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Article(id=1322942131547021407, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322941718441623734, articleNumber=null, orderNo=null, doi=10.13809/j.cnki.cn32-1825/te.2024337, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739203200000, receivedDateStr=2025-02-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793343583236, onlineDateStr=2026-10-30, pubDate=1779724800000, pubDateStr=2026-05-26, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793343583236, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793343583236, creator=13701087609, updateTime=1793343583236, updator=13701087609, issue=Issue{id=1322941718441623734, tenantId=1146029695717560320, journalId=1322258508890079423, year='2026', volume='16', issue='3', pageStart='489', pageEnd='736', issueExtLink='null', onlineDate='null', pubDate='1779724800000', pubDateStr='2026-05-26', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793343484745, creator='13701087609', updateTime=1793344165564, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322944574066402279, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322941718441623734, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322944574070596584, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322941718441623734, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=607, endPage=615, ext={EN=ArticleExt(id=1322942134021660768, articleId=1322942131547021407, tenantId=1146029695717560320, journalId=1322258508890079423, language=EN, title=A deep learning-based seismic frequency extension method using generalized regression neural network, columnId=1322941494130258079, journalTitle=Petroleum Reservoir Evaluation and Development, columnName=Oil and Gas Exploration, runingTitle=null, highlight=

To meet the urgent demand for high-resolution seismic data in the exploration of structural-lithological oil and gas reservoirs in the Subei Basin, and to address the limitations of traditional deconvolution and frequency-domain transformation methods such as insufficient utilization of well information and poor adaptability to nonlinear characteristics, this study proposed a deep learning-based intelligent seismic frequency extension method using generalized regression neural network (GRNN). By integrating well-seismic joint technology, a deep learning framework centered on GRNN was constructed to fully utilize logging data to synthesize high-frequency seismic labels and realize the intelligent extension of the seismic frequency band. The GRNN network, based on the Parzen window probability density estimation theory, adopted a four-layer topology consisting of input layer, pattern layer, summation layer, and output layer. It had advantages including non-parametric modeling, local feature adaptive approximation, and noise robustness, which effectively solved problems such as non-stationarity of seismic signals and high-dimensional noise interference. In the YA high-density 3D survey area of the Subei exploration area, high-frequency seismic traces were synthesized from well data to train the network for frequency extension processing. The results showed that the effective frequency range was extended from 7-43 Hz to 6-56 Hz. The thin sand body boundaries were delineated more clearly, and the well-seismic matching was good, with a correlation of synthetic records of 82%. Additionally, this study explored the effects of different training set sizes and different selection methods of training traces on the prediction results. It was found that selecting seismic traces that could control the entire seismic profile as training data yielded better frequency extension results. The deep learning-based frequency extension method has been applied to multiple blocks in the Subei Basin, yielding favorable results that verified its effectiveness and applicability. The research findings provide high-resolution data support for the fine characterization of complex lithological oil and gas reservoirs and promote the intelligent development of seismic frequency extension technology.

, articleAbstract=null, authors=SHAOFENG ZHANG1, CHONG XU2, TINGCHAO GUO2, authorsList=SHAOFENG ZHANG, CHONG XU, TINGCHAO GUO, authorCompany=null, correspAuthors=CHONG XU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1322942142380908659, articleId=1322942131547021407, tenantId=1146029695717560320, journalId=1322258508890079423, language=CN, title=一种基于广义回归神经网络的深度学习地震拓频方法, columnId=1322941494327390371, journalTitle=油气藏评价与开发, columnName=油气勘探, runingTitle=null, highlight=

