Channelized facies recovery based on weighted compressed sensing
Published in 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016
A new image reconstruction algorithm is proposed based on weighted compressed sensing (WCS) for the problem of channelized facies recovery from pixel-based measurements.
This algorithm integrates information from:
- Signal structure dependencies in a transform domain (the discrete cosine transform)
- Multiple-point statistics obtained from a training image.
In the experimental side, excellent results are reported showing that the WCS scheme provides good reconstruction for geological facies models even in the 0.3%-1.0% range of data. Experiments show that the proposed solution outperforms methods based on CS and MPS, when reconstruction performance is measured in terms of similarity perceptual indicators (MSSIM).
Recommended citation: H. Calderón, F. Santibañez, J. F. Silva, J. Ortiz and Á. Egaña. (2015). "Channelized facies recovery based on weighted compressed sensing." 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). Rio de Janerio, pp. 1-5.
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