Article(id=1322506484601180762, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506406058635943, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.10.480, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1729440000000, receivedDateStr=2024-10-21, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793239716910, onlineDateStr=2026-10-29, pubDate=1763136000000, pubDateStr=2025-11-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793239716910, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793239716910, creator=13701087609, updateTime=1793239716910, updator=13701087609, issue=Issue{id=1322506406058635943, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='11', pageStart='1101', pageEnd='1210', issueExtLink='null', onlineDate='null', pubDate='1763136000000', pubDateStr='2025-11-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793239698184, creator='13701087609', updateTime=1793239698184, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1155, endPage=1162, ext={EN=ArticleExt(id=1322506484802507355, articleId=1322506484601180762, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Comparison of Optimization Methods and Line Search Methods in Joint Tomography of First-Arrival and Reflected Waves, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

We use the joint tomography of first-arrival and reflected waves based on the adjoint techniques and conventional optimization methods to compare the combination methods of three commonly used optimization methods (CG, CGDESCENT, L-BFGS) and line search methods (LNS and CWI), and test the six combination algorithms using anomaly body model and chessboard model. The results show that: 1) For high-speed mutation models, CGDESCENT+LNS has slightly higher accuracy than L-BFGS+CWI, but CGDESCENT did not converge in low-speed models, while both L-BFGS+LNS and L-BFGS+CWI perform well. CGDESCENT is more suitable for large-scale models. 2) Under the same conditions, the accuracy of CWI is slightly higher than that of LNS, but CWI is prone to local minimum and is very unstable.

, authors=Yuntao ZHANG1, 2, Jishun PAN2, *, Yonghong DUAN1, authorsList=Yuntao ZHANG, Jishun PAN, Yonghong DUAN, authorCompany=null, correspAuthors=Jishun PAN, authorNote=

ZHANG Yuntao, assistant engineer, majors in seismic tomography, E-mail:

, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322506489214915183, articleId=1322506484601180762, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=初至波与反射波联合层析成像中的优化方法与线搜索方法对比, columnId=1322506193558422163, journalTitle=大地测量与地球动力学, columnName=地震地质, runingTitle=null, highlight=null, articleAbstract=

基于伴随技术与常规优化方法联合的初至波与反射波联合层析成像, 对比CG法、CGDESCENT法、L-BFGS法3种常用的优化方法与LNS法和CWI法2种线搜索方法的组合方法, 并采用异常体模型和棋盘模型对6种组合算法进行测试。结果表明: 1)在高速突变模型时, CGDESCENT+LNS法的精度略高于L-BFGS+CWI法, 但在低速模型时, CGDESCENT法未收敛, L-BFGS+LNS法和L-BFGS+CWI法都有较好的表现, CGDESCENT法更适用于大规模模型; 2)在相同条件下, CWI法的精度比LNS法高, 但易陷入局部极小值, 非常不稳定。

, authors=张允涛1, 2, 潘纪顺2, *, 段永红1, authorsList=张允涛, 潘纪顺, 段永红, authorCompany=null, correspAuthors=潘纪顺, authorNote=

张允涛, 助理工程师, 主要研究方向为地震层析成像, E-mail:

, correspAuthorsNote=

潘纪顺, 博士, 教授, 主要研究方向为地震波成像与反演, E-mail:

PAN Jishun, PhD, professor, majors in seismic wave imaging and inversion, E-mail:

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CGDESCENT+CWI法未收敛,故用空图代替

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CGDESCENT+LNS法与CGDESCENT+CWI法均未收敛,故用空图代替

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CGDESCENT+CWI法未收敛,故用空图代替

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CGDESCENT+CWI法未收敛,故用空图代替

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初至波与反射波联合层析成像中的优化方法与线搜索方法对比
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张允涛 1, 2 , 潘纪顺 2, * , 段永红 1
大地测量与地球动力学 | 地震地质 2025,45(11): 1155-1162
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大地测量与地球动力学 |地震地质 2025 , 45 (11) : 1155 -1162
初至波与反射波联合层析成像中的优化方法与线搜索方法对比
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张允涛1, 2 , 潘纪顺2, * , 段永红1
作者信息
  • 1 中国地震局地球物理勘探中心, 郑州市文化路75号, 450002
  • 2 华北水利水电大学地球科学与工程学院, 郑州市金水东路136号, 450046
通讯作者:

