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Article(id=1281933713533538614, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1281909275651969257, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2025.08.281, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1755446400000, receivedDateStr=2025-08-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1783566414483, onlineDateStr=2026-07-09, pubDate=1781452800000, pubDateStr=2026-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783566414483, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783566414483, creator=13701087609, updateTime=1783566414483, updator=13701087609, issue=Issue{id=1281909275651969257, tenantId=1146029695717560320, journalId=1281212831689347082, year='2026', volume='46', issue='6', pageStart='662', pageEnd='789', issueExtLink='null', onlineDate='null', pubDate='1781452800000', pubDateStr='2026-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1783560588038, creator='13701087609', updateTime=1783566454347, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1281933881221812905, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1281909275651969257, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1281933881221812906, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1281909275651969257, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=718, endPage=727, ext={EN=ArticleExt(id=1281933713734865207, articleId=1281933713533538614, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Tomography of the Luxi Uplift Based on the Deep Learning Dataset, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

A high-precision seismic dataset was constructed using deep learning methods, and a regional three-dimensional velocity model was obtained through double-difference tomography. The study reveals a correlation between surface velocity structures and geological features: mountainous areas generally exhibit high-velocity P-wave anomalies, whereas depression zones are characterized by low-velocity P-wave anomalies. This pattern may be attributed to the widespread presence of bedrock with relatively high wave velocity in folded regions, in contrast to the Quaternary sedimentary layers with lower velocity in depression areas. Seismic epicenters are densely distributed along the transition zones between high- and low-velocity anomalies. These transitional regions experience significant stress gradients, which facilitate rock fracturing and lead to heightened seismic activity.The Luxi uplift is characterized by several near-vertical high- and low-velocity blocks, with some earthquake epicenters also displaying steeply dipping distributions, suggesting the presence of high-angle faults. This structural configuration may result from multiple phases of compression and extension under the influence of Pacific Plate subduction and the tectonic activity of the Tan-Lu fault zone. These processes have led to cyclic uplift and subsidence in the region, during which progressive compression and extension of rock folds gradually increased their dip angles, forming near-vertical velocity blocks and high-angle faults.Integrated analysis of P- and S-wave velocities suggests the presence of mantle-derived basaltic magmatic intrusions beneath both the Nishan area and the northeastern region of the Lushan. In the Nishan area, the intrusions are predominantly plutonic, whereas in northeastern Lushan, mantle-derived basaltic magma likely reached the surface, forming ancient volcanoes and extrusive rocks.

, authors=Xin CUI1, Tengchao DONG1, Chunpeng ZHANG1, authorsList=Xin CUI, Tengchao DONG, Chunpeng ZHANG, authorCompany=null, correspAuthors=null, authorNote=null, 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=1281933716683460932, articleId=1281933713533538614, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=基于深度学习数据集的鲁西隆起区域层析成像, columnId=1281933709121138897, journalTitle=大地测量与地球动力学, columnName=地球动力学, runingTitle=null, highlight=null, articleAbstract=

采用深度学习方法构建一个高精度地震数据集, 基于双差层析成像方法反演得到区域三维速度模型。研究表明, 地表速度结构与地质构造之间存在联系, 即山脉区多呈现P波高速异常, 而坳陷区多呈P波低速异常。这种特征可能源于, 褶皱区地表多为基岩, 波速相对高, 而坳陷区地表多为第四系土层, 波速相对低。地震震中密集分布在高速和低速异常的过渡区域。高速和低速过渡区域应力梯度变化显著, 更易发生破裂而导致地震活动性强。鲁西隆起区存在一些近垂直的高速和低速块体, 部分震中也呈高倾角分布, 推测为高倾角断层。这可能源于, 在太平洋板块俯冲和郯庐断裂带运动影响下, 鲁西隆起经历多个挤压和伸展的构造作用过程, 形成多个隆起和坳陷的周期, 期间岩石褶皱不断挤压和伸展, 褶皱的倾角逐渐升高, 形成近垂直的速度块体和高倾角断层。通过P波与S波速度联合讨论, 推测尼山和鲁山东北区域均存在地幔玄武质岩浆的侵入, 尼山地区以侵入岩为主, 而鲁山东北区域则存在地幔玄武岩浆突破地表, 形成古火山与喷出岩。