针对苏北盆地构造-岩性油气藏勘探对高分辨率地震资料的迫切需求，传统反褶积和频域变换方法存在井信息利用不足、非线性特征适应性差等局限性的问题，提出一种基于广义回归神经网络（Generalized regression neural network，GRNN）的深度学习地震智能拓频方法。通过融合井震联合技术，构建以GRNN为核心的深度学习框架，充分利用测井数据合成高频地震标签，实现地震频带智能化拓展。GRNN网络基于Parzen窗概率密度估计理论，采用输入层、模式层、求和层及输出层的4层拓扑结构，具有非参数化建模、局部特征自适应逼近及噪声鲁棒性等优势，有效解决了地震信号非平稳性、高维噪声干扰等问题。在苏北探区YA高密度三维工区，通过井资料合成高频地震道并训练网络进行拓频处理，其结果表明：有效频率范围从 7~43 Hz 拓展到 6~56 Hz，薄砂体边界刻画更清晰，与钻井井震匹配度良好，合成记录相关性达82%。同时，研究探讨了不同数量训练集和不同训练道选取方式对预测效果的影响，发现选取能控制整个地震剖面的地震道作为训练数据时拓频效果更佳。利用基于深度学习拓频方法在苏北盆地多个区块进行了应用均取得了较好应用效果，验证了方法的有效性和适用性，研究成果为复杂岩性油气藏精细刻画提供了高分辨率数据支撑，推动了地震拓频技术的智能化发展。

, articleAbstract=null, authors=张韶峰1, 许冲2, 郭廷超2, authorsList=张韶峰, 许冲, 郭廷超, authorCompany=null, correspAuthors=许冲, authorNote=

张韶峰（1974—），男，本科，高级工程师，从事油气勘探研究与国际石油物探项目管理等相关工作。地址：北京市朝阳区吉市口路9号，邮政编码：100020。E-mail：

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许冲（1984—），女，硕士，高级工程师，从事油气勘探研究等相关工作。地址：江苏省南京市尧新大道68号，邮政编码：210046。E-mail：
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张韶峰（1974—），男，本科，高级工程师，从事油气勘探研究与国际石油物探项目管理等相关工作。地址：北京市朝阳区吉市口路9号，邮政编码：100020。E-mail：

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张韶峰（1974—），男，本科，高级工程师，从事油气勘探研究与国际石油物探项目管理等相关工作。地址：北京市朝阳区吉市口路9号，邮政编码：100020。E-mail：

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均匀选道控制选道居中选道
道号损失值道号损失值道号损失值
1000.020 21000.010 71000.008 0
2000.025 72000.005 82000.006 8
3000.014 53000.006 13000.009 7
4000.019 14000.007 94000.006 0
5000.020 45000.005 05000.010 9
6000.013 36000.004 76000.008 3
7000.007 77000.003 57000.034 6
), ArticleFig(id=1322942157958553771, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942131547021407, language=CN, label=表1, caption=

不同训练道选取方式的损失值分析

, figureFileSmall=null, figureFileBig=null, tableContent=
均匀选道控制选道居中选道
道号损失值道号损失值道号损失值
1000.020 21000.010 71000.008 0
2000.025 72000.005 82000.006 8
3000.014 53000.006 13000.009 7
4000.019 14000.007 94000.006 0
5000.020 45000.005 05000.010 9
6000.013 36000.004 76000.008 3
7000.007 77000.003 57000.034 6
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一种基于广义回归神经网络的深度学习地震拓频方法
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张韶峰 1 , 许冲 2 , 郭廷超 2
油气藏评价与开发 | 油气勘探 2026,16(3): 607-615
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油气藏评价与开发 |油气勘探 2026 , 16 (3) : 607 -615
一种基于广义回归神经网络的深度学习地震拓频方法
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张韶峰（1974—），男，本科，高级工程师，从事油气勘探研究与国际石油物探项目管理等相关工作。地址：北京市朝阳区吉市口路9号，邮政编码：100020。E-mail：

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张韶峰（1974—），男，本科，高级工程师，从事油气勘探研究与国际石油物探项目管理等相关工作。地址：北京市朝阳区吉市口路9号，邮政编码：100020。E-mail：

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张韶峰1 , 许冲2, 郭廷超2
作者信息
  • 1　中国石化地球物理公司国际业务发展中心，北京　100020
  • 2　中国石化江苏油田分公司，江苏　 　南京　210046
通讯作者:
许冲（1984—），女，硕士，高级工程师，从事油气勘探研究等相关工作。地址：江苏省南京市尧新大道68号，邮政编码：210046。E-mail：
作者简介:

张韶峰（1974—），男，本科，高级工程师，从事油气勘探研究与国际石油物探项目管理等相关工作。地址：北京市朝阳区吉市口路9号，邮政编码：100020。E-mail：