潘纪顺, 博士, 教授, 主要研究方向为地震波成像与反演, E-mail:

PAN Jishun, PhD, professor, majors in seismic wave imaging and inversion, E-mail:

作者简介:

张允涛, 助理工程师, 主要研究方向为地震层析成像, E-mail:

Comparison of Optimization Methods and Line Search Methods in Joint Tomography of First-Arrival and Reflected Waves
Yuntao ZHANG1, 2 , Jishun PAN2, * , Yonghong DUAN1
Affiliations
  • 1 Geophysical Exploration Center, CEA, 75 Wenhua Road, Zhengzhou 450002, China
  • 2 College of Geosciences and Engineering, North China University of Water Resources and Electric Power, 136 East-Jinshui Road, Zhengzhou 450046, China
  • ZHANG Yuntao, assistant engineer, majors in seismic tomography, E-mail:

出版时间: 2025-11-15 doi: 10.14075/j.jgg.2024.10.480
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基于伴随技术与常规优化方法联合的初至波与反射波联合层析成像, 对比CG法、CGDESCENT法、L-BFGS法3种常用的优化方法与LNS法和CWI法2种线搜索方法的组合方法, 并采用异常体模型和棋盘模型对6种组合算法进行测试。结果表明: 1)在高速突变模型时, CGDESCENT+LNS法的精度略高于L-BFGS+CWI法, 但在低速模型时, CGDESCENT法未收敛, L-BFGS+LNS法和L-BFGS+CWI法都有较好的表现, CGDESCENT法更适用于大规模模型; 2)在相同条件下, CWI法的精度比LNS法高, 但易陷入局部极小值, 非常不稳定。

联合层析成像  /  优化算法  /  伴随方法  /  线搜索  /  初至波

We use the joint tomography of first-arrival and reflected waves based on the adjoint techniques and conventional optimization methods to compare the combination methods of three commonly used optimization methods (CG, CGDESCENT, L-BFGS) and line search methods (LNS and CWI), and test the six combination algorithms using anomaly body model and chessboard model. The results show that: 1) For high-speed mutation models, CGDESCENT+LNS has slightly higher accuracy than L-BFGS+CWI, but CGDESCENT did not converge in low-speed models, while both L-BFGS+LNS and L-BFGS+CWI perform well. CGDESCENT is more suitable for large-scale models. 2) Under the same conditions, the accuracy of CWI is slightly higher than that of LNS, but CWI is prone to local minimum and is very unstable.