, authors=崔鑫1, 董腾超1, 张春鹏1, authorsList=崔鑫, 董腾超, 张春鹏, authorCompany=null, correspAuthors=null, authorNote=

崔鑫, 博士, 工程师, 主要研究方向为区域精细构造及层析成像, E-mail:

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崔鑫, 博士, 工程师, 主要研究方向为区域精细构造及层析成像, E-mail:

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(b)~(g)上方子图为剖面区域高程，第二子图为P波速度分布，第三子图为S波速度分布，最下子图为波速比分布

, figureFileSmall=Vp7iiGiar7UgraGfYCzalA==, figureFileBig=/QJgSj9PNcCt2f+IQ9KqbA==, tableContent=null), ArticleFig(id=1281933721624351087, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1281933713533538614, language=EN, label=Tab. 1, caption=

Seismic one-dimensional velocity model of Luxi uplift zone

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P波速度/ km·s-1 S波速度/ km·s-1 层顶深度/km 层底深度/km
5.50 3.29 0 5
5.80 3.43 5 10
6.10 3.61 10 20
6.40 3.74 20
), ArticleFig(id=1281933721704042864, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1281933713533538614, language=CN, label=表1, caption=

鲁西隆起区域地震一维速度模型

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P波速度/ km·s-1 S波速度/ km·s-1 层顶深度/km 层底深度/km
5.50 3.29 0 5
5.80 3.43 5 10
6.10 3.61 10 20
6.40 3.74 20
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基于深度学习数据集的鲁西隆起区域层析成像
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崔鑫 1 , 董腾超 1 , 张春鹏 1
大地测量与地球动力学 | 地球动力学 2026,46(6): 718-727
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大地测量与地球动力学 |地球动力学 2026 , 46 (6) : 718 -727
基于深度学习数据集的鲁西隆起区域层析成像
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崔鑫, 博士, 工程师, 主要研究方向为区域精细构造及层析成像, E-mail:

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崔鑫1 , 董腾超1, 张春鹏1
作者信息
  • 1 山东省地震局, 济南, 250014
作者简介:

崔鑫, 博士, 工程师, 主要研究方向为区域精细构造及层析成像, E-mail:

Tomography of the Luxi Uplift Based on the Deep Learning Dataset
Xin CUI1 , Tengchao DONG1, Chunpeng ZHANG1
Affiliations
  • 1 Shandong Earthquake Agency, Jinan 250014, China
出版时间: 2026-06-15 doi: 10.14075/j.jgg.2025.08.281
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摘要
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采用深度学习方法构建一个高精度地震数据集, 基于双差层析成像方法反演得到区域三维速度模型。研究表明, 地表速度结构与地质构造之间存在联系, 即山脉区多呈现P波高速异常, 而坳陷区多呈P波低速异常。这种特征可能源于, 褶皱区地表多为基岩, 波速相对高, 而坳陷区地表多为第四系土层, 波速相对低。地震震中密集分布在高速和低速异常的过渡区域。高速和低速过渡区域应力梯度变化显著, 更易发生破裂而导致地震活动性强。鲁西隆起区存在一些近垂直的高速和低速块体, 部分震中也呈高倾角分布, 推测为高倾角断层。这可能源于, 在太平洋板块俯冲和郯庐断裂带运动影响下, 鲁西隆起经历多个挤压和伸展的构造作用过程, 形成多个隆起和坳陷的周期, 期间岩石褶皱不断挤压和伸展, 褶皱的倾角逐渐升高, 形成近垂直的速度块体和高倾角断层。通过P波与S波速度联合讨论, 推测尼山和鲁山东北区域均存在地幔玄武质岩浆的侵入, 尼山地区以侵入岩为主, 而鲁山东北区域则存在地幔玄武岩浆突破地表, 形成古火山与喷出岩。

