When interpreting seismic reflection data, a common problem is the uncertainty in the representation of a reflecting interface — is it a peak, zero crossing or trough? This uncertainty itself leads to different qualitative interpretations by different geoscientists and ultimately results in different subsurface property estimations (Bond, et al., 2012). Therefore, it is desirable to find alternative tools (other than reflectivity based) to interpret the subsurface quantitatively. This is the idea behind seismic inversion, based on amplitude variations with offset or angle (AVO/AVA), which effectively transforms prestack seismic reflections into layer properties (e.g. acoustic and elastic impedances). Seismic inversion is the basis for reservoir characterisation and has enabled us to study the subsurface in greater detail and ultimately with less uncertainties and drilling risks (Vargas-Meleza et al., 2004).

Prior to seismic inversion, a great deal is done to preserve the amplitudes in seismic data processing workflows. Further seismic data “conditioning” is also often performed as part of quantitative interpretation (QI) workflows. One of the critical steps in the latter is conditioning the seismic amplitudes, via global (or sometimes local) scaling, such that they honour the background AVO behaviour (after all this is the fundamental part of seismic inversion). These scalers are usually fed into the seismic inversion and are applied to the prestack seismic on the fly as schematically shown in Figure 1. Even though these scalers are often averaged values for the entire survey (i.e. one scaler for each angle stack), their accuracy still has a significant role on the inversion outcome and may require tedious adjustments by trial and error until the inverted impedances optimally match the measured logs at the wells. This is the motivation behind this paper; where we introduce an optimisation algorithm that computes the scalers by working directly in the impedance domain to minimise a cost function based on the difference between the inverted and measured logs at the wells.

Prior to seismic inversion, a great deal is done to preserve the amplitudes in seismic data processing workflows. Further seismic data “conditioning” is also often performed as part of quantitative interpretation (QI) workflows. One of the critical steps in the latter is conditioning the seismic amplitudes, via global (or sometimes local) scaling, such that they honour the background AVO behaviour (after all this is the fundamental part of seismic inversion). These scalers are usually fed into the seismic inversion and are applied to the prestack seismic on the fly as schematically shown in Figure 1. Even though these scalers are often averaged values for the entire survey (i.e. one scaler for each angle stack), their accuracy still has a significant role on the inversion outcome and may require tedious adjustments by trial and error until the inverted impedances optimally match the measured logs at the wells. This is the motivation behind this paper; where we introduce an optimisation algorithm that computes the scalers by working directly in the impedance domain to minimise a cost function based on the difference between the inverted and measured logs at the wells.