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.

The issue of how the explicit introduction of information impacts traditional economic analysis of, for example, competitive equilibrium or monopolistic behaviour has been investigated fruitfully within the paradigm of asymmetric information. The success of explaining non-perfectly competitive outcomes has however led to a neglect of the issue of how information itself can be considered valuable. A single Value of Information (VoI) literature does not exits and the issue spans over diverse economic fields and even other disciplines that aim to discriminate signal from noise. Of course any investigation of a Value of Information needs a specific reference frame in which such a "value" may occur.