鲁西隆起  /  济阳坳陷  /  双差层析成像(TomoDD)  /  深度学习方法  /  地震数据集  /  三维速度模型

A high-precision seismic dataset was constructed using deep learning methods, and a regional three-dimensional velocity model was obtained through double-difference tomography. The study reveals a correlation between surface velocity structures and geological features: mountainous areas generally exhibit high-velocity P-wave anomalies, whereas depression zones are characterized by low-velocity P-wave anomalies. This pattern may be attributed to the widespread presence of bedrock with relatively high wave velocity in folded regions, in contrast to the Quaternary sedimentary layers with lower velocity in depression areas. Seismic epicenters are densely distributed along the transition zones between high- and low-velocity anomalies. These transitional regions experience significant stress gradients, which facilitate rock fracturing and lead to heightened seismic activity.The Luxi uplift is characterized by several near-vertical high- and low-velocity blocks, with some earthquake epicenters also displaying steeply dipping distributions, suggesting the presence of high-angle faults. This structural configuration may result from multiple phases of compression and extension under the influence of Pacific Plate subduction and the tectonic activity of the Tan-Lu fault zone. These processes have led to cyclic uplift and subsidence in the region, during which progressive compression and extension of rock folds gradually increased their dip angles, forming near-vertical velocity blocks and high-angle faults.Integrated analysis of P- and S-wave velocities suggests the presence of mantle-derived basaltic magmatic intrusions beneath both the Nishan area and the northeastern region of the Lushan. In the Nishan area, the intrusions are predominantly plutonic, whereas in northeastern Lushan, mantle-derived basaltic magma likely reached the surface, forming ancient volcanoes and extrusive rocks.

