基于深度学习的地层倾角计算方法研究  

Calculation method of formation dip angle based on deep learning

作　　者：丰超 潘建国[1] 姚清洲[1] 王宏斌[1] 张希晨 Feng Chao;Pan Jian-Guo;Yao Qing-Zhou;Wang Hong-Bin;Zhang Xi-Chen(Northwest Branch of Research Institute of Petroleum Exploration&Development,Lanzhou,Gansu,730020,China)

机构地区：[1]中国石油勘探开发研究院西北分院,兰州730020

出　　处：《Applied Geophysics》2024年第2期303-315,420,共14页应用地球物理（英文版）

基　　金：funded by the CNPC(China National Petroleum Corporation)Scientific Research and Technology Development Project(Grant No.2021DJ05).

摘　　要：地层倾角是反映地下构造的重要特征参数之一,在地震勘探和地质解释中应用广泛。然而常规倾角计算方法存在假象干扰、精度不足等问题,制约了精细勘探技术的发展,如何更加准确求取地层倾角成为普遍关注的问题。针对该问题,本文提出一种基于深度学习的地层倾角计算方法,将倾角计算看作回归问题,通过建立合成地震数据与倾角数据标签库,利用数据驱动拟合地震数据与倾角之间的非线性函数关系,进而实现倾角智能计算。将本方法在合成沉积模型与实际资料分别进行了验证,并与业界主流倾角计算方法效果对比,结果展示出了本方法更加真实的反映构造起伏特征,兼具准确性与抗干扰能力。The formation dip angle is an important characteristic parameter that reflects underground structures,and it is widely used in seismic exploration and geological interpretation.However,the routine dip angle calculation method suff ers from the problems of artifact interference and insufficient accuracy,which restricts the development of fine exploration technology.More accurately obtaining the formation dip angle has become a general concern.To solve these problems,this paper proposes a method for calculating the formation dip angle using deep learning.The dip angle calculation is considered a regression problem.By establishing a database of synthetic seismic data and dip data tags,data-driven fi tting of nonlinear functional relationships between the seismic data and dip angles are achieved,and intelligent dip calculations are realized.The method is verifi ed using the synthetic sedimentary model and actual data and is compared with the mainstream dip angle calculation method in the industry.The results show that this method more realistically refl ects the undulating characteristics of a structure with both high accuracy and anti-interference ability.

关 键 词：卷积神经网络 地层倾角 倾角扫描 平面波分解 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P631.4[自动化与计算机技术—控制科学与工程]