Successful leveraging of image processing and machine learning in seismic structural interpretation: A review
As a process that identifies geologic structures of interest such as faults, salt domes, or elements of petroleum systems in general, seismic structural interpretation depends heavily on the domain knowledge and experience of interpreters as well as visual cues of geologic structures, such as texture and geometry. With the dramatic increase in size of seismic data acquired for hydrocarbon exploration, structural interpretation has become more time consuming and labor intensive. By treating seismic data as images rather than signal traces, researchers have been able to utilize advanced image processing and machine learning techniques to assist interpretation directly. In this paper, we mainly focus on the interpretation of two important geologic structures, faults and salt domes, and summarize interpretation workflows based on typical or advanced imageprocessing and machine learning algorithms. In recent years, increasing computational power and the massive amount of available data have led to the rise of deep learning. Deep learning models that simulate the human brain's biological neural networks can achieve state of the art accuracy and even exceed human level performance on numerous applications. The convolutional neural network a form of deep learning model that is effective in analyzing visual imagery has been applied in fault and salt dome interpretation. At the end of this review, we provide insight and discussion on the future of structural interpretation. Introduction In general, seismic interpretation acquires subsurface information from seismic data and reveals geologic meanings. As a main category of interpretation workflows, structural interpretation aims to identify important geologic structures in hydrocarbon exploration. Interpretation that depends only on domain experts is becoming increasingly time consuming and labor intensive because seismic surveys have grown over the years in size and complexity. Data sizes have increased from hundreds of megabytes for the first 3D seismic survey collected in the 1970s to hundreds of gigabytes or even terabytes today. In addition, seismic data generated by advanced acquisition systems have a higher resolution and reveal more complicated details of geologic structures in the subsurface, which makes manual interpretation more difficult. To tackle these challenges, researchers have investigated image processing and machine learning algorithms and utilized them to implement automated or semiautomated interpretation workflows. For years, image processing theories and algorithms have been employed to assist structural interpretation and have made essential contributions to the field. Structural interpretation commonly involves two main steps attribute extraction and structure identification. Attributes that capture the key Zhen Wang1, Haibin Di1, Muhammad Amir Shafiq1, Yazeed Alaudah1, and Ghassan AlRegib1 components of seismic data are implicitly or explicitly defined based on signal and image processing techniques. For example, instantaneous attributes (Taner et al. 1979) are derived from the Hilbert transform, and spectral attributes (Sinha et al. 2005) are the results of multiresolution analysis. Since edge, texture, and shape information are important to both images from nature and seismic images, edge detectors (Amin and Deriche, 2015) texture descriptors (Shafiq et al. 2017) and the Hough transform (Wang and AlRegib, 2017) although initially designed for use with natural images, have shown strong capability in identifying geologic structures in seismic images. From another perspective, interpretation as a complex and subjective task can be conducted based on the human visual system. Recently, algorithms involving the human visual system model, such as saliency detection (Shafiq et al. 2018) and color space analysis (Wang et al. 2014) have been proposed to mimic the interpretation process by extracting the most perceptually representative attributes of seismic data. The richness and rapid progress in image processing and computer vision have taken the automation of structural interpretation to a higher level. We believe that seismic interpretation as a challenging problem will continue to impact the advancement of image processing in the future. Although a deep understanding of and training in geophysics have been considered prerequisites for developing effective interpretation workflows, recent progress in machine learning has shed new light on its role in this domain specific problem (Zhao et al. 2015; Huang et al. 2017; AlRegib et al. 2018) Machine learning models trained on input data can produce repeatable, reliable results for seismic interpretation and alleviate two significant issues that interpreters may encounter interpreting large volumes of data and understanding the relationship of various types of data simultaneously. Machine learning based interpretation methods have two main schemes. One extracts multiple seismic attributes based on interpreters' domain knowledge and experience and trains them on conventional supervised or unsupervised machine learning models such as the self organizing map (Strecker and Uden, 2002) the multilayer perceptron (Aminzadeh, 2013; Zheng et al. 2014) and K means clustering (Di and AlRegib, 2017) The other utilizes deep learning models, especially the convolutional neural network (CNN) In recent years, with the dramatic growth of computational power and available data, deep learning that simulates the brain's structures and functions has achieved remarkable success in various artificial intelligence applications. The CNN motivated by the visual cortex in the brain, as one form of deep learning models, consists of one or more convolutional layers. 2D kernels in convolutional layers are trained to capture spatial and structural features from input images, which makes the CNN better suited for visual 1Georgia Institute of Technology, Center for Energy and Geo Processing, Atlanta, Georgia, USA. E mail: zwang313@gatech.edu; haibin.di@ece. gatech.edu; amirshafiq@gatech.edu; alaudah@gatech.edu; alregib@gatech.edu. https:/doi.org/10.1190/tle37060451.1. D ow nl oa de d 06 /1 3/ 18 to 1 63 .1 85 .1 48 .2 45 R ed is tr ib ut io n su bj ec t t o SE G li ce ns e or c op yr ig ht s ee T er m s of U se a t h ttp lib ra ry .s eg .o rg