Impedance Inversion

Inversion of seismic data to acoustic impedance allows an integrated approach to geological interpretation.  TGS offers inversion as the transformation of both poststack and prestack seismic traces into impedance data.

The low-frequency trend of acoustic impedance is usually derived from well logs or stacking velocities and used as a priori information during the inversion process.  This helps enhance the lateral consistency of the impedance data so produced.  The weak high-frequency signal components indicate notches or roll offs on the higher end of the amplitude spectra of seismic traces. Processing steps that tend to broaden the spectral band are usually adopted so that the data that is input to inversion has an enhanced effective frequency bandwidth.

Several different techniques/methodologies are commonly used to perform acoustic impedance inversion.

TGS offers the following types of inversion processes:

Poststack:         - band-limited                                 

                              - model-based

                              - sparse-spike

                              - colored

Prestack:             - simultaneous

                              - elastic

                              - geostatistical

                              - joint inversion

Model-based inversion:

This method models the subsurface as layers or blocks in terms of acoustic impedance and time.  The starting model is defined by a few 3D main time horizons.  Well log data are used to tie the main time horizons to the seismic data and define the impedance bounds for each model layer.  The impedance within each layer may vary laterally and vertically.  The impedance bounds are set to keep the optimized model laterally smooth and within given limits.  The non-uniqueness is taken care of by restricting the number of layers relative to the number of seismic samples.  The starting model is compared to the seismic data and iteratively the model is updated in such a way to better match the seismic data.

Sparse-spike inversion:

This method gives an estimate of the reflectivity series that would approximate the seismic data with a minimum number (sparse) of spikes.  Nonuniqueness is taken care of by applying the sparse reflectivity criterion.  The maximum likelihood deconvolution and L1 norm algorithms are commonly used.

As the sparse spike inversion method tends to remove the embedded wavelet from the data, the inversion results are broadband for the higher frequencies, maximizing vertical resolution and minimizing the tuning effects.