基于聚类和局部线性回归的初至波自动拾取算法  

First-arrival automatic picking algorithm based on clustering and local linear regression

作　　者：高磊[1] 罗关凤 刘荡 闵帆[1,2] GAO Lei;LUO Guanfeng;LIU Dang;MIN Fan(School of Computer Science,Southwest Petroleum University,Chengdu Sichuan 610500,China;Institute for Artificial Intelligence,Southwest Petroleum University,Chengdu Sichuan 610500,China)

机构地区：[1]西南石油大学计算机科学学院,成都610500 [2]西南石油大学人工智能研究所,成都610500

出　　处：《计算机应用》2022年第2期655-662,共8页journal of Computer Applications

摘　　要：初至波拾取是地震数据处理中的关键步骤,会直接影响动校正、静校正和速度分析等的精度。目前,现有的算法受到背景噪声和复杂近地表条件的影响时拾取精度会降低。基于此,提出基于聚类和局部线性回归的初至波自动拾取算法(FPCL)。该算法由预拾取和微调两个阶段来实现。预拾取阶段先基于k均值(k-means)技术找到初至波簇,再利用基于密度的噪声应用空间聚类(DBSCAN)技术在初至波簇中进行拾取。微调阶段通过局部线性回归补齐缺失值,再利用能量比值最小化技术调整错误值。在两个地震数据集上,将FPCL与改进的能量比(IMER)法相比,准确率分别提升了4.00个百分点和3.50个百分点;与互相关技术(CCT)相比,准确率分别提升了38.00个百分点和10.25个百分点;与基于模糊C均值聚类的微震数据自动时间拾取算法(APF)相比,准确率分别提升了34.50个百分点和3.50个百分点;与基于两阶段优化的初至波自动拾取算法(FPTO)相比,准确率分别提升了5.50个百分点和16.25个百分点。上述实验结果表明FPCL更准确。First-arrival picking is an essential step in seismic data processing,which can directly affect the accuracy of normal moveout correction,static correction and velocity analysis.At present,affected by background noise and complex near-surface conditions,the picking accuracies of the existing methods are reduced.Based on this,a First-arrival automatic Picking algorithm based on Clustering and Local linear regression(FPCL)was proposed.This algorithm was implemented in two stages:pre-picking and fine-tuning.In the pre-picking stage,the k-means technique was firstly used to find first-arrival cluster.Then the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)technique was used to pick first-arrivals from the cluster.In the fine-tuning stage,the local linear regression technique was used to fill in missing values,and the energy ratio minimization technique was used to adjust error values.On two seismic datasets,compared with Improved Modified Energy Ratio(IMER)method,FPCL had the accuracy increased by 4.00 percentage points and 3.50 percentage points respectively;compared with Cross Correlation Technique(CCT),FPCL had the accuracy increased by 38.00 percentage points and 10.25 percentage points respectively;compared with Automatic time Picking for microseismic data based on a Fuzzy C-means clustering algorithm(APF),FPCL had the accuracy increased by 34.50 percentage points and 3.50 percentage points respectively;compared with First-arrival automatic Picking algorithm based on Two-stage Optimization(FPTO),FPCL had the accuracy increased by 5.50 percentage points and 16.25 percentage points respectively.The above experimental results show that FPCL is more accurate.

关 键 词：初至波拾取 K均值聚类 基于密度的噪声应用空间聚类 局部线性回归 能量比值 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]