI Imaging
      • Velocity & NMO Analysis
      • Anisotropy Analysis
      • Time to Depth Convrsion
      • 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
    • Static Correction
  • Tomographic Inversion
  • Residual Moveout
  • AI Imaging
    • Velocity & NMO Analysis
    • Anisotropy Analysis
    • Time to Depth Convrsion
    • 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

Stacking

Seismic stacking is a key step in seismic data processing. It involves summing multiple seismic traces (typically from Common Midpoint [CMP] gathers) to produce a single trace that represents the subsurface reflectivity at that location. The main goal of stacking is to increase the signal-to-noise ratio (SNR) by reinforcing coherent signals (reflections) while suppressing random and incoherent noise.


  

Why is Stacking Needed?

When seismic waves are recorded:

  • Each reflection event is recorded multiple times at different source-receiver offsets.
  • Coherent reflection signals align properly if the correct Normal Moveout (NMO) correction is applied.
  • Random noise, multiples, and other unwanted energy do not align coherently.
  • Stacking sums these aligned signals, so the reflection signals add constructively, and random noise cancels out destructively     (by √N, where N = number of traces).


  

Basic Stacking Method

  1. CMP Sorting
    • Gather traces that share the same midpoint.

  1. Velocity Analysis
    • Estimate stacking velocity for each CMP gather.

  1. NMO Correction
    • Apply Normal Moveout correction to flatten hyperbolic       reflection events.

  1. Stacking (Simple Arithmetic Stack)
    • Sum or average the NMO-corrected traces       trace-by-trace.

Result: A single high SNR trace for each CMP.

Common Stacking Techniques

Conventional (Arithmetic) Stack

· Simply sums or averages the traces.

· Best when velocity model is good and data is clean.

· Very widely used for 2D and 3D surveys.

Weighted Stack

· Weights traces before stacking to improve SNR.

· Weights can be based on:

o Offset(e.g., near offsets get higher weights)

o Coherency(traces that correlate well are given more weight)

o Signal quality(low noise traces contribute more)

Robust Stack (e.g., F-X or SVD-based)

· For Amplitude Versus Offset (AVO) studies, stacking might preserve true amplitude variations by:

o Limited-offset stack (stacking near-offset only).

o Offset sector stacks (near, mid, far stacks).

o Angle stacks (common in pre-stack migration).

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