joint tomography  /  optimization algorithm  /  adjoint method  /  line search  /  first-arrival waves
张允涛, 潘纪顺, 段永红. 初至波与反射波联合层析成像中的优化方法与线搜索方法对比. 大地测量与地球动力学, 2025 , 45 (11) : 1155 -1162 . DOI: 10.14075/j.jgg.2024.10.480
Yuntao ZHANG, Jishun PAN, Yonghong DUAN. Comparison of Optimization Methods and Line Search Methods in Joint Tomography of First-Arrival and Reflected Waves[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (11) : 1155 -1162 . DOI: 10.14075/j.jgg.2024.10.480
精确获取地下速度结构是地震勘探研究的核心目标之一[1]。地震层析成像技术通过数值计算将地震波场数据转化为速度模型[2],基于此已发展出多种地震波旅行时反演方法[3-11],但传统单波型层析方法(如初至波或反射波层析)因受波场特性限制,难以同时实现速度结构重建与地层界面约束的协同优化[12-15]。现有层析算法虽种类多样,但其适用场景存在显著差异[16-17]。研究表明,初至波-反射波联合层析成像通过融合多源波场信息,可有效克服单一波型成像的局限性[7]。利用初至波高精度走时约束浅层速度,结合反射波走时拓展深层反演能力,可显著降低反演多解性,并提升垂向分辨率[18-19]。初至波走时虽具有易识别、高精度特性[20],但其远炮检距数据的可靠性随偏移距增大而降低;反射波虽能有效构建深层结构,但对浅层异常体的分辨能力弱于折射波。该特性差异决定了联合反演中需针对性地优化数据权重分配策略。
目前走时层析成像技术中常用的优化方法有共轭梯度法(CG法)[21]、共轭梯度法-最速下降法(CGDESCENT法)[22]和有限内存拟牛顿法(L-BFGS法)[23],而常用的确定步长方法为信赖域方法[24]和线搜索方法[25],其中线搜索方法又分为精确线搜索和非精确线搜索[26]。精确线搜索旨在找到使目标函数在当前搜索方向上最优的步长;非精确线搜索通过引入一定的误差容限,减少计算量,达到较快的收敛效果[27]。不同的非精确线搜索方法会影响反演结果和收敛速度[28],方法不合适将陷入局部极小值收敛。对于线搜索方法,最常用的为切线法[29]和三次样条插值法[30],对于每次迭代步长的确定,常用的限制条件为Wolfe条件[31],即满足Armijo条件和curvature条件[32]。Armijo条件要求搜索时步长要有足够的下降;curvature条件则是避免下降得太小,即梯度方向的改变不能小于梯度的模与常数的乘积。但Wolfe条件无法应对梯度方向过大的情况,强Wolfe条件则更为严苛[33],在满足Armijo条件和curvature条件的基础上,增加不能让梯度方向改变太大的条件,不能过于远离极值的限制。在常规的牛顿法走时层析成像中需要求解Frechet导数矩阵,但该方法计算量大且耗时,针对该问题,Lions[34]于1971年首次在控制理论中提出伴随技术,随后将其引入到地球物理领域,并得到广泛应用[35-36]。
本文使用CG法、CGDESCENT法和L-BFGS法3种优化算法与强Wolfe条件下的切线法(LNS法)和三次样条插值法(CWI法)的组合算法,通过异常体模型和高、低速棋盘梯度模型,从多个角度分析各组合方法的优劣。
文献[7]已验证相关联合层析成像程序的有效性,基于此,本文对梯度介质下的高、低速度模型进行数值模拟,在相同初始模型和观测系统条件下,将3种优化算法和2种线搜索方法进行组合并应用于层析成像,从多个角度分析各组合方法的优劣。
实验分别在横向1 875 m、纵向384 m和横向3 600 m、纵向896 m处设置相对高速体和相对低速体,异常范围为±500 m/s。为模拟更真实的地下介质,速度模型采用梯度均质联合模型,即0~640 m为梯度模型,速度范围为2 000~3 500 m/s;640~1 280 m为均质模型,速度为3 500 m/s。高、低速体用来模拟断层出现时的局部破碎(低速体)和局部挤压(高速体)。模型采用纵横向160×600网格,纵向网格间距8 m,横向网格间距12.5 m,沿横向均匀布置20个震源、600个检波器,道间距12.5 m,并将炮点和检波器按正弦函数均匀布设,以模拟真实的地表起伏。对于权重因子,从浅到深初至波的权重逐渐减小,反射波的权重逐渐增大,以直观地看出各组合方法的优劣。
