Seismic Signal Processing TRIZ Cases

Comparing vertical and horizontal shear moduli detects sand production in slow formations.

Laplace domain deghosting operators compensate for source depth and time delay differences, separating mixed signals to increase recorded bandwidth and clarity.

A computerized image synthesis system generates synthetic geological formation images from rock fragment data.

Sparsity penalized signal reconstruction extracts slowness dispersion characteristics from broadband acoustic waves using over-complete dictionaries.

Computing individual source wavelets with local earth filtering effects resolves the trade-off between operational efficiency and source separation accuracy.

Time-frequency masks recover lost high-frequency content from prestack seismic data, maintaining vertical resolution after signal-to-noise ratio enhancement.

Multi-beam echo sounder data corrects high-resolution marine seismic sections by applying precise water depth measurements to two-way travel times.

Bayesian inversion updates geological models via Monte Carlo Markov chains, reducing history matching time and computational costs.

A seismic imaging method uses the Signal-to-Distortion Ratio attribute to quantify time-lapse signal geometry differences.

Segmenting source and receiver wavefields into windows to calculate directional components for seismic imaging.

A method determines karst cave presence using distance values and average data within a three-dimensional geological mesh model.

Voronoi cell density analysis isolates natural fracture networks by removing hydraulic event clusters, resolving measurement precision versus data complexity.

A lateral statistical estimation system links geophysical measurements with genetically connected rock units to delineate hydrocarbon reservoirs.

Transforming ambient noise data into the frequency-wavenumber domain to generate velocity versus depth functions.

Interpolates sparse marine seismic measurements via Fourier transforms and iterative reconstruction to resolve spatial aliasing artifacts.

Global consistency constraints enforce reciprocity and continuity during iterative dip estimation, resolving local inaccuracies in faulted regions.

Embedded discrete fracture modeling refines gridblocks to simulate subterranean transient flow accurately.

Iterative deconvolution extracts coherent signals from near-continuous seismic data, resolving the trade-off between processing speed and measurement precision.

Graph space optimal transport permutations compute partial time shifts to align seismic data, mitigating cycle-skipping in full-waveform inversion.

A seismic inversion method selects elastic parameter values using a global minimization algorithm.

Expanded image gathers separate migration from assembly to process individual traces before summation.

Layered one-way seismic wave propagation operators invert linear equations to balance processing speed and image accuracy.

A transverse variable H-V curve construction method uses Kriging interpolation to align velocity variations with drilling data.

Wavelet transform equalizes signal portions across multiple sensor data sets to isolate excess noise components for removal.

Calibration system aligns well facility equipment data into time series format to resolve noise from disparate sources.

A map-viewing interface displays geo-positioned utility layers and file tags within a single computing system window.

A generalized internal multiple imaging procedure uses a background Green's function to generate higher order images.

Inverting interval splitting intensity values estimates subsurface anisotropic parameters directly from surface seismic data.

Partial labelling and dynamic distance loss reduce labeled data requirements while improving fault detection accuracy in geophysical interpretation.

Computing a transfer function normalizes wavelet effects across surveys, resolving nonrepeatable energy source issues in 4-D seismic monitoring.

An evolutionary algorithm refines these candidates through iterative retraining, resolving vertical resolution losses in carbonated subsoils.

An analytic filter aligns seismic traces from base and monitor surveys to estimate time shifts accurately.

Neural networks predict sonic logs for high-resolution 3D seismic depth conversion, resolving resolution loss in reservoir characterization.

A phase-based dispersion analysis method derives slowness from acoustic signals using linear fitting of phase differences across receiver stations.

A coherency filter component applies a linear 3D least-squares tau-P transformation to seismic data.

Sparse seismic monitor data combines with external base survey information to reconstruct accurate 3D reservoir images.

A faulted seismic horizon mapping method incorporates discrete fault planes into an optimization framework to extract accurate subsurface structures.

Dimensionality reduction segments high-dimensional seismic data into lower subspaces, lowering computational time while maintaining prediction accuracy.

A preserved-amplitude reverse time migration full waveform inversion method updates velocity models using zero time lag deconvolution of wavefields.

Multi-stage inversion method separates blended seismic signals using sparse optimization to resolve source interference noise.

Optimization operations on seismic traces estimate wavelets and reflectivity changes to resolve inaccuracies in 4D reservoir interpretation.

A compressive domain transform separates and predicts multiple diffractions in seismic data.

Least-square migration uses dip decomposition and matching operators to reduce computational time while maintaining image resolution.

Azimuthal binning determines fast anisotropy axes to generate gathers, resolving imaging inaccuracies caused by velocity variations.

Linear-to-linear transformation maps shear wave velocity to lateral tectonic strain, resolving constant strain assumptions that distort stress profiles.

Synthesized sonic logs fill gaps in wellbore data, enabling accurate mechanical property determination without complete physical logging.

A deghosting algorithm removes source and receiver ghost signals from marine seismic data using sparse inversion techniques.

Separates seismic energy into aliased and unaliased parts in the frequency-wavenumber domain for accurate processing.

Machine learning models estimate lithology probabilities from seismic and well log data, reducing gas volume prediction percent error from 38.6% to 16.9%.

Seismic analysis system estimates time-dependent rock properties using multi-interval data trends to resolve incomplete subsurface measurements.