Run Jiang, Xiaodong Sun, ZhenChun Li, DongDong Peng, Liang Zhao
{"title":"一种多信息组合卷积神经网络速度谱自动采摘方法","authors":"Run Jiang, Xiaodong Sun, ZhenChun Li, DongDong Peng, Liang Zhao","doi":"10.1093/jge/gxad090","DOIUrl":null,"url":null,"abstract":"Seismic velocity is a critical parameter in seismic exploration, and its accuracy significantly impacts the reliability of data processing and interpretation results. However, manual velocity picking methods are not only inefficient but also time-consuming, making them increasingly inadequate for meeting the demands of practical production work. This paper introduces the Multi-Information Combination Convolutional Neural Network (MCCN) velocity auto-picking method. Building upon the foundation of convolutional neural networks, we have designed the network structure of the MCCN method specifically tailored to the characteristics of stacked velocity picking tasks. Given that velocity spectrum energy clusters exhibit both morphological and trend features, we employs a regression convolutional neural network to enhance the accuracy of velocity picking. Furthermore, as the velocity spectrum contains interference from multiple waves and other noise, we employ a coordinate attention mechanism to mitigate the influence of interfering information. Our approach involves the simultaneous incorporation of velocity spectrum and CMP information through a dual-combination network, thereby further enhancing velocity picking accuracy. Finally, we compare our method with fully connected convolutional neural networks and manual velocity picking methods, demonstrating the practicality and precision of our proposed approach.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-information combined convolutional neural network velocity spectrum automatic picking method\",\"authors\":\"Run Jiang, Xiaodong Sun, ZhenChun Li, DongDong Peng, Liang Zhao\",\"doi\":\"10.1093/jge/gxad090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Seismic velocity is a critical parameter in seismic exploration, and its accuracy significantly impacts the reliability of data processing and interpretation results. However, manual velocity picking methods are not only inefficient but also time-consuming, making them increasingly inadequate for meeting the demands of practical production work. This paper introduces the Multi-Information Combination Convolutional Neural Network (MCCN) velocity auto-picking method. Building upon the foundation of convolutional neural networks, we have designed the network structure of the MCCN method specifically tailored to the characteristics of stacked velocity picking tasks. Given that velocity spectrum energy clusters exhibit both morphological and trend features, we employs a regression convolutional neural network to enhance the accuracy of velocity picking. Furthermore, as the velocity spectrum contains interference from multiple waves and other noise, we employ a coordinate attention mechanism to mitigate the influence of interfering information. Our approach involves the simultaneous incorporation of velocity spectrum and CMP information through a dual-combination network, thereby further enhancing velocity picking accuracy. Finally, we compare our method with fully connected convolutional neural networks and manual velocity picking methods, demonstrating the practicality and precision of our proposed approach.\",\"PeriodicalId\":54820,\"journal\":{\"name\":\"Journal of Geophysics and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Geophysics and Engineering\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1093/jge/gxad090\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysics and Engineering","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1093/jge/gxad090","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
Seismic velocity is a critical parameter in seismic exploration, and its accuracy significantly impacts the reliability of data processing and interpretation results. However, manual velocity picking methods are not only inefficient but also time-consuming, making them increasingly inadequate for meeting the demands of practical production work. This paper introduces the Multi-Information Combination Convolutional Neural Network (MCCN) velocity auto-picking method. Building upon the foundation of convolutional neural networks, we have designed the network structure of the MCCN method specifically tailored to the characteristics of stacked velocity picking tasks. Given that velocity spectrum energy clusters exhibit both morphological and trend features, we employs a regression convolutional neural network to enhance the accuracy of velocity picking. Furthermore, as the velocity spectrum contains interference from multiple waves and other noise, we employ a coordinate attention mechanism to mitigate the influence of interfering information. Our approach involves the simultaneous incorporation of velocity spectrum and CMP information through a dual-combination network, thereby further enhancing velocity picking accuracy. Finally, we compare our method with fully connected convolutional neural networks and manual velocity picking methods, demonstrating the practicality and precision of our proposed approach.
期刊介绍:
Journal of Geophysics and Engineering aims to promote research and developments in geophysics and related areas of engineering. It has a predominantly applied science and engineering focus, but solicits and accepts high-quality contributions in all earth-physics disciplines, including geodynamics, natural and controlled-source seismology, oil, gas and mineral exploration, petrophysics and reservoir geophysics. The journal covers those aspects of engineering that are closely related to geophysics, or on the targets and problems that geophysics addresses. Typically, this is engineering focused on the subsurface, particularly petroleum engineering, rock mechanics, geophysical software engineering, drilling technology, remote sensing, instrumentation and sensor design.