Seismic Envelope-Driven Broadband Acoustic Impedance Inversion Using End-to-End Deep Sequential Convolutional Neural Network

Absolute impedance estimation is crucial for quantitative interpretation of petrophysical parameters such as porosity and lithology, from band-limited seismic data. The missing low-frequency part of the conventional seismic data leads to non-uniqueness in the solution and causes a hindrance to the absolute impedance estimation. This work presents an application of seismic envelope to retrieve absolute acoustic impedance (AI) values directly from band-limited data in an innovative workflow based on a deep sequential convolutional neural network (DSCNN). Along with the band-limited data and seismic envelope, we also incorporate the instantaneous phase information (to compensate for the lost phase information in a seismic envelope) as an auxiliary input into the DSCNN model to map the band-limited data into broadband data and then to retrieve absolute AI values. We have tested the proposed workflow on two synthetic benchmark datasets of Marmousi2 and SEAM 2D subsalt Earth model, as well as one field dataset of the F3 block, the Netherlands. Our results underline that the proposed approach is efficient in recovering the deeper features quite well as compared to the conventional approach, wherein only seismic band-limited data are used as input. Numerical tests show that the estimated low-frequency impedance is recovered well with our proposed seismic envelope-driven approach. Thus, the proposed workflow provides a robust solution for broadband impedance inversion by utilizing only one regression-based unified deep learning (DL) model. This work primarily highlights the potential of seismic envelope to greatly improve the estimation of low-frequency components of subsurface impedance model in a DL framework. Such a workflow for absolute impedance inversion from band-limited seismic will play an important role in reservoir characterization and in quantifying the elastic attributes.