层析成像结果显示:1)使用LNS法时(图 1(d)、1(e)和1(f)),3种方法的反演精度都较高。从384 m和896 m深度的单道对比结果来看(图 2(a)和2(b)),与真实模型最接近的是L-BFGS法,CGDESCENT法次之,CG法与真实模型偏离最大。但根据残差对比结果(图 2(c)和2(d))可知,CGDESCENT+LNS法的总体误差最小,L-BFGS+LNS法次之,CG+LNS法最差。2)使用CWI法时(图 1(g)和1(i)),由于CGDESCENT法未收敛,判定为反演变化不剧烈的模型时效果最差;虽然L-BFGS法的精度比CG法高,但根据L-BFGS法在迭代第5次时出现残差和误差均增大的情况,推测为都陷入局部最小值,导致算法一直在最优点附近徘徊,无法找到最优解。3)根据迭代时间可知,在使用LNS法时,3种优化方法在迭代第8次前CG法收敛最快,L-BFGS法次之,CGDESCENT法最慢,但在迭代至第10次以后,CGDESCENT法的精度比其他方法略高;由于使用CWI法时出现局部最小值,在第5次迭代时基本都停止下降,不具有比较性,本文不作赘述。
棋盘模型是一种用于研究空间分辨率和成像算法性能的理想化模型。模型采用纵横向160×600网格,从浅到深采用梯度模型,设置梯度模型为初始模型(图 3(b)),速度范围为2 000~3 300 m/s。棋盘网格大小为纵横向320 m×250 m。低速与高速交叉并行,异常范围为±500 m/s。反射层设置为4层,分别为320 m、640 m、960 m和1 280 m,反射界面与波传播方向正交。炮数为100炮,接收道数为600道,道间距为8 m。
从层析成像结果可知:
1) 在使用CG法时(图 3(d)和3(g)),无论是在恢复深度模型还是成像精度方面,使用CWI法时均出现局部极小值,LNS法表现出较高精度;在使用L-BFGS法时(图 3(f)和3(i)),LNS法和CWI法反演结果基本一致。根据速度对比结果(图 4(a)和4(b))、全局误差对比结果(图 4(d))可知,LNS法效果比CWI法稍好。
2) 在使用LNS法时(图 3(d)和3(f)),CG法与L-BFGS法都可以较好地反映棋盘的首层异常,但CG法在3 000 m附近的地表处表现不稳定;对于棋盘模型第2层,L-BFGS法能更平滑地恢复夹在低速异常中的高速异常,但精度相对略低;在使用CWI法(图 3(g)和3(i))时,很明显L-BFGS法完全压制CG法,CG法不仅浅层恢复精度比L-BFGS法低,且在深层出现模糊现象,由图 4(a)和4(c)可知,在反演棋盘模型第2层时陷入局部极小值。
3) 根据2个单道对比结果(图 4(a)和4(b))可知,在棋盘模型首层,L-BFGS+LNS法效果最接近真实模型(虚线),CG+CWI法最差;在棋盘模型第2层,所有组合算法都无法完整地恢复低速体模型,这主要是受偏移距和深度扰动影响,对于恢复高速体的情况,L-BFGS+CWI法最接近真实模型,L-BFGS+LNS法次之,CG+LNS法最差。CG+CWI法出现极不稳定的情况,很容易陷入局部极小值。根据平均残差和全局误差结果(图 4(c)和4(d))可知,迭代至8次后趋于稳定,L-BFGS+LNS法最接近真实模型,L-BFGS+CWI法次之,CG+LNS法略差。
在低速情况下,CGDESCENT法无论是结合LNS法或CWI法都出现迭代500次还未收敛的情况,但当其他条件不变时,将速度梯度改为3 000~4 300 m/s时,根据层析成像结果(图 5)可知,CGDESCENT+LNS法在该速度范围内表现出较高的精度,但CGDESCENT+CWI法仍未收敛。
根据单道对比结果(图 6(a)和6(b))可知,虽然除CG+CWI法以外的组合算法对浅层异常都有较好的效果,但L-BFGS+LNS法存在明显的过拟合现象;对于深层的高速异常,L-BFGS+LNS法与CGDESCENT+LNS法都存在过拟合现象,但对于深层的低速异常,CGDESCENT+LNS法略差于L-BFGS+LNS法。根据平均残差对比结果(图 6(c))可知,CGDESCENT+LNS法总体比其他组合较好,L-BFGS+CWI法次之,CG+LNS法最差。
本次实测数据采用呼包盆地的深反射数据,该地区第四系发育齐全,岩相变化频繁,由北向南沉积相由山麓洪积相过渡为河湖相,各沉积相之间呈交错渐变关系。盆地基底受断裂活动(乌拉山山前断裂、大青山山前断裂)控制,总体呈北深南浅的箕状构造特征,沉降中心位于盆地北侧,盆地边缘活动断裂标志明显。
根据酆少英等[37]得到的呼包盆地岩石圈平均速度和叠加速度剖面,建立横向范围为28.92~66 km的初始模型(图 7),同时建立TQ、TN、TE和Tg四条反射剖面。
