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基于多源遥感与航空物探数据的岩性分类方法

于长春, 孙杰, 张迪硕, 张艳, 胡越

于长春, 孙杰, 张迪硕, 张艳, 胡越. 2022: 基于多源遥感与航空物探数据的岩性分类方法. 地质通报, 41(2-3): 210-217. DOI: 10.12097/j.issn.1671-2552.2022.2-3.002
引用本文: 于长春, 孙杰, 张迪硕, 张艳, 胡越. 2022: 基于多源遥感与航空物探数据的岩性分类方法. 地质通报, 41(2-3): 210-217. DOI: 10.12097/j.issn.1671-2552.2022.2-3.002
YU Changchun, SUN Jie, ZHANG Dishuo, ZHANG Yan, HU Yue. 2022: Lithologic classification method based on multi-source remote sensing and aero geophysical data. Geological Bulletin of China, 41(2-3): 210-217. DOI: 10.12097/j.issn.1671-2552.2022.2-3.002
Citation: YU Changchun, SUN Jie, ZHANG Dishuo, ZHANG Yan, HU Yue. 2022: Lithologic classification method based on multi-source remote sensing and aero geophysical data. Geological Bulletin of China, 41(2-3): 210-217. DOI: 10.12097/j.issn.1671-2552.2022.2-3.002

基于多源遥感与航空物探数据的岩性分类方法

基金项目: 

国家重点研发计划课题《综合航空地球物理探测系统集成与方法技术示范研究》 2017YFC0602201

中国地质调查局项目《秦岭及天山等重点成矿区带航空物探调查》 121201203000160006

详细信息
    作者简介:

    于长春(1964-),男,博士,教授级高工,从事航磁方法技术研究和航磁资料解释工作。E-mail: bjycc@126.com

  • 中图分类号: P588;P527;P631

Lithologic classification method based on multi-source remote sensing and aero geophysical data

  • 摘要:

    遥感影像可以获取地表岩性的光谱、色调、纹理等信息,但其所提取的信息局限于地表,对深层地质问题解释并无明显优势;航空物探数据则对地下深部异常体信息的提取更具优势。单一某类数据难以满足基础地质、资源勘查等方面复杂应用的需求。因此,提出一种遥感与航空物探信息联合分析方法,以新疆某地为研究区,结合遥感与航空物探多源数据特征,基于随机森林方法对研究区岩性进行分类。结果表明,与使用单一某类数据相比,遥感与航空物探信息联合分析方法能提高岩性分类精度。该方法对于推动遥感与航空物探技术在地质填图中的精细化应用,具有一定实用价值与指导意义。

    Abstract:

    Remote sensing images can acquire the spectrum, tone, texture and other information of the surface rocks, but the extracted information is limited to the surface and has no obvious advantage in the interpretation of deep geological information.Aero geophysical data has more advantages in extracting information of underground abnormal bodies. A single type of data cannot meet the needs of complex applications in basic geology and resource exploration.Therefore, a combined method of remote sensing and aero geophysical analysis was proposed. A certain area in Xinjiang was taken as the study area to classify the lithology through the analysis of multi-source remote sensing images and aero geophysical data based on the random forest method.The results show that the proposed method can improve the accuracy of lithologic classification in the study area compared with a single type of data.The proposed method has certain practical value and guiding significance in promoting the fine application of remote sensing and aero geophysical exploration technology in geological mapping.

  • 致谢: 感谢中国地质科学院地质力学研究所胡健民教授对本文研究工作的支持与建议,感谢中国自然资源航空物探遥感中心钟昶高级工程师对遥感高分数据融合处理的技术支持;感谢审稿专家对本文提出的宝贵意见及建议。
  • 图  1   1:5万研究区地质图(a)和GF-2影像(b)

    Figure  1.   1:50000 geological map (a) and GF-2 image(b)of the study area

    图  2   研究区ASTER影像(a)、Sentinel-1 VV影像(b)、DEM(c)和伽玛能谱钾含量(d)

    Figure  2.   ASTER image (a), Sentinel-1 VV image(b), DEM(c)and Gamma energy spectrum potassium content(d) of the study area

    图  3   1:5万研究区地质图(a)、基于遥感特征组合分类结果(b)、基于物探特征组合分类结果(c)和基于遥感和物探联合特征组合分类结果(d)

    Figure  3.   1:50000 geological map of the study area(a), classification result based on remote sensing features (b), based on geophysical exploration features (c) and based on joint remote sensing and geophysical exploration features (d)

    图  4   岩性分类特征重要性分析

    Figure  4.   Importance analysis of lithologic classification

    表  1   遥感与物探信息特征提取

    Table  1   Feature extraction from remote sensing and geophysical data

    特征类型 特征参数 数据源
    遥感信息特征 光谱特征 VNIR1-3, SWIR1-6, TIR1-5 ASTER
    波段比值 2/1, 4/3, 5/3, 5/4, 5/6, 5/3+1/2, 9/8, (4+6)/5, (5+7)/6, (7+9)/8
    主成分分析 PC1-PC9
    纹理特征 均值,方差,同质性,反差,差异性,熵,二阶矩,相关性 GF-2
    地形特征 TPI, TRI, Roughness DEM
    空间坐标信息 X, Y
    后向散射系数 VV, VH Sentinel-1
    物探信息特征 化极磁场 化极磁场值M 1:2.5万航空放射性测量
    伽玛能谱 U, Th, K与总道计数率(TC) 1:2.5万频率域航电测量
    比值特征 K/U, K/Th, U/Th
    电阻率 视电阻率值 1:2.5万航空磁测
    下载: 导出CSV

    表  2   岩性分类特征组合

    Table  2   Different feature combination for lithology classification

    组号 特征组合 特征个数/个
    A 遥感信息特征 48
    B 物探信息特征 10
    C 遥感信息和航空物探信息联合特征 58
    下载: 导出CSV

    表  3   岩性分类样本选取

    Table  3   Sample selection for lithology classification

    类别 训练样本/个 验证样本/个
    下石炭统干墩组第二岩段细粒岩屑砂岩(C1g2) 162 107
    石炭系下石炭统干墩组第三岩段凝灰质粉砂岩(C1g3) 1275 512
    早二叠世二红洼超单元灰白色中粒正长岩(P1) 213 135
    早二叠世二红洼超单元灰黑色细粒辉长岩(P1) 324 207
    早二叠世二红洼超单元灰白色中细粒闪长岩(P1) 171 117
    早二叠世二红洼超单元浅灰黑色中粗粒辉长闪长岩(P1Rvδ) 912 549
    中二叠世山口序列浅灰绿色细粒英云闪长岩(P2Sγo) 46 34
    中二叠世山口序列浅灰色中细粒花岗闪长岩(P2Sγδ) 152 145
    上更新统砾石(QP3pl) 1130 567
    全新统砾石(Qhpal) 161 232
    渐—中新统桃树园组砂岩(E3-N1)t 244 214
    石炭系苦水构造混杂岩(CK) 1540 498
    玄武岩(β) 597 301
    总数 6927 3618
    下载: 导出CSV

    表  4   不同特征组合岩性分类的精度对比

    Table  4   Accuracy comparison of lithology classification of different feature combinations

    组号 特征组合 RF
    总体精度/% Kappa/%
    A 遥感信息特征 70.95 67.06
    B 物探信息特征 65.48 60.81
    C 遥感和航空物探信息联合特征 80.29 77.74
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-27
  • 修回日期:  2020-11-28
  • 网络出版日期:  2023-08-15
  • 刊出日期:  2022-03-14

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