Seismic Signal Processing TRIZ Cases
- LS-FDR Depth Migration for High-Resolution Seismic Imaging
An LS-FDR depth-migration workflow refines an initial earth model to produce high-fidelity images for drilling decisions.
- Omnifocal Seismic Denoising With Dip-Corrected Gather Stacking
Dip correction aligns neighboring seismic gathers before weighted combining, attenuating noise and improving subterranean image resolution.
- Seismic Data Internal Multiple Removal via Pseudo-Depth Aggregation
Pseudo-depth transformation and aggregation make 3D internal multiple prediction more feasible, reducing ISS computation for seismic interpretation.
- Subsurface Structure Identification With CNN Seismic Segmentation
Learn how a CNN partitions migrated seismic volumes into sub-volumes and maps subsurface structures, cutting geo-body segmentation time by 1,000× or more.
- Seismic Imaging Framework with Gradient Conditioning
Synthetic seismic data and gradient conditioning improve iterative inversion for clearer velocity models and fewer false anomalies.
- Borehole Receiver Depth from Longest-Wavelength Seismic Waves
Weak seismic events can be masked by background noise; this case sets borehole receiver depth from the longest measured surface-wave wavelength to improve SNR.
- Fourier Anti-Leakage Seismic Interpolation via Iterative Spectrum Refinement
Weight functions remove strongest spectral attributes slice by slice before inverse transformation, reducing ALFT computation.
- Seismic Noise Attenuation During Simultaneous Source Deblending
Expanding receiver gathers before source excitation helps attenuate ambient noise and recover weak signals during seismic deblending.
- Subsurface Map Updating Through Seismic–Well Orientation Alignment
Interpolating well-measured orientations at well–surface intersections aligns seismic data and reduces subsurface map uncertainty.
- Phase Cross-Correlation Seismic Velocity Modeling for Cycle-Skipping
Phase cross-correlation aligns observed and simulated seismic traces to reduce cycle-skipping in velocity models and subterranean imaging.
- Rotating Water-Tank Calibration for Hydrophone Line Arrays
A rotating fixture, reference hydrophone, and water-filled tank calibrate line-array receive sensitivity while limiting boundary effects.
- Physics-Guided CNN Workflow for Automated Seismic Inversion
Physics-guided CNNs combine seismic stacks, well logs, and structural models to automate inversion and reduce manual supervision.
- Low-Order Interbed Multiple Identification Using Velocity Spectra
Velocity spectra and propagation paths distinguish low-order interbed multiples from primaries for more reliable seismic imaging.
- Seismic Imaging With Velocity Gradient Conditioning for Subsurface Accuracy
Near-surface heterogeneity and illumination gaps are addressed by conditioning velocity gradients before iterative seismic inversion.
- Seismic Data Internal Multiple Removal Using Pseudo-Depth Aggregation
Pseudo-depth transformation and aggregation split 3D seismic processing into tractable steps for accurate internal multiple prediction and removal.
- Automated 3D Seismic Interpretation With Swin Transformer Pretraining
Limited 3D hardware memory and scarce diverse labels are addressed with transformer pretraining and task-specific fine-tuning for field seismic.
- Bayesian Depth Uncertainty from Seismic Velocity and Anisotropy
Candidate velocity and anisotropic models feed a Bayesian seismic workflow using joint probabilities and MCMC to quantify depth uncertainty.
- Deep Learning Seismic Inversion With Physics-Guided Refinement
A two-network workflow uses seismic stacks, well logs, and structural models to improve property resolution while reducing oversight.
- Source Wavelet Estimation Using Well Logs and Reflectivity Correlation
Well logs and time-migrated seismic data are cross-correlated with band-limited reflectivity to reduce noise and data misfit.
- Seismic Imaging Framework for Acquisition Footprint Reduction
Artifact-specific parameter selection reduces acquisition footprints and improves subsurface imaging accuracy in complex subsalt regions.
- Seismic First-Arrival Picking for Locating Hydrocarbon Formations
Conventional seismic picking is slow and error-prone; texture features, energy ratios, and clustering automate accurate first-arrival detection.
- Seismic Imaging Artifact Classification for Adaptive Footprint Reduction
Automatic artifact classification selects reduction parameters for seismic data, improving subsurface imaging accuracy in complex geological settings.
- Interior-First Seismic Volume Loading for Faster Interpretation
Prioritizing interior seismic-volume portions before boundaries delivers critical data sooner and reduces computation during subsurface interpretation.
- Seismic Shot-Record Arrival Detection With Energy Ratios
Fuzzy c-means separates linear signals from random noise, while an energy-ratio matrix improves seismic arrival picks within a 20-ms tolerance.
- Holographic Inversion With Spectral Analysis for Hydrocarbon Detection
Holographic inversion redatums seismic gathers and compares high- and low-frequency amplitudes to map hydrocarbon anomalies.