Luxi uplift  /  Jiyang depression  /  double-difference tomography (TomoDD)  /  deep learning method  /  seismic dataset  /  3D velocity model
崔鑫, 董腾超, 张春鹏. 基于深度学习数据集的鲁西隆起区域层析成像. 大地测量与地球动力学, 2026 , 46 (6) : 718 -727 . DOI: 10.14075/j.jgg.2025.08.281
Xin CUI, Tengchao DONG, Chunpeng ZHANG. Tomography of the Luxi Uplift Based on the Deep Learning Dataset[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (6) : 718 -727 . DOI: 10.14075/j.jgg.2025.08.281
正文
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鲁西隆起位于郯庐断裂带西侧，是中国东部重要的构造单元(图 1)。鲁西隆起属于华北地块的一个次级构造单元，其北部为济阳坳陷，东部为郯庐断裂带、苏鲁褶皱带。
鲁西隆起历史上经过多个周期的抬升，该抬升和济阳坳陷的下降存在隆凹耦合关系，具体的抬升周期时间存在争议，有研究认为是62-53 Ma、44-37 Ma、23-20 Ma[1]，也有研究认为是120-100 Ma、90-60 Ma、50-35 Ma[2]。其抬升过程和演化受到太平洋板块俯冲[3]、地幔物质上涌和郯庐断裂带的控制和影响[4]。鲁西隆起地区地震活动性较强，近年来最大的地震为济南长清M4.1地震，区域内由于矿产资源丰富，非天然地震事件也较多。
鲁西隆起区域最主要的断裂带是其东部的郯庐断裂带。郯庐断裂带历史上存在多个运动阶段，晚三叠世至中白垩世为左旋走滑断裂带，总位移超过100 km[5]。在晚白垩世，其经历强烈的伸展活动，形成一系列裂陷盆地[6]，并在晚始新世逐渐演化为右旋走滑断裂带[7]。根据郯庐断裂带的构造特征和演化历史，将其划分为北段、中段和南段，鲁西隆起位于郯庐断裂带中段的西部。
鲁西隆起区域吸引众多研究人员开展研究，如隆坳耦合关系[1]、隆起历史[2]、反射地震剖面[8]及郯庐断裂带及其邻区的速度模型研究[4, 9]。迄今为止，已有的地震层析成像研究多聚焦于更大区域或地幔尺度的速度模型反演，使用的数据也多为2020年以前的低密度省级台网数据，速度模型分辨率较低[4, 9]，这些都不足以获得鲁西隆起地壳深部构造的高分辨率速度模型。本文基于近年来的加密地震台网数据和深度学习技术获得更为丰富的地震数据集，并进行高分辨率的速度模型反演，探索鲁西隆起地壳的深部构造细节。
1 地震数据和地震一维速度模型
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1.1 地震数据
实时智能地震处理系统(RISP)基于深度学习方法进行到达时间选取、震相关联和地震定位，产出高精度、有价值的地震目录和走时数据。该方法基于PhaseNet[10]和EQTransformer[11]2种深度学习算法来检测地震事件并从地震波形中提取地震震相，然后基于组触发和等时八叉树搜索的组合来执行地震震相关联，最后使用NLLoc[12]地震定位方法进行地震定位。
与人工处理相比，RISP在时间效率方面有相当大的改善。基于随机选取的12 h波形记录，对RISP系统与人工处理方式进行对比分析(图 1、图 2)。对于1 h的波形记录，人工处理平均需要约30 min，而RISP系统只需要约5 min。仅以此例来看，RISP的处理效率是人工处理的6倍。此外，RISP系统借助多核处理器和并行计算技术，可同时处理成百上千条地震记录。在此基础上，其处理效率可达到人工处理的数十倍甚至更高。
使用的原始数据为2020-01—2021-11山东省147个地震台站的地震波波形[13]。基于RISP，共获得26 858条地震事件及其震中参数和走时震相数据(图 1(a))。
为提高数据质量，对数据进行筛选，筛选过程如下。
1) 走时数据筛选：绘制拟合走时曲线，剔除偏离该曲线超过规定距离的震相(图 1(b)~(d))。
2) 地震震相校核：RISP软件既可以识别直接波相Pg，也可以识别其他波相P，但在当地近震数据处理中，易出现震相类型误判——P相通常对应远震传播的纵波，近震记录中不应出现该类震相，而软件可能将近震的Pg相误识别为远震P相，因此需对识别出的震相进行校核，并将误标注的P相校正为Pg相。
