Acoustic Impedance Deblurring With a Deep Convolution Neural Network

Domain-specific methods for deblurring particular sorts of objects have gained increasing attention due to the ineffectiveness of generic methods. We present a simple and effective convolutional neural network that deblurs postinversion acoustic impedance images. The architecture of our model consists of a convolutional layer that highlights edges and contours related to interfaces between rock layers; a locally connected layer that performs a convolutional step with unshared weights; and, finally, two fully connected layers that perform a nonlinear estimation of acoustic impedance values. We use the updated Standford VI reservoir model as training data set, which is composed of 150 acoustic impedance sections, each section with 200 traces. In this letter, we adopt a strong supervised learning that exploit, trace by trace, the data set of the inverted and ground truth impedance images. We also present an analysis comparing the frequency bandwidth among the latent, blurry, and deblurred images. Furthermore, the peak signal-to-noise ratio is calculated and compared with a classical deblurring method. We additionally address the requirement of deep learning for the huge amount of training examples by inserting rectified linear units and keeping the network architecture simple. The experimental results demonstrate the efficacy of the proposed method.