  Estimating porosity models via seismic data is challenging due to the signal
noise and insufficient resolution of seismic data. Although impedance inversion
is often used by combining with well logs, several hurdles remain to retrieve
sub-seismic scale porosity. As an alternative, we propose a machine
learning-based workflow to convert seismic data to porosity models. A ResUNet++
based workflow is designed to take three seismic data in different frequencies
(i.e., decomposed seismic data) and estimate their corresponding porosity
model. The workflow is successfully demonstrated in the 3D channelized
reservoir to estimate the porosity model with more than 0.9 in R2 score for
training and validating data. Moreover, the application is extended for a
stress test by adding signal noise to the seismic data, and the workflow
results show a robust estimation even with 5\% of noise. Another two ResUNet++
are trained to take either the lowest or highest resolution seismic data only
to estimate the porosity model, but they show under- and over-fitting results,
supporting the importance of using decomposed seismic data in porosity
estimation.
