Research Advancements in Surface Wave Exploration
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摘要:
面波勘探通过对提取面波的频散曲线等观测进而反演获得探测目标信息。面波勘探技术始于20世纪60年代，但近几十年来迅猛发展，已经被广泛应用在地震灾害及火山活动预测、深部地质结构、工程施工、矿区采空区、塌陷区探测以及星体近地表结构探测等方面。为了更全面了解面波勘探技术，本文从面波勘探技术的主动震源和被动震源这两种数据类型开始，回顾基于频散曲线和水平垂直振幅谱比观测的面波勘探技术基本理论，简要介绍面波反演方法，并介绍面波勘探技术的应用领域和发展趋势和前景。
Abstract:Surface wave exploration plays a vital role in obtaining target detection information by analyzing and retrieving the dispersion curve of surface waves. Although surface wave exploration technology originated in the 1960s, it has experienced significant advancements in recent decades and has found widespread applications in earthquake disaster and volcanic activity prediction, deep geological structure analysis, engineering construction, mining area and goaf assessment, subsidence area detection, and near-surface structure investigation, including celestial bodies such as stars. This paper aims to provide a comprehensive overview of surface wave exploration technology. It begins by discussing two types of data sources, namely active source and passive source, and proceeds to review the fundamental theory of surface wave exploration based on dispersion curve analysis and observations of horizontal and vertical amplitude ratios. This paper also provides a brief introduction to the surface wave inversion method. Additionally, it highlights the various application domains of surface wave exploration, outlines the current development trend, and presents future prospects for this technology.
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Keywords:
- surface wave exploration technology /
- dispersion curve /
- Rayleigh wave /
- HVSR
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胸主动脉瘤、胸主动脉夹层等，作为急诊诊疗中频繁出现的临床症状，其急性发作尤为常见。在胸主动脉疾病诊断过程中，主动脉CT血管成像（CT angiography，CTA）技术因其高效、准确的特性，现已成为明确诊断的首选方式[1]。然而，对于急危重症患者，因抢救时效性，胸主动脉CTA受限于患者的低配合度及自身不良血管条件（如接受长期放化疗后）等因素，使用小型号留置针（黄色24 G）与PICC管（非抗高压型）造成较低对比剂注射速率（≤1.5 mL/s），因此血管强化常显不足，对图像质量造成负面影响，不利于精准诊断。
以往针对血管强化不佳的问题，受限于技术条件，只能选择重复扫描，这不仅在紧急情况下延误了患者的救治时机，还增加了患者接受的辐射剂量和对比剂的用量。能谱扫描后重建的低能级图像能提高碘对比剂衰减值，从而提高CTA容错率，但随着图像能级的降低，噪声水平也呈现出显著上升的趋势[2]。
鉴于此，深度学习重建算法（deep learning image reconstruction，DLIR）作为一种新兴的重建算法，在常规扫描和能谱扫描（单能量图像，物质定量图像）的图像噪声抑制方面均展现出显著的改善效果[3-4]。因此，本研究旨在探索DLIR结合能谱单能量图像在改善胸主动脉造影图像强化不足方面的潜在能力，并评估其对图像质量的整体优化效果。
