摘要
在油气勘探开发领域,为充分发挥多波多分量地震勘探的技术优势,需进行多波地震信号的高精度匹配处理。异于传统方法通过改变地震信号的传播时间、相位、频率等特征完成匹配,提出了一种基于深度学习的多波地震信号智能匹配方法,它利用了卷积神经网络(CNN)的特征提取能力,直接提取地震信号的波形特征,并辅以重采样抽取转换波(PS)、纵波(PP)和转换波特征损失加权、Adam梯度下降算法更新PS波特征等,使PS波波形在保持整体不变的前提下,在时间域向PP波逼近。通过PP波与PS波的波形匹配,自动完成多波地震信号的传播时间、相位、频率等动力学、运动学和几何学特征匹配。川西坳陷新场3D3C地震资料的应用表明,该方法在多波地震信号的匹配过程中,不需人工干预,具有高精度、高效率、智能化和自动化等优点,匹配后的PS波在保持原始特性的基础上,主频、频宽、波形等更逼近PP波,能有效地描述地层接触关系,更有利于断层识别、地层追踪、岩性边界刻画等地质解释,为多波地质解释、联合反演等奠定坚实基础。
In the field of oil and gas exploration and development,high-precision matching of multiwave seismic signals needs to be conducted to give full play to the technical advantages of multiwave and multi-component seismic exploration. This research proposes an intelligent matching method based on deep learning for multiwave seismic signals.Different from the traditional method that changes such features of the seismic signal as propagation time,phase,and frequency to complete the matching,this method uses the powerful feature extraction ability of the convolutional neural network(CNN)to directly extract the waveform features of the seismic signal.Moreover,converted wave(PS)extraction by resampling,longitudinal wave(PP)and converted wave feature loss weighting,and the Adam gradient descent algorithm to update PS wave features are also applied so that the waveform of the PS wave approaches that of the PP wave in the time domain with no overall changes.The dynamic,kinematic,and geometric features,such as the propagation time,phase,and frequency,of multiwave seismic signals are matched automatically through the waveform matching between the PP wave and the PS wave.The application of the 3D3Cseismic data from Xinchang in the Western Sichuan Depression shows that this method does not require manual intervention in the matching of multiwave seismic signals and that it has the advantages of high precision,high efficiency,intelligence,and automation.In addition to maintaining its original characteristics,the matched PS wave obtains a dominant frequency,bandwidth,and waveform closer to those of the PP wave.Effectively describing the formation contact relationship and being more conducive to geological interpretations,such as fault identification,formation tracking,and lithological boundary characterization,the proposed method lays a solid foundation for subsequent applications such as multiwave contrast geological interpretation and joint inversion.
作者
凌里杨
徐天吉
冯博
许宏涛
魏水建
LING Liyang;XU Tianji;FENG Bo;XU Hong-tao;WEI Shuijian(School of Computer Science and Engineering(School of Cyberspace Security),University of E-lectronic Science and Technology of China,Cheng-du,Sichuan 611731,China;School of Resources and Environment,Univer-sity of Electronic Science and Technology of Chi-na,Chengdu,Sichuan 611731,China;Yangtze Delta Region Institute of University of Electronic Science and Technology of China,Huzhou,Huzhou,Zhejiang 313000,China;SINOPEC Petroleum Exploration and Produc-tion Research Institute,Beijing 100083,China)
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2022年第4期768-776,I0001,I0002,共11页
Oil Geophysical Prospecting
基金
中国石化“十条龙”重大科技攻关项目“人工智能地震解释技术及软件研发”(P20052-3)
四川省科技厅创新人才计划项目“页岩储层智能化含气性预测方法”(2020JDRC0013)联合资助。
关键词
多波匹配
高精度
智能化
自动化
深度学习
卷积神经网络
multiwave matching
high precision
intelligence
automation
deep learning
convolutional neural network(CNN)