A deep learning-based seismic frequency extension method using generalized regression neural network
SHAOFENG ZHANG1 , CHONG XU2, TINGCHAO GUO2
Affiliations
  • 1 International Department, Sinopec Geophysical Corporation, Beijing 100020, China
  • 2 Sinopec Jiangsu Oilfield Company, Nanjing, Jiangsu 210046, China
出版时间: 2026-05-26 doi: 10.13809/j.cnki.cn32-1825/te.2024337
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苏北盆地  /  广义回归神经网络  /  深度学习  /  频率拓展  /  测井信息
Subei Basin  /  generalized regression neural network  /  deep learning  /  frequency extension  /  logging information
张韶峰, 许冲, 郭廷超. 一种基于广义回归神经网络的深度学习地震拓频方法. 油气藏评价与开发, 2026 , 16 (3) : 607 -615 . DOI: 10.13809/j.cnki.cn32-1825/te.2024337
SHAOFENG ZHANG, CHONG XU, TINGCHAO GUO. A deep learning-based seismic frequency extension method using generalized regression neural network[J]. Petroleum Reservoir Evaluation and Development, 2026 , 16 (3) : 607 -615 . DOI: 10.13809/j.cnki.cn32-1825/te.2024337
正文
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苏北盆地属于东部勘探老区，构造-岩性油气藏已成为目前勘探的重点[1-4]，对地震资料品质要求更高，常规处理技术无法满足勘探要求，特别是对资料分辨率的要求。同时勘探区域井资料丰富，如何有效利用这些井信息辅助地震拓频处理是地球物理工作者研究的热点。深度学习近年来发展迅速，在地震资料处理及解释的众多场景得到了广泛应用，为地震智能拓频提供了机遇。
反褶积作为地震资料高分辨率处理的核心技术，其多维度效应（地层滤波消除、子波能量压缩、多次波压制）能够促使地震记录趋近于理想反射系数序列。ROBINSON[5]1967年提出地震记录褶积模型，首次将反射系数序列与子波动态耦合关系数学化，阐释了地下波场传播的动力学特征。在此理论基础上，众多改进型反褶积算法相继问世，包括具有前导预测功能的预测反褶积[6]、基于信息熵最小化准则的最小熵反褶积[7]、采用复赛谱解析技术的同态反褶积[8]、谱匹配优化技术驱动的谱模拟反褶积[9]等方法。
目前在地球物理勘探领域，提高地震资料分辨率的研究方法多样且广泛。学者们通过压缩子波、时频分析等技术手段，如傅立叶变换[10]、小波变换[11]、Radon变换[12]、Shearlet变换[13]和广义S变换[14]等，有效提升了地震资料品质。压缩感知理论的应用也显著提高了地震资料的分辨率[15-16]。这些方法虽各有特色，但普遍存在对井信息利用不足的问题。因此，研究者们提出了多种井震联合技术。例如，PEDERSEN-TATALOVIC等[17]提出的多变量插值方法，通过整合测井信息、地震参数和速度，显著提升了低频成分的预测准确性，尤其是频率介于2~8 Hz；周超等[18]利用测井高频信息求取拓频算子，进而提高地震道分辨率；李曦宁[19]通过反褶积技术、测井信息约束井旁地震道，从而提升分辨率；崔永福等[20]提出了一种井控谱约束反褶积方法，通过地震数据和垂直地震剖面（VSP）资料的频带确定有效信号范围，设计谱约束算子。这些井控技术的应用，不仅提高了地震资料的品质和可靠性，也推动了井震联合技术的持续发展和完善。
