Seismic Signal Processing TRIZ Cases

A remote system determines seismic sensor orientation using microseism records and earthquake waveforms for accurate alignment.

A seismic rock fabric attribute integrates reflection and microseismic data to characterize subsurface geological structures.

Combining dipole and quadrupole measurements resolves 90-degree azimuthal uncertainty in borehole sonic logging.

Multi-angle seismic velocity ratios identify horizontal fractures, resolving vertical fracture prediction limitations in shale gas reservoirs.

Spectral decomposition of seismic traces isolates instantaneous frequencies to determine thin-bed tuning parameters.

Diagonalizes the G matrix using orthonormal basis vectors to identify null vectors, resolving computational time constraints while maintaining accuracy.

Computing statistical data ranges for seismic amplitudes resolves ambiguity in fluid and lithology discrimination.

Directly reconstructs residual depth errors from velocity scans via a derived formula, bypassing complex kinematic demigration to stabilize model updates.

An artificial neural network analyzes 4D seismic image slices to predict reservoir property changes over time.

Extrapolating missing low-frequency components prevents cycle skipping in full wavefield inversion, ensuring accurate subsurface modeling.

Machine learning transforms low-quality seismic data into high-fidelity results using generative adversarial networks.

A slowness filter isolates target acoustic signals by applying phase-delayed waveform subtraction to downhole sonic data.

Well data models identify spatial areas of peg-leg multiples contamination, enabling targeted attenuation that preserves primary signal integrity.

Concentric cylindrical shells simulate tool-induced acoustic dispersion to correct measurement inaccuracies during geologic formation inversion.

A receiver-based ghost filter generates optimized parameters through iterative least squares calculations in the frequency domain.

Transforming marine geophysical data isolates noise patterns, enabling single-pass removal that preserves signal integrity.

Curve fitting extracts slowness dispersion data from acoustic waveform coherence peaks, reducing noise errors in complex borehole conditions.

Separate up-going and down-going wavefields to isolate primary reflections from source-side effects.

A velocity model construction method decomposes subsurface data into hierarchical-scale spatially localized basis functions for iterative updates.

Minimizes average squared differences between seismic spectra from multiple receiver pairs to improve Q estimation accuracy and stability.

Simultaneous geostatistical inversion merges base and monitor surveys to resolve noisy, non-unique property differences in reservoir monitoring.

A one-step inversion method updates a prior model using learned sub-seismic information to improve petrophysical property estimates.