1. 资料与方法
1.1 研究对象
本文回顾性分析2016年1月至2023年12月福建医科大学附属协和医院急危重症且胸主动脉强化欠佳患者。
纳入标准。对比剂注射速率≤1.5 mL/s[5]且120 kVp-like图像上胸主动脉CT值≤250 HU[1]。使用低流速进行注射主要是为规避由于血管条件差（弹性降低、血管脆性增大）而引起的对比剂外渗风险，主要包括：①护士反馈生理盐水预冲时阻力较大只能使用 24 G密闭式静脉留置针或PICC管（非抗高压型）；②年龄>65岁且血管出现生理性退化；③接受放化疗超过 1年；④已知患有血管狭窄或闭塞。排除标准：①心血管畸形；②运动伪影较重。
最终纳入50例，男21例，女29例；年龄27～76岁，平均（54.42±11.16）岁；体重60～79 kg，平均（67.75±4.95）kg；身高153～179 cm，平均（164.90±0.06）cm。生理盐水预冲阻力较大患者24（48％）例，年龄>65岁且血管出现生理性退化4（8％）例，接受放化疗超过1年16（32％）例，患有血管狭窄或闭塞6（12％）例。
就诊原因：不明原因胸痛怀疑主动脉瘤或主动脉夹层。疾病诊断：主动脉瘤6（12％）例，主动脉夹层13（26％）例，主动脉炎11（22％）例，心功能不全4（8％）例，其他除血管外疾病16（32％）例。
1.2 扫描方案
采用GE HealthCare 256排Revolution CT行GSI模式扫描。患者头先进，仰卧位，双上肢上举置于头顶，深吸气后屏气。
管电压为80/140 kV瞬时切换，管电流采用GSI Assist模式，200～445 mA，噪声指数（noise index，NI）12。采用螺旋扫描，螺距0.992∶1，机架转速0.5 s/r，前置自适应迭代重建（adaptive statistical iterative reconstruction-V，ASIR-V）40％，扫描层厚5 mm，间距5 mm。
采用团注追踪法，当注射对比剂后，时间-密度曲线已达峰值且已呈现下降趋势但仍未达到触发阈值（200 HU），则立即手动触发扫描（纳入的所有患者均未达到触发阈值）。
1.3 造影剂注射方案
使用碘佛醇（350 mg/mL，非离子型碘对比剂），加热至37℃（降低对比剂的黏滞度，减小液体流动阻力）。使用高压注射系统经肘正中静脉注射，对比剂剂量为60 mL，注射速率≤1.5 mL/s，再以相同流速注射30 mL生理盐水。
1.4 重建参数
所有患者的影像数据均生成120 kVp-like图像，40、50和60 keV单能量图像。所有图像均分别进行ASIR-V 40％、DLIR-M与DLIR-H重建。所有重建层厚与层间距均为0.625 mm。
1.5 图像传输和分析
1.5.1 图像传输
将重建图像传至Advanced Workstation 4.7（GE HealthCare）工作站进行审阅。
1.5.2 客观数据分析
由一位具有5年以上诊断经验的放射科医师在对患者信息和重建信息均未知的情况下进行感兴趣区（region of interest，ROI）勾画和数据测量。
在降主动脉肺动脉分叉处血管中心区域选择ROI，面积大于测量血管面积的2/3，避开狭窄、斑块和血管壁；在同层面冈下肌肌肉均匀区域，勾画面积大于20 mm2的ROI，避开血管、脂肪和肩胛骨射线硬化伪影。测量并记录ROI的CT值和标准差（standard deviation，SD）。同时，挑选有硬化射束伪影的升主动脉血管，设置感兴趣区，面积大于测量血管面积的2/3；在相同层面，选取对侧无硬化射束伪影腋窝皮下脂肪组织设置感兴趣区，测量并记录两个ROI的SD值。
最后，计算图像噪声、信噪比（signal noise ratio，SNR）、对比噪声比（contrast noise ratio，CNR），硬化伪影指数值（beam hardening artefact，BHA），计算公式[6-7]：
$$ \left\{\begin{aligned} &\mathrm{图像噪声=冈下肌SD值} \\ &\mathrm{SNR=胸主动脉CT值/冈下肌SD值}\\ &\mathrm{CNR=(胸主动脉CT值-冈下肌CT值)/冈下肌SD值}\\ &\mathrm{BHA=(硬化射束伪影的升主动脉SD值}^{ \mathrm{2}}- \mathrm{脂肪SD值}^{ \mathrm{2}} )^{ \mathrm{1/2}} \end{aligned}\right.。 $$ (1) 1.5.3 主观图像质量分析
由两名从事放射诊断相关工作5年以上的放射科医生随机获取不同重建算法的患者图像进行诊断和图像质量评价。图像评价标准[8]：血管强化效果极差或极大噪声，血管边界及结构显示不清，图像质量极差，不能诊断，1分；血管强化效果较差或大量噪声，血管边界及结构显示较差，图像质量较差，影响诊断，2分；血管强化效果中等，有明显噪声，血管边界及结构显示中等，图像质量一般，可以诊断，3分；血管强化效果良好，有少许噪声，血管边界及结构显示良好，图像质量良好，便于诊断，4分；血管强化效果优秀，无明显噪声，血管边界及结构显示优秀，图像质量优秀，利于诊断，5分。
主观评分过程两位医师协商完成。患者所有重建图像默认窗宽350 HU，窗位30 HU，诊断医师在诊断过程中可自主调节窗口设置。
