Seismic Signal Processing TRIZ Cases

Segmented deep neural networks classify seismic waveforms to constrain parameter prediction, resolving overfitting and low accuracy in depth-domain analysis.

Seismic trace analysis blends instantaneous frequency and amplitude attributes to differentiate thick hydrocarbon-bearing sands from water sands.

Segmenting seismic data isolates interference in a residual portion for targeted attenuation, preserving reflection energy and reducing signal leakage.

A multi-scale photoacoustic detection system combines optical imaging with acoustic wave data to generate velocity models for geological structures.

A downhole sonic tool uses motion sensor data to estimate and remove tool-borne noise from pressure signals.

Smart streamer steering devices actively control cable orientation during vessel turns to enable continuous wide-azimuth seismic data collection.

Asymmetric marine seismic source configurations enhance crossline resolution while preserving baseline repeatability for accurate 4D subsurface monitoring.

Maximum likelihood estimation locates microseismic events using simulated waveform models to filter correlated noise and determine seismic moment tensors.

Automated machine learning denoises microseismic waveforms with synthetic dictionaries, replacing cumbersome manual engineering workflows.

Fast sweeping method updates attenuated travel times across a grid, resolving accuracy limits in ray-based approaches while reducing memory requirements.

A downhole acoustic logging tool generates guided borehole waves that propagate into the formation as body waves and reflect from interfaces.

Constrained seismic inversion determines rock properties within identified geobodies to update earth and velocity models.

A visco-acoustic reverse time migration model corrects seismic wave propagation in tilted transverse isotropy media.

Wave-equation deconvolution generates demultiple data by subtracting modeled multiples, resolving shallow water amplitude errors.

Multi-scale optimization extracts seismic horizons using sparse global grids and local adjustments, reducing computational intensity for large datasets.

Extracts constant-phase and minimum-phase wavelets from normalized seismic velocity and acceleration signals.

Pre-computed ray paths link offset-dependent time shifts to model parameters, improving measurement precision without re-tracing during inversion.

A downhole tool system uses Stoneley wave temporal measurements to calculate distance to the borehole bottom.

Transforms subsurface saturation data into front location and sweep intensity parameters for efficient history matching.

Sorting seismic data into common mid-point gathers to estimate trace differences between surveys.