3) 地震事件核查：保留6个以上地震台站记录的地震事件。
通过上述数据校核和双差层析成像法(TomoDD)处理，共得到地震事件10 280条。
由于各种干扰，RISP地震目录可能包含一定数量的假阳性地震事件。这些假阳性地震事件并非真实地震事件，而是由于事件聚类错误或远震事件分析错误等原因组合而成的。假阳性事件现象在很多深度学习技术的应用中出现[14-16]，这种现象会污染数据，进而干扰后续的研究，因此需要采取措施进行筛选和处理，以保证反演过程的正确性。
真实地震事件的波形应在每个台站存在Pg和Sg的震动波形，且Pg和Sg的到达时间应与震中距离有关，即越近的台站，Pg和Sg的到达时间越早。图 2(a)是一个真实地震事件的例子，每个台站都包含Pg和Sg，每个台站的到达时间与震中距离呈现近似比例关系。图 2(b)为一个假阳性事件的例子，它错误地拾取了一些未知的干扰，并将它们组合成一个地震事件，该事件中NINY和WSH台站无Sg震相的震动波形，且DSD和LQS台站的Pg和Sg震相走时与震中距关系存在矛盾。图 2(c)为另一个假阳性事件的例子，当研究区域接收到由遥远的大地震传播的地面运动信号时，RISP将该地震的P波和随后的波形识别为另一个本地地震的P波和S波部分，从而合并为一个不存在的地震。如NINY、XCH台站的S震相波形实际为远震的初至波形部分，而QUF和SSH台站的P波震相波形信噪比非常低，应为一种干扰扰动，并非实际地震波形震动。
为了消除假阳性事件，对RISP地震目录进行分析和审查, 对每个RISP地震事件的地震波波形进行检查, 并对所有假阳性事件进行识别和去除，最终给出RISP的真实地震目录。审查过程包括：手动检查每个事件的波形，根据Pg和Sg的震相位置与波形符合程度确定真假阳性事件(图 2(a)~(c))。原始的RISP地震目录包括10 280个地震事件，通过波形检查检测到2 298个假阳性事件。总的来说，假阳性事件约占所有数据的22%。去除上述假阳性事件后，得到7 982个地震事件，其中Pg相66 658个，Sg相46 070个。相对于地震事件的数量，地震震相的数量并不多。造成这种现象的原因是地震台站间距大，约30 km。其次，地震事件震级普遍较低，平均震级为M0.0，低震级事件触发的地震台站数量一般在10个以下。作为对比，人工地震目录中的地震数量为722次，因此深度学习数据量是人工处理数据的10倍左右。
图 2(d)显示，在同一时间，RISP地震目录中的事件数量大约是人工地震目录的10倍。通过震级与地震次数的关系，RISP地震目录的最小完整震级约为M0.1，而人工地震目录的最小完整震级约为M0.9(图 2(e)、(f))。综上，RISP可以提供有价值的地震目录，产出大量地震信息，对地震定位、层析成像等地震研究具有重要意义。
1.2 地震一维速度模型
收集山东、河北两省地震波一维速度模型，并在鲁西隆起区域收集直达波Pg、Sg震相和首波Pn、Sn震相数据，通过走时曲线拟合技术获得上地壳和上地幔的速度。在上述模型的基础上，结合三维速度模型的反演结果，插值得到最终的一维速度模型。所采用的速度模型参数见表 1。
2 地震双差层析成像
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2.1 阻尼因子、平滑因子的参数最优化
为获得最佳的层析成像结果，需要获得最佳的反演参数和速度模型分辨率。反演参数包括阻尼因子和平滑因子，它们会影响反演过程的步长和反演目标的平滑度。利用L型折衷曲线技术[17]，在数据拟合误差与模型范数之间进行权衡的基础上，给出阻尼因子和平滑因子的最优值。速度模型的分辨率，包括水平网格间距和垂直网格间距，控制着三维速度模型的精细程度，并通过检测板实验评价速度模型的最佳分辨率[18]。
在反演计算中，阻尼因子和平滑因子影响速度模型的可靠性和走时数据的拟合误差，优化后的参数因子能获得反演迭代速度、反演稳定性和模型平滑度之间的最佳效果。利用L型折衷曲线技术对各因子的不同取值进行反演[17]，记录速度模型和走时数据的误差，然后根据L型折衷曲线分布选择最佳参数因子。
基于大量反演实验的结果，分别绘制平滑因子和阻尼因子的L型折衷曲线(图 3)，最终选择平滑因子为30，阻尼因子为400。
2.2 检测板实验
为了评估当前速度模型反演的可靠性，通过检测板实验对速度模型的最佳分辨率进行量化[18]。首先，建立一个正负相间的速度模型，根据速度模型、地震资料和当前研究区的台站分布，计算对应的理论走时，对该走时数据加入误差后作为反演的数据集; 然后，利用层析成像方法对以上数据进行反演，并在一系列反演中分析反演速度模型与实际速度模型的一致性，获得最佳的速度模型分辨率。