根据《主动脉夹层CT血管成像标注专家共识》[1]，当胸主动脉CT值≥250 HU且主观评分≥3分的图像认为符合诊断要求，补救成功。
1.6 统计学分析
采用Graphpad Prism 8.0和SPSS 22.0进行。计量资料以
$ (\bar{x}\pm s) $ 表示；分类资料采用例数（百分比）表示；组间分析采用Friedman检验并进行多重比较。P<0.05认为差异具有统计学意义。2. 结果
2.1 客观图像质量
在同一重建算法下，CT值变化趋势：40 keV>50 keV>60 keV>120 kVp-like图像。对于同一类型/能级，不同重建算法图像的胸主动脉CT值之间无统计学差异。SD值和BHA值：40 keV>50 keV>60 keV>120 kVp-like图像，ASIR-V 40％ >DLIR-M>DLIR-H。SNR和CNR值：40 keV>50 keV>60 keV>120 kVp-like图像。
不同能级的所有DLIR图像（DLIR-M/H）的SNR和CNR均高于ASIR-V图像（表1）。
表 1 不同重建算法120 kVp-like与单能级图像客观指标比较$ (\bar{x}\pm s) $ Table 1. The comparison of objective indicators$ (\bar{x}\pm s) $ between 120 kVp-like and monoenergetic images reconstructed with different algorithms重建参数 重建算法 统计检验 ASIR-V DLIR-M DLIR-H F P CT值 40 keV 375.2±87.08 367.9±84.63 367.6±84.31 4.84 0.089 50 keV 258.1±57.49 253.3±55.94 253.1±55.73 5.76 0.056 60 keV 186.2±39.58 182.9±38.57 182.8±38.45 4.00 0.135 120 kVp-like 141.6±28.82 139.2±28.13 139.1±28.04 2.56 0.278 统计检验 F 150.00 150.00 150.00 P <0.001 <0.001 <0.001 SD 40 keV 52.89±10.72 35.12±7.536 27.21±6.265 100.00 <0.001 50 keV 38.36±7.676 25.04±5.273 19.51±4.317 100.00 <0.001 60 keV 29.37±5.860 18.86±3.948 14.78±3.204 100.00 <0.001 120 kVp-like 23.84±4.704 15.12±3.127 11.93±2.518 100.00 <0.001 统计检验 F 150.00 150.00 150.00 P <0.001 <0.001 <0.001 SNR 40 keV 7.482±2.666 11.10±4.034 14.45±5.485 94.12 <0.001 50 keV 7.098±2.487 10.71±3.772 13.83±5.090 94.12 <0.001 60 keV 6.696±2.327 10.26±3.525 13.16±4.742 94.12 <0.001 120 kVp-like 6.273±2.156 9.727±3.270 12.39±4.365 98.04 <0.001 统计检验 F 150.00 114.20 127.00 P <0.001 <0.001 <0.001 CNR 40 keV 5.906±2.475 8.746±3.773 11.39±5.079 94.12 <0.001 50 keV 5.269±2.224 7.913±3.393 10.23±4.524 94.12 <0.001 60 keV 4.585±1.974 6.977±3.007 8.952±3.985 94.12 <0.001 120 kVp-like 3.884±1.711 5.962±2.609 7.593±3.422 94.12 <0.001 统计检验 F 150.00 150.00 150.00 P <0.001 <0.001 <0.001 BHA 40 keV 59.95±28.50 50.00±26.62 45.77±26.15 68.60 <0.001 50 keV 42.19±19.18 34.71±17.93 31.76±17.43 73.32 <0.001 60 keV 31.26±13.54 25.32±12.66 23.15±12.14 66.43 <0.001 120 kVp-like 24.56±10.07 19.56±9.465 17.89±8.866 66.43 <0.001 统计检验 F 141.00 141.00 141.00 P <0.001 <0.001 <0.001 2.2 主观评分
在不同重建算法下，同一能级图像主观评分：DLIR-H>DLIR-M>ASIR-V，差异有统计学意义。对于相同重建算法，不同能级主观评分：40 keV>50 keV>60 keV>120 kVp-like（表2和图1）。ASIR-V 120 kVp-like图像质量一般以上（≥3分）的图像占8％，DLIR-H图像中占18％；ASIR-V 40 keV图像质量一般以上占42％，DLIR-H 40 keV图像质量一般以上占100％。
