基于深度学习的高效构造解释技术研发及工业化应用

Research and industrial application of efficient structural interpretation technology based on deep learning

深度学习为传统地震资料解释技术的发展带来了强劲动力,由此催生了大量的智能化解释技术。但目前的研究成果很少能实现规模化生产应用。为此,面向中、低信噪比地震资料,着力基于深度学习的智能化解释技术工业化落地。在开发智能化软件研发平台的基础上,形成了具有较强资料适应能力的智能化层位解释与断层检测技术,在大规模连片资料层位解释、复杂断块精细描述中发挥了重要作用。与传统的自动解释技术相比,该技术的层位解释效率提高8~20倍,断层识别精度相对于相干、曲率属性等得到显著提高。目前已能完全取代原有的自动解释功能,基本实现了构造解释的智能化转型。

Deep learning technology has given strong impetus to the development of interpretation technology for traditional seismic data,which has spawned a large number of intelligent interpretation technologies.However,limited research achievements can be applied in large-scale production.This study focuses on the industrial implementation of intelligent interpretation technologies based on deep learning for low-to-medium signalto-noise ratio(SNR)data.Upon the development of the intelligent software development platform,an intelligent horizon interpretation and fault detection technology with strong data adaptability is formed,which plays an important role in the horizon interpretation of large-scale continuous survey data and the fine description of complex fault blocks.The efficiency of horizon interpretation by this method is increased by a factor of 9-21compared with that of the traditional automatic interpretation techniques,and the accuracy of fault identification is significantly improved in comparison with that of classic techniques such as coherence and curvature.It can completely replace the original automatic interpretation modules and achieve the intelligent transformation of structural interpretation.