在地球物理勘探领域，深度学习作为多层机器学习模型，通过多隐层结构从海量数据中提取高阶特征以实现分类与预测[21-23]。其核心特征体现在2个方面：①层级化网络架构（如含多个隐层的深度神经网络）；②具备从底层特征自主构建复杂表征的能力。近年来，该技术已广泛应用于地震岩相智能识别[24]、断层检测[25]、高分辨率处理[26]和Radon变换反演[27]等领域。针对地震拓频问题，国内学者提出多项创新性方法：张联海等[28]基于数据驱动范式构建深度卷积神经网络（DCNN），实现地震反射信号的稀疏反褶积求解；倪文军等[29]融合宽频子波特性，设计出基于卷积神经网络（CNN）的子波整形反褶积算法；田欣然[30]提出条件深度卷积生成对抗网络（C-DCGAN），通过对抗训练机制提升地震数据分辨率；CHAI等[31]借鉴机器视觉中的图像转换理论，提出多道稀疏反褶积深度学习方法，但其模型未引入测井数据作为神经网络训练的监督约束，限制了井震联合信息的协同优化能力。当前研究普遍表明：深度学习在拓频处理中展现出强大的非线性映射优势，但如何有效融合先验地质信息仍是亟待突破的技术瓶颈。
传统地震频带扩展技术通常依赖于地震数据满足特定的理论假设（如子波平稳性、噪声高斯分布等），其应用效果受制于地质模型简化带来的理论偏差。深度学习算法是数据驱动的方法，因此被应用于各种需要泛化的地球物理问题[32]。在之前研究的基础上，引入具有较强表征学习能力的广义回归神经网络（Generalized Regression Neural Network，GRNN）构建策略，发展了基于深度学习地震频率拓展的技术框架，研发了深度学习域地震拓频处理基础模块。在实现方法和算法的基础上，将基于GRNN网络的深度学习地震拓频方法应用于模型和苏北探区多块三维地震资料拓频处理中，验证了该方法的正确性和可行性。
1　基本理论
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深度学习是一种基于多层神经网络结构的数学模型[33-34]，其核心在于通过增加网络层数，即网络深度来提升模型对数据特征的抽象能力。该技术利用大规模数据集高效学习数据间的内在规律，从而实现高精度的分类与预测任务。深度学习的本质特征在于其能够通过逐层组合底层特征，构建更为抽象的高阶特征表示，进而提取数据的关键特性。从数学角度而言，深度学习及其他机器学习算法均可视为一种基于大数据的拟合机制，旨在从海量数据中归纳出可推广的规律。具体而言，深度神经网络在贝叶斯反演理论框架下，通过迭代更新网络参数，实现对特定信号特征的深度提取与表征。近年来，广义回归神经网络凭借其强大的特征学习能力，在地球物理勘探领域受到广泛关注，为复杂地质特征的建模与解释提供了新的技术途径。
GRNN是一种基于概率密度函数估计的前馈神经网络[35]，其理论基础源于Parzen窗非参数估计方法。GRNN的核心在于通过样本数据直接估计联合概率密度函数，无需预设模型结构。给定训练样本集：
{(xi,yi)}i=1n
式中：xi∈Rd为输入值；R为实数集合；d为输入向量的维度（即特征数量）；yi∈R为输出值；i为样本索引（取值介于1~n）；n为训练样本总数。
GRNN通过Parzen窗方法估计输入和输出的联合概率密度函数：
f^x,y=1n2πd+1/2σd+1∑i=1nexp-x-xi2+y-yi22σ2
式中： x为输入值，表示地震数据的特征（如振幅、频率等），通常经过归一化处理；y为输出值；σ为平滑参数（带宽），控制核函数的宽度。
基于条件期望理论，GRNN的输出预测值y^x可表示为：
y^x=∑i=1nyiexp-x-xi22σ2∑i=1nexp-x-xi22σ2
式（3）表明GRNN的输出是训练样本输出的加权平均，权重由输入向量与样本间的欧氏距离决定。GRNN的训练过程仅需确定σ，通常通过交叉验证或最大似然估计优化：
σ*=γargmaxσ ∏j=1nf^xj,yj|σ
式中：σ*为最优平滑参数（带宽），通过最大化联合概率密度的乘积确定；γarg为参数，在优化问题中与max组合使用，即γargmax为寻找使目标函数取得最大值的参数值；j为样本索引（取值介于1~n）；xj为第j个训练样本的输入值（如地震数据特征值），通常经标准化处理；yj为第j个训练样本的输出值（如高频地震道预测值），通常经标准化处理。
GRNN的优势在于无需迭代训练、结构简单且对非线性映射具有较强拟合能力，尤其适用于小样本和高维数据的回归问题。
2　基于深度学习地震拓频技术框架