表 2 不同重建算法120 kVp-like与单能级图像主观评分比较Table 2. The comparison of subjective scores between 120 kVp-like and monoenergetic images reconstructed with different algorithms重建参数 主观评分 1 2 3 4 5 40 keV ASIR-V 7（14.0％） 22（44.0％） 20（40.0％） 1（2.0％） 0 DLIR-M 0 1（2.0％） 22（44.0％） 20（40.0％） 7（14.0％） DLIR-H 0 0 6（12.0％） 19（38.0％） 25（50.0％） 统计检验 F 93.87 P <0.001 50 keV ASIR-V 2（4.0％） 20（40.0％） 19（38.0％） 9（18.0％） 0 DLIR-M 0 4（8.0％） 19（38.0％） 15（30.0％） 12（24.0％） DLIR-H 0 3（6.0％） 12（24.0％） 12（24.0％） 23（46.0％） 统计检验 F 82.62 P <0.001 60 keV ASIR-V 7（14.0％） 30（60.0％） 10（20.0％） 3（6.0％） 0 DLIR-M 4（8.0％） 24（48.0％） 10（20.0％） 8（16.0％） 4（8.0％） DLIR-H 4（8.0％） 19（38.0％） 14（28.0％） 7（14.0％） 6（12.0％） 统计检验 F 47.38 P <0.001 120 kVp-like ASIR-V 29（58.0％） 17（34.0％） 3（6.0％） 1（2.0％） 0 DLIR-M 26（52.0％） 15（30.0％） 5（10.0％） 4（8.0％） 0 DLIR-H 23（46.0％） 18（36.0％） 5（10.0％） 4（8.0％） 0 统计检验 F 23.29 P <0.001 ![]()
图 1 不同重建算法的120 kVp-like与单能级图像Figure 1. The 120 kVp-like and monoenergetic images reconstructed with different algorithms2.3 补救分析
50例患者的120 kVp-like图像（ASIR-V、DLIR-M、DLIR-H）均不满足诊断要求。通过生成更低能量图像和使用DLIR，均可获得补救成功的可诊断图像（CT≥250 HU且主观评分≥3分），其中通过40 keV-DLIR-H补救的病例50例，通过50 keV-DLIR-H/DLIR-M补救的病例22例，通过60 keV补救的病例3例（表3）。
表 3 补救成功的病例情况Table 3. The successful cases of remediation重建参数 ASIR-V DLIR-M DLIR-H 40 keV 21 49 50 50 keV 21 22 22 60 keV 3 3 3 120 kVp-like 0 0 0 3. 讨论
在CTA中，受限于患者自身血管情况，低对比剂流速场景并不少见。许多肿瘤患者因长期接受放化疗，血管内皮细胞受到损伤，血管弹性也随着减弱；或因高血压、高血脂、系统性红斑狼疮、感染性血管炎等原因，也可导致血管内皮损伤，常表现为血管狭窄或闭塞[9]。这类患者血管无法使用大型号封闭式静脉留置针（20 G以上），黄色24 G封闭式静脉留置针无法承受常规CTA所需高对比剂注射速率的压力（过高的注射速率可能导致留置针损坏造成对比剂外渗或血管破裂），根据指南[5]，注射速率不高于2 mL/s。因而为了患者安全，对于此类患者常常采用低对比剂注射速率。然而，低对比剂注射速率引起血管强化效果不佳从而影响诊断的问题也不容忽视。
此外，心功能不全或血液循环较差的患者，心输出量少，更加剧了这一现象，故本研究旨在探索DLIR结合能谱单能量图像在改善胸主动脉造影图像强化不足方面的潜在能力。结果表明：50例患者全部能通过40 keV DLIR-H获得补救图像（CT≥250 HU且主观评分≥3分），通过50 keV DLIR补救的病例数为22例，通过60 keV补救的病例3例。且DLIR比ASIR-V具有更高的补救潜力。
本文通过降低能级以达到提高组织间的对比度，优化强化欠佳的胸主动脉，提高了胸主动脉的CT值。但随着能级的降低图像噪声随之增加，导致图像质量下降，不能满足临床诊断要求。有研究[10]表明，自适应迭代重建技术（ASIR-V）能提高单能量重建血管造影的图像质量。
本研究的最佳能级为40 keV，与现有的主动脉最佳重建能量65 keV[11]有所差异，可能是因为本研究纳入的患者均采用低对比剂注射速率，造成胸主动脉内CT值较常规对比剂注射速率低，因此需要更低能级较高的碘衰减值进行补救。又因补救能级的进一步降低，ASIR-V图像已无法满足诊断需要的图像质量，本文从而利用深度学习重建算法，对低能级图像增加胸主动脉强化程度的同时降低图像噪声，进一步提高整体图像质量。
