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.

Migrating seismic datasets to a dip-angle image domain enables calculation of decimating weights for accurate subsurface imaging.

Inverts seismic data directly to determine porosity and shale volume at single trace locations.

A seismic guided electromagnetic inversion method constrains resistivity maps using stratigraphic structures to enhance subsurface imaging accuracy.

A shale gas total organic carbon prediction method uses pre-stack simultaneous inversion to extract primary-to-shear wave velocity ratio data.

Wavelet convolution on synthesized seismic images resolves the contradiction between enhanced structural information and image clarity.

Acoustic velocity measurements through casing resolve statistical fluctuations in nuclear data, enabling precise hydrocarbon saturation determination.

Sparse seismic monitor data sets generate accurate 3D reservoir representations using external shape information.

Wavefield extrapolation separates up-going and down-going components to predict surface multiples in ocean-bottom cable seismic data.

A microseismic analysis method determines individual correlation exponents based on event distances to identify fracture-related seismic activity.

A processing system estimates subsurface temperatures using seismic two-way travel time data and a thermal conductivity scaling factor.

Adjusts seismic particle motion magnitudes within computed upper bound power spectral densities to mitigate noise in multimeasurement data.

Automated geometrical calculations assign probability values to seismic points for precise fault boundary identification, reducing manual interpretation errors.

Segmenting the array with a mini-streamer resolves noise interference and entanglement trade-offs while enabling precise near-zero offset measurements.

Phase slowness coherence transforms frequency-domain waveforms into a formation slowness curve for anomaly detection.

Inclined borehole measurements resolve poor surface data quality by enabling accurate anisotropic velocity determination for improved subsurface imaging.

Beam tomography integrates depth constraints into earth models, resolving positioning errors in complex subsurface structures.

Reconstructs multiples wavefields as secondary sources to increase angular illumination and resolve poor crossline resolution in seismic imaging.

A nonparametric time series classification method detects seismic wave features by comparing raw data against training libraries.

Automated 1.5-dimensional full waveform inversion integrates uphole velocity data to generate high-resolution subsurface models.

Computer system generates subsurface stack images and angle gathers using distributed incremental least-squares algorithms.

Automates first-arrival picking by calculating equivalent linear velocity from diving wave equations, eliminating manual bias and reducing processing time.

A seismic amplitude analysis method classifies angle stack sub-cubes using standard score statistics to identify amplitude variation with angle anomalies.

Dual receiver arrays measure axial and circumferential acoustic signals to compute five elastic constants directly, eliminating geophysical inference errors.