Abstract: A facies classification apparatus and method of training the apparatus is provided for seismic image super-resolution. The apparatus includes an input for receiving a low resolution seismic images extracted from a seismic volume and a feature extraction section to extract features from the low resolution images. A non-linear feature mapping section generates feature maps using a self-calibrated block with pixel attention having a plurality of Depthwise Separable Convolution (DSC) layers. A late upsampling section combines at least one DSC layer that upsamples the feature maps to a predetermined dimension. An output provides approximate upsampled super-resolution seismic images that correspond to the low resolution seismic images, with a desired scale of at least two times the low resolution seismic image. A pretrained facies classifier classifies facies in the seismic volume based on the approximate upsampled super-resolution seismic images to obtain class labels.

Abstract: A system and a method for displaying super-resolution images generated from images of lower resolution, includes processor circuitry for a combination multi-core CPU and machine learning engine configured with an input for receiving the low resolution images, a feature extraction section to extract features from the low resolution images, non-linear feature mapping section, connected to the feature extraction section, generating feature maps using a self-calibrated block with pixel attention having a plurality of Depthwise Separable Convolution (DSC) layers, a late upsampling section combines at least one DSC layer and a skip connection that upsamples the feature maps to a predetermined dimension, and a video output for displaying approximate upsampled super-resolution images that corresponds to the low resolution images.

Abstract: A facies classification apparatus and method of training the apparatus is provided for seismic image super-resolution. The apparatus includes an input for receiving a low resolution seismic images extracted from a seismic volume and a feature extraction section to extract features from the low resolution images. A non-linear feature mapping section generates feature maps using a self-calibrated block with pixel attention having a plurality of Depthwise Separable Convolution (DSC) layers. A late upsampling section combines at least one DSC layer that upsamples the feature maps to a predetermined dimension. An output provides approximate upsampled super-resolution seismic images that correspond to the low resolution seismic images, with a desired scale of at least two times the low resolution seismic image. A pretrained facies classifier classifies facies in the seismic volume based on the approximate upsampled super-resolution seismic images to obtain class labels.

Abstract: A system and a method for displaying super-resolution images generated from images of lower resolution, includes processor circuitry for a combination multi-core CPU and machine learning engine configured with an input for receiving the low resolution images, a feature extraction section to extract features from the low resolution images, non-linear feature mapping section, connected to the feature extraction section, generating feature maps using a self-calibrated block with pixel attention having a plurality of Depthwise Separable Convolution (DSC) layers, a late upsampling section combines at least one DSC layer and a skip connection that upsamples the feature maps to a predetermined dimension, and a video output for displaying approximate upsampled super-resolution images that corresponds to the low resolution images.