Automatic Seismic Salt Interpretation with Deep Convolutional Neural Networks

Abstract

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.

Semi-supervised Segmentation of Salt Bodies in Seismic Images Using an Ensemble of Convolutional Neural Networks

“…A lot of research efforts have been devoted to interpretation of seismic images [41,37,53,47,3]. With the advent of CNNs, several approaches have been proposed for supervised seismic image interpretation using deep learning [9,43,52]. But the small size of the available datasets and lack of the annotations seismic image interpretation did not allow to unfold the full potential of the CNNs.…”

“…The advent of convolutional neural networks (CNNs) brought significant advancements in different problems and several attempts have been made to apply CNNs in the field of seismic imaging [43,11,45,52]. CNNs overcome the need for manual feature design and show superior performance on the tasks of the salt body delineation compared to the methods based on the handcrafted features.…”

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

“…Di, Wang and AlRegib ; Waldeland et al . ; Zeng, Jiang and Chen ; Shi, Wu and Fomel ). However, little work has been done towards an in‐depth comparison of these techniques in seismic saltbody delineation.…”

“…Di, Shafiq and AlRegib 2017;Huang, Dong and Clee 2017;Guitton, Wang and Guitton 2017) and saltbody delineation (e.g. Di, Wang and AlRegib 2018a;Waldeland et al 2018;Zeng, Jiang and Chen 2018;Shi, Wu and Fomel 2019). However, little work has been done towards an in-depth comparison of these techniques in seismic saltbody delineation.…”

Does shallow geological knowledge help neural-networks to predict deep units?

“…During the last few years, new neural network designs have shown that they can provide high-quality interpretations. In particular, U-nets were used for the interpretation of geological units [Peters et al, 2019a,b], horizons [Peters et al, 2018], salt [Zeng et al, 2018], and faults [Wu et al, 2019, Zhang et al, 2019.…”