近年来，基于人工神经网络的深度学习重建算法在图像降噪方面已被广泛应用于CT图像重建，弥补了自适应迭代重建算法不足的同时提高了图像质量[12]。对比自适应迭代重建算法（ASIR-V）的重建图像，结果表明，在相同能级情况下，不同程度的深度学习算法的图像均较同层厚自适应迭代重建算法的图像噪声低、硬化伪影小、图像质量更高。目前，已有大量研究表明深度学习重建算法可提高传统CT成像的图像质量[13-14]。
此外，DLIR在维持图像空间分辨率和准确CT值的同时能降低图像噪声[15]。而在低kV研究上，DLIR明显降低了低kV下肢动脉CTA的噪声并且提高了图像质量[16]。有证据表明[17]，DLIR在40 keV图像较IR可明显提高胰腺癌的显示能力和图像质量。本文中，对于40 keV单能量重建图像，虽然CT值明显提高，但ASIR-V无法改善低能级带来的图像噪声，DLIR则可以重建出满足诊断要求的图像质量。
过高权重的ASIR-V可能造成图像失真[18]。本研究通过低能级（40 keV）结合DLIR，较高艳山等[6]的研究，补救强化欠佳的胸主动脉成功率更高，得益于低能级的高对比度与DLIR强降噪相结合。
CT硬化伪影通常是因为混合能量的X线束通过人体组织后，低能量X线被吸收，高能量X线穿透组织后，整体X线束变硬，在密度差异较大的组织临界区形成放射状或条带状的伪影[19]。在CT血管造影中，因对比剂的持续团注，高浓度碘对比剂在上腔静脉处因与临近组织形成巨大密度差异造成射线束硬化伪影（beam-hardening artifacts，BHA）[20]，严重影响临近主动脉根部和主动脉弓的观察，降低了图像质量。
有研究[7]显示，80 keV结合ASIR-V 50％ 的图像能有效解决射线硬化伪影。但因本研究的重建能级和ASIR-V权重均较其低下，故硬化伪影的优化差强人意。因此本研究利用单能量DLIR对高浓度对比剂在上腔静脉聚集引起的射线硬化伪影的优化进行探索，结果表明，DLIR较ASIR-V对射线硬化伪影的优化作用进一步提升。
本研究的局限性。首先，因此类患者数量较少，样本群体不够大，应进一步进行多中心、大样本研究；其次，对不同BMI患者的优化效果值得进一步研究；再次，因未与标准CTA进行比较，诊断效能有待考究；最后，单能量的重建间隔可以继续优化（10 keV可减小至5 keV）。
综上所述，因低对比剂注射速率造成强化效果欠佳的胸主动脉CT造影图像可通过能谱低能量图像结合深度学习重建算法进行补救。
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图 2 常用面波单点观测台阵示意图
Figure 2. Schematic representations of common surface wave single-point observation arrays
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图 3 三层速度递增水平层状介质模型地震记录及合成面波频散能量谱[24]
（a）合成Rayleigh波地震记录。频散能量谱：（b）$\tau-p $变换法（c）相移法（d）$f-k $变换法
Figure 3. Seismic record and synthetic surface wave dispersion energy spectrum of a three-layer horizontal layered media model with increasing velocity
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图 4 实验数据频散能量图[28]
Figure 4. Dispersion energy diagrams of Rayleigh wave data from the Changzhou suburb
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图 5 SPAC法提取频散曲线流程图[30]
Figure 5. Flow chart outlining the SPAC method for extractingdispersion curve
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图 6 折射微动法用于研究南加州纽霍尔消防站得到的结果图[31]
Figure 6. Application of the refraction micromotion method to study the Newhall Fire Station in Southern California
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图 7 模型不含噪声数据ACMPA与多种优化算法收敛曲线对比[75]
Figure 7. Comparison of convergence curves of ACMPA with those of other optimization algorithms; noise-free data from the model are used
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