Ultrasonic Logging Crack Image Inpainting Method Based On Generative Adversarial Networks

With the rapid development of the world economy,the energy consumption represented by oil is increasing day by day,driving the prosperity of the oil mining industry.The automation and intelligence of oil exploitation has become an urgent demand.In oil drilling engineering,the acquisition,survey and evaluation of downhole information is a key link.Due to the complex and changeable underground environment,bad conditions and many interferences,the underground information obtained by the information acquisition equipment is extremely easy to produce problems such as signal distortion and information loss,which greatly hinders the correct evaluation of the underground environment.Taking ultrasonic imaging logging as an example,when evaluating the condition of wellbore cracks,the acquired crack image information has obvious faults and information missing due to the widespread existence of signal interference.How to solve the problem of missing information automatically and accurately is an important research topic of ultrasonic imaging logging.With the development of artificial intelligence technology,digital image processing methods have been greatly developed.The image inpainting technology represented by artificial neural network has made great progress.Among them,the representative is the deep learning method,which takes the deep artificial neural network as the model to model and optimize the image inpainting problem,showing a strong ability to model and solve complex systems,adaptability and robustness.At the same time,the deep learning method can also pre-train the model with a huge data set as the knowledge base to achieve better versatility.The image inpainting method based on deep learning can not only repair the image texture,but also achieve the semantic level repair on the image with rich content,which has great application potential.Based on the deep learning image inpainting method,this paper proposes a method of ultrasonic logging fracture image inpainting based on the generative adversarial networks,which provides a new solution to the problem of ultrasonic imaging logging crack image inpainting.This paper first introduces the ultrasonic imaging logging technology and deep learning image processing methods,then introduces the preprocessing methods suitable for the characteristics of the ultrasonic imaging logging images,and proposes the data expansion methods based on style transfer and the data generation method based on the denoising diffusion probabilistic models for the scarcity of the ultrasonic imaging logging image data.Then,a suitable image repair model and algorithm is designed according to the characteristics of the logging crack images,that is,the image inpainting method of ultrasonic imaging logging crack images based on generative adversarial networks.The experimental results show that the method proposed in this paper can achieve high quality inpainting results of ultrasonic imaging logging crack images.