One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision. Traditionally, this is accomplished by visually picking the salt/sediment boundaries, which requires a great amount of manual work and may introduce systematic bias. With recent progress of deep learning algorithm and growing computational power, a great deal of efforts have been made to replace human effort with machine power in salt body interpretation. Currently, the method of Convolutional neural networks (CNN) is revolutionizing the computer vision field and has been a hot topic in the image analysis. In this paper, the benefits of CNNbased classification are demonstrated by using a state-of-art network structure U-Net, along with the residual learning framework ResNet, to delineate salt body with high precision. Network adjustments, including the Exponential Linear Units (ELU) activation function, the Lovász-Softmax loss function, and stratified K-fold cross-validation, have been deployed to further improve the prediction accuracy. The preliminary result using SEG Advanced Modeling (SEAM) data shows good agreement between the predicted salt body and manually interpreted salt body, especially in areas with weak reflections. This indicates the great potential of applying CNN for salt-related interpretations. arXiv:1812.01101v1 [physics.geo-ph] 24 Nov 2018Deep learning, which is capable of extracting extremely detailed features from given data, has had a huge impact on the development of image analysis, especially, semantic segmentation. Recently, deep learning found its application in oil and gas industry, such as well log correlation, fault interpretation (Maniar et al., 2015) and rock facies classification (Chen and Zeng, 2018). Convolutional Neural Network (CNN), being one of the most powerful 'weapons' in the deep learning arsenal, utilizes numerous convolving/pooling/activation layers to obtain a collection of underlying features from the original image. The effectiveness of CNN in salt-body identification has been shown in a recent study (Di et al., 2018), where a proof-of-principle study focusing on factors contributing to the superiority of CNN has been provided.In this paper, we aim to extend on the work by utilizing the state-of-art CNN with U-Net architecture to fully exploit its potential in regards to salt-body identification. We will first describe the deployed convolutional network structure, and then discuss the adjustments we have made to improve the network training. Finally, we will show the preliminary salt interpretation result and will have some discussions on its possible applications and how to further improve.
In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.
Linear feature detection in digital images is an important low-level operationin computer vision that has many applications. In remote sensing tasks, it can be usedto extract roads, railroads, and rivers from satellite or low-resolution aerialimages,which can be used for the capture or update of data for geographic information andnavigation systems. In addition, it is useful in medical imaging for the extraction ofblood vessels from an X-ray angiography or the bones in the skull from a CT or MRimage. It also can be applied in horticulture for underground plant root detection inminirhizotron images.
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