通过检测板实验对水平网格间距为0.1°~0.3°和垂直网格间距为3~5层的速度网格的不同组合进行测试。结果表明，当水平网格间距为0.2°，垂向网格为4层时，检测板实验可以获得满意的反演结果(图 4)。为增强上地壳的垂向分辨率，在垂向上并未平均分布各速度层，而是采用0 km、5 km、10 km、20 km的速度层分布。综合以上检测板实验的结果，采用水平网格间距为0.2°、垂直4层网格作为最终速度模型。
2.3 地震双差层析成像
通过L型折衷曲线技术分析，得到反演的最佳阻尼因子和光滑因子参数。通过检测板实验和地震射线密度分布分析，得到双差层析成像的最佳水平和垂直网格间距。在此基础上，进行双差层析成像，最后反演得到鲁西隆起区域的速度模型。
将三维速度模型反演结果绘制为鲁西隆起区P波、S波和波速比三维速度结构的水平剖面，并将三维速度模型重定位的震中绘制在各个剖面上(图 5)。为分析鲁西隆起内部及其与济阳坳陷、郯庐断裂带的速度结构，绘制穿过鲁西隆起的6条垂向剖面(图 6(a))，同时将三维速度模型重定位的震中绘制在各个剖面上(图 6(b)~(g))。
P波速度结构显示，在0 km深度，山脉或褶皱区域一般呈现高速异常，如泰山、尼山、蒙山、沂山等; 而坳陷区域常呈现低速异常，如济阳坳陷、鲁西南坳陷、尼山与徂徕山山间盆地区域等(图 5(a))。
在5~10 km深度，速度异常分布呈现较为明显的横向非均匀性，且该异常分布与震中分布存在以下模式：震中密集分布于高速与低速异常分布的过渡区域(图 5(b)~(c))。
在20 km深度，速度异常分布的横向非均匀性减弱。鲁山和蒙山区域呈现高速分布，鲁西南坳陷区域呈现低速分布(图 5(d))。
S波和波速比速度结构显示，在0 km深度，速度结构分布与水域分布具有相关性，即湖泊、水库等区域常呈现S波低速和高波速比分布(图 5(e)、图 5(i))。在5~20 km深度，尼山、徂徕山呈现S波低速分布，而鲁山、济阳坳陷呈现S波高速分布(图 5(f)~(h)、图 5(j)~(l))。
P波速度垂向剖面显示(图 6(b)~(g))，各个山脉或者坳陷下方多表现为高倾角的高速或低速异常。其中山脉区域多呈现高速分布，如泰山(图 6(b)~(c))、蒙山(图 6(d)~(f))、尼山(图 6(d)、(g))、沂山(图 6(b))，与此不同的是，鲁山南侧呈现低速分布(图 6(b)、图 6(d)~(e))。坳陷区域多呈现低速分布，如济阳坳陷(图 6(b)~(e))、鲁西南坳陷(图 6(c)、图 6(g))。
S波速度垂向剖面显示(图 6(b)~(g))，尼山、济阳坳陷下方呈现S波高速异常，波速比高值异常。
3 讨论
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3.1 地震速度结构与地表地质构造、地震分布的关系
地表速度结构显示(图 5(a))，速度异常分布呈现较为明显的横向非均匀性。褶皱带或山脉区域普遍呈现高速异常，如泰山、尼山、蒙山、沂山等，而济阳坳陷、鲁西南坳陷等坳陷则呈现低速异常。褶皱带或山脉区域地表以花岗岩、闪长岩等各类基岩为主，基岩的波速相对较高，因而多呈现高速异常分布; 坳陷区域地表则以第四系沉积或土壤为主，此类介质波速相对较低，因此多呈现低速异常分布。很多研究区域也呈现类似的分布特征，如古浪[19]等。
地下10 km的速度结构显示(图 5(b))，速度异常分布仍显示较为明显的横向非均匀性。天然地震事件的震中深度多数分布于当前深度附近，且震中往往分布于高速与低速的过渡区域，垂向剖面图上也呈现这一分布特征(图 6(d)~(g))。这种现象源于高速区域往往为高应力区域，而低速区域应力水平相对低，但易于传导应力，高、低速过渡区域由于高、低应力的传导和变化，容易导致岩石发生破裂，继而发生地震，因此成为地震活动性较高的区域。地震震中密集分布于高低速过渡区的特征，在海原[20]等区域的研究中也呈现类似的分布特征。
3.2 地壳内存在近垂直的高速体
