石油物探2025，Vol.64Issue(4)：701-715,15.DOI:10.12431/issn.1000-1441.2024.0028
基于Dix引导的智能速度建模方法
A Dix-guided intelligent velocity modeling method
摘要
Abstract
Velocity modeling is a key challenge in geophysical exploration.Although artificial intelligence methods enable efficient model building,they often lack domain-specific guidance and fail to consider the structural similarity between the migration image and velocity model,resulting in inaccurate geological structure interpretation.To address this limitation,we propose a Dix-guided intelligent velocity modeling method,which uses the structural similarity between imaging profiles and interval velocities as well as the Dix relation to optimize the neural network-based modeling process.Specifically,common midpoint(CMP)gathers and velocity spectra are used as inputs to extract features that reflect relative velocity magnitude and variation trends.Then,the stacked profile is introduced to guide the network to convert there features into interval velocities(hidden layers),which are structurally consistent with the profile.This process is constrained by root-mean-square(RMS)velocity lables and a newly designed Dix activation function.Experimental results demonstrate that the Dix activation function effectively guides the network to incorporate geological structures into velocity modeling,enabling it to learn simple morphological structural mappings and enhancing the model's generalization capability.The predicted velocity model accurately delineates stratigraphic boundaries and exhibits strong lateral continuity.关键词
速度建模/结构相似性/Dix激活函数/叠加剖面/人工智能Key words
velocity modeling/structural similarity/Dix activation function/stacked profile/artificial intelligence分类
能源科技引用本文复制引用
张海风,袁三一,王丹阳,于越,王尚旭..基于Dix引导的智能速度建模方法[J].石油物探,2025,64(4):701-715,15.基金项目
国家重点研发计划(2018YFA0702504)、国家自然科学基金(42174152)、中国石油天然气集团有限公司科技管理部项目(2022DQ0604-01)和中国石油天然气集团有限公司-中国石油大学(北京)战略合作科技专项(ZLZX2020-03)共同资助.This research is financially supported by the National Key Research and Development Program of China(Grant No.2018YFA0702504),the National Natural Science Foundation of China(Grant No.42174152),R&D Department of China National Petroleum Corporation(Grant No.2022DQ0604-01)and the CNPC-CUPB Strategic Cooperation Research Program(Grant No.ZLZX2020-03). （2018YFA0702504）
