2024年5月19日 星期日
基于深度学习的地震速度反演方法研究
Research on Seismic Velocity Inversion Method Based on Deep Learning
摘要

获得地震速度的方法包括偏移速度分析、层析速度反演和全波形反演, 但这些方法都有共同的问题: 随着地震数据量的增大, 处理数据获得地震速度的时间成倍增加, 且后两种方法对初始地震速度依赖性较强。针对以上方法所存在的问题, 本文改进了一种基于深度学习的地震速度反演方法。同时提出了一种随机生成大量速度模型的方法, 该方法生成的速度模型拥有与真实地下构造相近的地质特征 (起伏层、断层、异常体等) 。利用生成的速度模型和波动方程进行正演, 可以实现数据集的高效建立。本文改进的深度学习反演方法基本原理为: 通过卷积神经网络提取训练数据的特征信息, 经过大数据训练, 获得地震炮记录和地震速度的非线性映射关系。在反演阶段, 将地震炮记录输入训练好的网络, 可以快速地反演出地震速度。为了使该网络充分发挥处理地震数据的优势, 作者通过数值模拟的方式获得了优势网络结构, 并取得了令人满意的反演结果, 最后本文通过对比实验, 验证了该方法的优势和适用性。

Abstract

Methods for obtaining seismic velocity include offset velocity analysis, tomography velocity inversion and full waveform. inversion, but these methods all share common problems: As the amount of seismic data increases, the time required to process the data to obtain the seismic velocity increases exponentially, and the latter two methods are more dependent on the initial seismic velocity. To address the problems of the above methods, this paper improves a seismic velocity inversion method based on deep learning. At the same time, a method that can generate a large number of velocity models randomly with geological features (undulation layer, faults, anomalies, etc. ) similar to those of real subsurface structures is also proposed. The generated velocity model and fluctuation equation for forward modeling are used to perform. the forward modeling, which allows for the efficient establishment of data set. The basic principle of the improved deep learning inversion method in this paper is as follows: the characteristic information of the training data is extracted by convolution neural network, which is trained with large data to obtain a nonlinear mapping relationship between seismic record and seismic velocity. In the inversion stage, the seismic velocity can be inversed quickly by inputting the seismic records into the trained network. In order to make the network give full play to the advantages of processing seismic data, the authors obtained the dominant network structure by means of numerical simulation, and achieved satisfactory inversion results. Finally, through comparative experiments, this paper verifies the advantages and applicability of the method.  

DOIDOI:10.48014/cpngr.20230425002
文章类型研究性论文
收稿日期2023-04-25
接收日期2023-05-20
出版日期2023-06-28
关键词地震速度, 深度学习, 卷积神经网络, 数据集, 反演问题
KeywordsSeismic velocity, deep learning, convolutional neural networks, data set, inversion problems
作者许旺1, 王非翊2,*, 张志强1, 刘恭利1, 段新意1
AuthorXU Wang1, WANG Feiyi2,*, ZHANG Zhiqiang1, LIU Gongli1, DUAN Xinyi1
所在单位1. 中海石油 (中国) 有限公司天津分公司, 天津 300459
2. 北京大学地球与空间科学学院, 北京 100871
Company1. China National Offshore Oil (China) Corporation, Tianjin Branch, Tianjin 300459, China
2. School of Earth and Space Sciences, Peking University, Beijing 100871, China
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引用本文许旺, 王非翊, 张志强, 等. 基于深度学习的地震速度反演方法研究[J]. 中国石油天然气研究, 2023, 2(2): 15-24.
CitationXU Wang, WANG Feiyi, ZHANG zhiqiang, et al. Research on seismic velocity inversion method based on deep learning[J]. Chinese Petroleum and Natural Gas Research, 2023, 2(2): 15-24.