联合层析成像对比结果(图 8)显示,CGDESCENT+CWI法仍未收敛,其余组合算法除CG+CWI法未反演出速度差异外,反演结果相差不大,都显示在约52 km处存在速度突变,对比叠加剖面图[37]判断该处为断层面,表明结果具有可靠性。但细微处仍存在差异,如在使用CWI法时,第2层处CG法、CGDESCENT法和L-BFGS法的速度更新量依次增大。
由1.5 km深度单道对比结果(图 9(a))可以看出,其余组合都呈现出马鞍型,唯有CGDESCENT+LNS法呈抛物线型,表明该算法较稳定。沿测线47 km的单道对比结果(图 9(b))显示,各种算法的速度曲线和数值基本一致。
根据平均残差(图 9(c))和全局误差对比结果(图 9(d))可知,L-BFGS+CWI法残差最小,CGDESCENT+LNS法次之。虽然CG+LNS法的全局误差最小,但该组合算法在局部的反演结果不尽人意。
本文使用CG+LNS、CG+CWI、CGDESCENT+LNS、CGDESCENT+CWI、L-BFGS+LNS、L-BFGS+CWI等组合算法反演异常体模型和高、低速棋盘梯度模型,得到以下结论:
1) 通过研究梯度加均质速度模型下的高低速异常体模型发现,CGDESCENT+LNS法整体效果最好,L-BFGS+LNS法局部效果最好,CG+LNS法效果最差。
2) 对于低速状态的棋盘模型,L-BFGS+LNS法整体误差最小,L-BFGS+CWI法次之,CG+LNS法效果略差;对于浅层部位,L-BFGS+LNS法可以更加平滑地恢复异常,精度比L-BFGS+CWI法略高,CG+LNS法则容易出现干扰,且精度较低;对于深层部位,L-BFGS+LNS法和L-BFGS+CWI法都可以较为准确地恢复高速异常,但对低速异常不敏感。从浅层到深层,CG+CWI法效果都最差。
3) 对于高速状态的棋盘模型,CGDESCENT+LNS法精度较高,L-BFGS+CWI法次之,CG+LNS法效果略差。
4) 在实测数据对比中,L-BFGS+CWI法精度最高,CGDESCENT+LNS法精度较高和稳定性最好,L-BFGS+LNS法表现最差。
5) 需要特别说明的是,CGDESCENT+CWI组合算法从始至终都因为FSM迭代次数超过500次而停止收敛。此外,所有优化方法在使用CWI法时易陷入局部最小值。
本文主要对比现有的成熟算法在应对不同地质模型的表现,在未来研究中将引用更多新算法应对复杂地质模型的鲁棒性,特别是在非线性问题中避免陷入局部最小值。未来的成像领域一定是更多尺度和更多维度的联合反演,如使用重力勘探进行密度约束,再对速度、密度等参数信息进行联合反演。
  • 国家自然科学基金(42374073)
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doi: 10.14075/j.jgg.2024.10.480
  • 接收时间:2024-10-21
  • 首发时间:2026-10-29
  • 出版时间:2025-11-15
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  • 收稿日期:2024-10-21
基金
National Natural Science Foundation of China(42374073)
国家自然科学基金(42374073)
作者信息
    1 中国地震局地球物理勘探中心, 郑州市文化路75号, 450002
    2 华北水利水电大学地球科学与工程学院, 郑州市金水东路136号, 450046

通讯作者:

潘纪顺, 博士, 教授, 主要研究方向为地震波成像与反演, E-mail:

PAN Jishun, PhD, professor, majors in seismic wave imaging and inversion, E-mail:

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2种不同金属材料的力学参数
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属数
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genus
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species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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