垂直剖面上(图 6)，各山脉与坳陷下方通常表现为高倾角(近于垂直)的高速或低速异常体。其中山脉区域多呈现高速分布，如泰山(图 6(b)~(c))、蒙山(图 6(d)~(f))、尼山(图 6(d)、图 6(g))、沂山(图 6(b))，与此不同的是，鲁山南侧呈现低速分布(图 6(b)、图 6(d)~(e))。坳陷区域多呈现低速分布，如济阳坳陷(图 6(b)~(e))、鲁西南坳陷(图 6(c)、图 6(g))。王光杰等[8]对鲁西地区的地震剖面研究也显示存在接近垂直的高速体，这与本文的速度模型结果类似。尼山、蒙山、鲁山的地震震中也呈高倾角的线性分布(图 6(b)~(d)、图 6(f))，震中的分布角度反映地下断层的高倾角特征，如尼山附近的仓尼断裂(图 6(c))。这种高倾角符合正断层的特点，意味着该区域断裂以正断层为主。研究显示，鲁西隆起和济阳坳陷经历多个隆起和坳陷阶段[1-2]，其中，鲁西隆起经历多个挤压构造作用和伸展构造作用的复杂过程，伴随这2种构造作用，岩层的向斜和背斜角度逐渐增大，并在破裂后形成高倾角的正断层分布，鲁西隆起和济阳坳陷的箕状构造也是这种过程的一种反映[21]。
以上过程的驱动机制，可能与太平洋板块俯冲及郯庐断裂带的运动有关。在太平洋板块向亚欧板块俯冲的过程中，在郯庐断裂带与俯冲带之间形成大地幔楔[4]，伴随着俯冲过程，大地幔楔中形成复杂的地幔对流，郯庐断裂带区域同时存在拆沉的下降流和湿热的上升流[4]，部分地幔玄武岩物质上涌并侵入地壳，形成鲁山和尼山区域的低速岩体以及昌乐、邹平等古火山分布。郯庐断裂带历史上存在多个运动阶段，晚三叠世至中白垩世为左旋走滑断裂带，总位移超过100 km[5]; 在晚白垩世，其经历强烈的伸展活动，形成一系列裂陷盆地[6]，并在晚始新世逐渐演化为右旋走滑断裂带[7]。郯庐断裂带的走滑和伸展活动，也会形成对鲁西隆起的挤压和伸展作用。地幔上升流与郯庐带的伸展构造作用导致区域性的多个伸展和挤压构造作用过程，继而产生鲁西隆起区域的隆起和坳陷。在此期间，挤压和伸展过程反复作用，形成高倾角的断层与近垂直的速度体分布。
3.3 P波与S波速度结构的联合讨论
岩石的波速比和泊松比反映地壳的矿物组成，较低的泊松比对应长英质矿物，而较高的泊松比意味着富含镁铁质矿物，或存在高孔隙度、流体填充及熔融物质的可能性[22-23]。
地表S波速度结构显示(图 5(e))，高速异常主要分布于山脉区域，如泰山、鲁山、尼山、蒙山等，济阳坳陷呈现低速异常，S波速度结构与地表水资源分布存在联系，即湖泊、水库密集分布区域常呈现S波低速异常，波速比高值异常。由于液态水不能传导S波，因此液态水的存在会导致区域S波速度降低[22-23]，研究区内湖泊、水库等水体覆盖区域的S波低速异常，也是液态水存在的速度结构响应。
下地壳的P波水平剖面显示(图 5(d))，在鲁西南坳陷区域存在大面积的低速异常，而低速异常可能意味着地幔玄武岩浆的上涌[4]。综合P波和S波速度异常分布特征，鲁西南坳陷区域地壳呈现波速比的低值异常(图 5(h)、图 5(l))，推测以上区域存在一定程度的地幔物质侵入，但未上涌至地表形成火山或喷出岩，而是以侵入形式固结，使得侵入岩成为该区域岩浆岩的主要类型。下地壳既有原长英质矿物，又包含地幔的镁铁质矿物，因此呈现波速比低值异常。
昌乐和邹平古火山的垂向速度剖面显示(图 6(c)、图 6(e))，二者均呈现P波低速异常分布。腾冲火山的层析成像显示，火山下方10~20 km呈现P波低速异常，推测为岩浆上升通道[24]。昌乐和邹平古火山的P波速度异常均呈现低速特征，推测为类似于腾冲火山的古岩浆上升通道。综合P波和S波速度结果，昌乐和邹平古火山呈波速比低值异常。富含长英质的地壳矿物呈低波速比，而富含镁铁质的地壳矿物呈高波速比[22-23]，推测古火山的上地壳既包含长英质矿物，又包含镁铁质矿物，下地壳则以长英质矿物为主，即下地壳已经没有地幔上涌的岩浆通道，推测原始的岩浆通道已经在多个伸展和挤压构造作用过程中被长英质矿物所侵占。
3.4 深度学习应用和展望
利用PhaseNet、EQtransformer等深度学习技术获取鲁西隆起区域的地震震相数据，通过地震关联和地震定位进行数据处理，并通过人工审核，建立基于深度学习技术的新型地震目录和走时震相数据集。
与传统地震目录相比较[13]，深度学习地震目录和数据有很大的改进，比如地震事件的数量有明显的增加。深度学习产出的数据集的数量大约是传统人工数据集数量的数十倍，这为地球物理研究提供了更丰富的数据基础，深度学习数据集的数量优势在很多研究区域均得到验证[25]。此外，深度学习技术避免了技术人员在地震震相判别时的主观差异，可大大提高地震数据的质量和处理效率，为后续地震研究提供更有价值和可靠的原始数据。以本文的层析成像研究为例，由于深度学习方法提供更有价值的地震目录和地震数据，层析成像的结果得到显著改善[26]，速度模型的分辨率也有显著提高，如深度学习数据的检测板水平分辨率为0.20°，而传统地震数据的水平分辨率为0.25°。深度学习方法的另一个优势是效率高，因为深度学习方法可以同时处理成百上千的海量数据，其产出地震目录和地震走时数据的处理时间远优于传统人工处理。深度学习技术在地震目录构建上的缺点主要在于假阳性事件。本文主要通过人工逐一审核地震事件的波形记录与其震相记录，若波形记录与震相记录不能对应，则判定为假阳性事件，并进行删除。通过假阳性事件的审核和筛选过程，可以确保地震数据的真实性，避免错误数据对数值计算的污染，保证层析成像反演过程的准确性。根据本文中假阳性事件的常见类型，将给出以下假阳性事件排除的定量标准，包括：
