Seismic Signal Processing TRIZ Cases

Seismic energy sources move along a path circumscribing a center with monotonically changing distance to vary shot point spacing.

Convolution of reflection coefficients with an enhanced wavelet restores low-frequency energy to resolve absorption attenuation in deep carbonate reservoirs.

Weighted fit to log-amplitude spectra determines interval seismic quality factor Q insensitive to absolute scaling, resolving bandwidth limitations.

Sensitivity kernels invert flexural wave dispersion data to resolve shear velocity profiles in borehole alteration zones with complex acoustic properties.

Deploying sources and sensors in directional wellbores improves measurement precision of geomechanical properties despite increased device complexity.

Correct Doppler shifts and ray-path variations in seismic data acquired by moving non-impulsive sources via spectral transformations.

Segmenting wavelet transform coefficients into discrete zones resolves heterogeneity challenges by enabling accurate, consistent stratigraphic classification.

Estimates clock drift by inverting relative time shifts from cross-correlated seismic traces, eliminating the need for expensive accurate internal clocks.

A neural network calculates shear slowness from frequency domain dispersion curves, eliminating iterative processing bottlenecks in slow formations.

Structure tensor constraints automate local dip and azimuth estimation, resolving the contradiction between measurement precision and processing efficiency.

Radial profiles of three shear moduli enable accurate horizontal stress estimation in subterranean formations.

Elastic full waveform inversion decomposes seismic data into offset groups to reduce parameter crosstalk during multi-parameter estimation.

Updates average velocity surfaces and calculates correction factor surfaces to reconcile depth-converted time horizons with well marker data.

Integrating active and passive seismic data via beam steering resolves fluid movement monitoring limitations in subsurface formations.

Frequency-dependent deconvolution filter removes source signature effects from seismic data using angle-specific weighting.

Corrects pressure and particle velocity signals using time delays based on arrival angles, resolving spatial aliasing and depth variation issues.

Multi-mode inversion methods analyze compressional and flexural waves to resolve shear anisotropy, overcoming precision limits in complex formations.

Visco-acoustic full waveform inversion generates simultaneous velocity and quality factor models using frequency-dependent velocity.

Inverse multi-resolution singular value decomposition recovers high-frequency losses in seismic signals by hierarchically reconstructing detailed components.

Logarithmic transformation linearizes wavelet coefficients to separate earth properties from seismic artifacts, resolving calculation inaccuracies.

Segments streamer arrays by depth to resolve ghost effect interference, capturing low frequencies from deep layers while maintaining high-frequency resolution.

A method identifies optimal hydraulic fracturing intervals by combining rock quality, in-situ stress, and natural fracture data.

Windowed statistical correlation resolves ambiguity between gradient polarity and symmetry azimuth while stabilizing estimates against noise.

Fully connected neural networks calibrate prestack seismic inversion results using well log data.

Adding a coupling term to the reverse time demigration equation resolves numerical instability, enabling accurate prediction of surface and interbed multiples.

Orthogonal elementary functions estimate seismic amplitude variations across acquisition parameters, resolving under-sampling in narrow azimuth sectors.

Deep learning framework transforms seismic data into the wavenumber-time domain to predict accurate velocity models robust to varying survey configurations.

Compensating for surface-consistent statics and structural dips before convolution reduces errors from water-bottom multiples.

An annihilator verifies extended seismic images against physical laws to refine wave velocity models.

Clustering algorithm extracts representative seismic lines via feature vectors, accelerating database searches and improving geological interpretation accuracy.

Equalize near-continuous wavefield measurements to isolate coherent noise components for accurate marine seismic data processing.

A neural network predicts impedance profiles directly from seismic traces using synthetic training datasets generated via data augmentation.

A processing system isolates intrinsic transducer noise from raw waveforms using adaptive subtraction models for accurate wellbore measurements.

Reverse-time migration processing suppresses non-physical reflection angles in the wavenumber domain to eliminate low frequency noise artifacts.

Convolution of a source wavelet with a geological layer template resolves thin bed thickness beyond quarter-wavelength limits.

Horizontal wellbore seismic profiling resolves spatial resolution limits by deploying source arrays within the formation to assess fracture networks.

Sort seismic traces into midpoint-offset bins and apply linear moveout corrections to reduce computation time while enhancing subsurface imaging accuracy.

Automated migration parameter selection generates accurate subterranean images by averaging multiple outputs, reducing manual analyst effort.

Three-dimensional surface multiple prediction algorithm convolves trace pairs to generate accurate seismic data without regularization.

Staircase discretization superposition stabilizes seismic wave propagation across discontinuous interfaces without grid refinement.

Decomposing seismic traces into predefined wavelets allows selective reconstruction that removes noise components overwhelming desired reflections.

Computes seismically-derived and structurally-derived values to generate difference values for seismic interpretation.

Transforming marine seismic data into the tau-p domain enables precise noise isolation using sparse Radon transforms.

A de-ghosting method generates migration and mirror migration data to produce ghost-free seismic models.

Dividing seismic datasets into subsets reduces memory bottlenecks during 3D Kirchhoff depth migration, achieving 19.5 times greater efficiency.

Cross-correlates seismic traces to generate corrected phase picks for distributed acoustic sensing data.

Workflow determines anisotropy parameters from vertical well logs, eliminating expensive laboratory data collection.

Tomographic inversion of transit time data resolves wavelet rotation issues to produce accurate acoustic and shear impedance estimates.

Decimating non-zero-offset receivers reduces computational time for seismic acquisition illumination without sacrificing imaging quality.