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基于地震数据的非平稳性、高维度、强噪声干扰特性[36-38]，创新性地构建了非参数化GRNN地震特征反演架构，结合时变谱约束损失函数与井控高频标签合成技术，形成了端到端的地震频带扩展技术框架。如图1所示，GRNN网络由输入层、模式层、求和层、输出层构成的四层拓扑结构组成，其中输入层接收输入值x；模式层神经元（Pn）计算每个训练样本的权重；求和层分别完成求和分母项SD、求和分子项SNT的加权求和；输出层通过SNT/SD计算最终预测值y^x。技术优势主要体现在以下3个方面：①基于Parzen窗概率密度估计理论，通过非参数化建模有效适应地震信号的非线性特征；②模式层神经元与训练样本严格对应，赋予网络对局部特征的自适应逼近能力；③求和层的概率归一化机制显著提升模型鲁棒性，可抑制地震数据中随机噪声的干扰。
图2所示基于深度学习高频拓展技术框架流程，呈现了由井资料合成高频地震道，并与井旁地震道相结合作为高低频训练道，输入到所选用的神经网络中训练网络，训练后的网络用于地震数据的高频拓展预测，最终得到拓频处理结果，实际应用中高频地震训练道的获取方式不限于井信息高频地震记录合成一种方式，根据训练的需要也可以通过其他提频方式直接获取高频训练道。
该框架中高频地震子波的提取、基于井资料计算反射系数、多口井频率域神经网络训练逼近关键核心环节，影响最后拓频的效果。其中，反射系数的计算需要获取声波时差测井和密度测井资料，并结合时深关系及滤波技术计算出与地震资料尺度匹配的反射系数，用于合成高频地震记录，作为训练输出；神经网络训练逼近需要通过测试确定残差范围、训练次数等关键参数，以提升神经网络的泛化能力。
3　实际数据测试
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基于GRNN的深度学习地震智能拓频方法在苏北探区YA、CN、SH等岩性油藏有利区开展测试和应用，均取得了良好应用效果。以高邮凹陷YA地区高密度三维为例，研究目标区YA构造上覆盖高邮凹陷的樊川次凹、汉留断裂构造带和北斜坡内坡过渡带。YA地区内汉留断裂在该区发生构造转换，发育近东西向优势展布的断层系统，切割形成断鼻构造与断块组合体，其中多个断块已证实具有油气赋存潜力。YA地区沉积体系位于三角洲前缘相带，联井储层对比发现戴南组储层厚度横向变化特征明显，是构造-岩性油气藏勘探有利区。受到复杂水网吸收衰减的影响，YA地区高密度地震资料成果数据显示戴南组主频在25 Hz左右，有效频率介于7~45 Hz。地震垂向分辨尺度只能达到砂组级别，单砂层厚度远低于地震垂向分辨率。常规地震处理、反演技术很难分辨薄砂体及描述砂体边界，直接影响滚动评价及开发效果。针对这一现状，应用了基于深度学习的智能拓频技术，充分利用该区的测井信息，提高地震数据的分辨率，为刻画薄砂体边界提供依据，取得良好钻探效果。
3.1　井资料合成高频地震道
为了充分利用工区内的井资料，首先对区域内的测井资料进行了分析。通过井的深度和时深转换关系，确定该测井段在地震道中的对应位置，进行深时转换，并对井资料进行抽稀等处理，使得测井数据和地震数据相匹配。利用声波时差和密度数据计算反射系数，合成高频地震记录，得到高频地震道标签数据。将对应的原始井旁地震道和高频合成地震标签道抽取出来作为训练集，参与神经网络的训练。
以苏北盆地高邮凹陷Y38井为例，利用井信息合成高分辨率地震道作为神经网络的训练样本，进行拓频处理。如图3所示，通过反射系数（图3a）和子波（图3b）进行褶积，得到高分辨率合成地震记录（图3c）。将合成的高分辨率地震记录替换到原始井旁地震道（图4a红框）中，形成完整的高分辨率标签地震道（图4b红框）。将合成的高分辨率标签地震道和低分辨率井旁地震道抽取出来作为训练集，参与神经网络的训练。
3.2　不同数量训练集训练网络的预测效果测试
研究中选取4、10、50道作为训练集训练网络，分别给出相应结果及分析（图5）：①采用4道作为训练集进行训练，其中4道均由4口测井所合成的高分辨率记录道对整个剖面进行预测，预测结果如图5b所示，对比图5a，此时预测效果不好；②采用10道作为训练集进行训练，其中4道为上述4口测井合成的高分辨率记录道，6道为时变反褶积生成的高分辨率记录道，对整个剖面进行预测，预测结果如图5c所示，对比图5a，准确度有了较大提升，但由于训练样本依然不足，信噪比偏低；③采用50道作为训练集进行训练，其中4道为上述4口测井合成的高分辨率记录道，46道为时变反褶积生成的高分辨率记录道，对整个剖面进行预测，预测结果如图5d所示，对比图5a，分辨率得到明显提升，但随着训练集继续扩充，效果改善不再明显。
3.3　不同训练道选取方式的损失值分析