1) 同一事件的P波与S波震相数量均在5条及以上；
2) P波和S波波段在震相后存在相应的P波和S波波段波形；
3) P波波段振幅与S波波段振幅均与事件震级振幅相符合。
根据以上定量标准，可以进行自动化筛选，提升深度学习数据的质量。
由于其强大的数据分析处理能力和高度的自动化效率，深度学习技术的应用，可为地震新型目录的构建和后续的地球科学研究提供更坚实的基础，如地震新型目录的构建[27]、接收函数的研究[28]等。未来通过技术的发展和升级，例如对各种复杂地震阶段的识别和应用，深度学习技术可以为地震科学研究提供更多服务。
4 结语
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利用深度学习方法对鲁西隆起的波形数据进行分析。利用地震定位技术，获得研究区的地震目录和地震震相资料。通过数据筛选和人工审查，去除假阳性事件，获得深度学习地震目录。利用L型折衷曲线技术和检测板试验确定反演的最佳阻尼因子、平滑因子、速度网格分辨率等参数，使用双差层析成像得到三维速度模型。通过分析讨论得出以下结论：
1) 地表速度结构与地质构造之间存在一定的联系，即山脉和褶皱区域多呈现P波高速异常分布，而坳陷或盆地区域多呈P波低速异常分布。这种特征可能源于，褶皱区域地表多为基岩，岩石波速较高，而坳陷或盆地区域地表多为第四系或土层，其波速较低。
2) 地震震中密集分布在高速和低速异常的过渡区域。高速区域岩石应力水平高、强度大，低速区域则应力相对低，且易于传导应力，高速和低速过渡区域由于应力梯度变化显著，更容易发生破裂而导致地震活动性强。
3) 鲁西隆起地区存在一些近垂直的高速和低速块体，部分震中也呈现高倾角分布，推测为高倾角断层。近垂直的速度块体和高倾角断层的形成原因可能在于，在太平洋板块俯冲和郯庐断裂带运动影响下，鲁西隆起区域经历多个挤压和伸展的构造作用过程，形成多个隆起和坳陷周期，期间岩石褶皱不断挤压和伸展，褶皱的倾角逐渐升高，形成近垂直的速度块体和高倾角断层。
4) 通过鲁西南坳陷区域昌乐、邹平古火山的P波与S波速度分析讨论，推测三者均存在地幔玄武岩的侵入。其中，鲁西南坳陷地区以侵入岩为主，而昌乐、邹平古火山区域则为地幔玄武岩浆突破地表，形成古火山与喷出岩。
5) 通过应用深度学习方法，对地震数据中的信息进行深度挖掘，建立有价值的研究区地震目录，提高地震层析成像的分辨率。深度学习数据集中包含一定数量的假阳性事件，需要进行检查和去除，以进一步提高数据质量。与传统人工处理过程相比，深度学习技术效率显著提高，且去除了人工主观的影响，其产出的数据集数量显著提升，可为相关研究提供更完善的数据基础。
基金
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  • 地震监测预报预警工作任务(CEA-JCYJ-202501035)
文献
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2026年第46卷第6期
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doi: 10.14075/j.jgg.2025.08.281
  • 接收时间：2025-08-18
  • 首发时间：2026-07-09
  • 出版时间：2026-06-15
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  • 收稿日期：2025-08-18
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地震监测预报预警工作任务(CEA-JCYJ-202501035)
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    1 山东省地震局, 济南, 250014
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
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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