为了研究不同训练道对测试结果影响，以及探寻选用哪些道作为训练道会使结果的准确性更高，进行了不同训练道的误差损失值对比分析。地震数据大小为701道，每道1 251个采样点。实际地震勘探中，一个剖面中的井控地震道通常较少，假定井控地震道为4。采用3种方式选井控地震道：①均匀选道，所选道号175、350、525、700；②控制选道，所选道号63、214、379、601；③居中选道，所选道号320、340、360、380。研究不同训练道选取方式的损失值分析，如表1所示。
通过对比可以看出，当选取可以控制整个地震剖面的地震道作为训练数据时，预测值和高分辨率输出道的误差最小，即拓频效果越佳。针对如何选取合适的训练地震道数据，可以从几方面着手：①训练道尽量可以贯穿所有的地层；②训练道尽量避免选在断层处，以免影响效果；③在对整个剖面做处理的时候可以先选择少量的样本道做拓频处理，通过对比再选择效果最佳的训练道。
3.4　基于深度学习拓频结果分析
基于GRNN的深度学习地震智能拓频方法是建立在训练学习的基础上，对网络结果和训练集都有一定要求，同时涉及合成井高分辨率地震道及其他拓频方法配合使用的问题，但只要考虑周全，保证一定数量的训练数据集，就可以较好满足处理目的。图6展示了苏北盆地高邮凹陷YA624线原始剖面和基于深度学习拓频结果对比，应用效果明显，可以在该工区推广应用。图7a为未使用基于深度学习地震智能拓频方法处理的频谱，振幅以-20 dB为准，有效频率介于7~43 Hz，图7b为基于深度学习地震智能拓频方法处理后的提高分辨率频谱，有效频率介于6~56 Hz，高频拓展了13 Hz。
从拓频地震资料与原始地震资料的井震标定对比分析来看（图8），图中蓝色虚线为三垛组一段五亚段地层，拓频地震资料能够较好保持地层反射界面特征，主要标志层的地震响应特征与原始地震资料基本保持一致。同时，拓频地震资料纵向分辨率得到较大提升，图8b中红色箭头指向的薄砂层在拓频地震上得到较好成像，与钻井具有较好井震匹配度，地震合成记录的相关性达到0.82以上。
4　结论与认识
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基于深度学习的地震智能拓频技术，有效提升了地震资料处理的品质和智能化程度，为岩性及隐蔽油气藏勘探提供了支撑。通过研究主要得到以下结论与认识：
1）建立的基于深度学习地震智能拓频理论框架，为基于深度学习地震频带智能化拓展技术开发及应用提供了理论指导。
2）深度学习网络种类繁多，应针对相关网络进行适应性测试分析，以提升其泛化能力，同时有必要研究建立合适的人工神经网络，提升对地震资料的适配性。
3）基于深度学习的智能拓频处理建立在训练学习的基础上，对网络结果和训练集都有一定要求，同时涉及合成井高分辨率地震道及其他拓频方法配合使用存在问题，但只要考虑周全，保证一定数量的训练数据集，也可以达到处理目的，同时大大提升了拓频过程的智能化程度。

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2026年第16卷第3期
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doi: 10.13809/j.cnki.cn32-1825/te.2024337
  • 接收时间：2025-02-11
  • 首发时间：2026-10-30
  • 出版时间：2026-05-26
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  • 收稿日期：2025-02-11
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    1　中国石化地球物理公司国际业务发展中心，北京　100020
    2　中国石化江苏油田分公司，江苏　 　南京　210046

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许冲（1984—），女，硕士，高级工程师，从事油气勘探研究等相关工作。地址：江苏省南京市尧新大道68号，邮政编码：210046。E-mail：
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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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