PROGRESS OF LIPID BIOMARKER RESEARCH IN DEEP-SEA HYDROTHERMAL VENT AREAS
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摘要: 类脂物生物标志化合物(简称类脂物生标)记录了原始生物母质的分子组成信息,是研究极微生物群落、探索生命起源与演化的重要载体。综述了海底热液喷口区类脂物生标的主要研究进展,重点探讨烷烃、脂肪酸、醚类生标的应用及科学意义。类脂物生标研究表明,海底热液喷口区具有独特而又丰富的生物群落,喷口区动物群落与其他深海环境相似物种在饮食结构上有明显差别,它们主要捕食化能自养细菌或古细菌,且不同物种所捕食的微生物种类也具有选择性。为应对喷口区的极端物化环境,热液细菌和古菌形成了独特细胞分子组构。类脂物生标及单体同位素组成显示,喷口区存在嗜热型氨氧化和甲烷缺氧氧化古菌活动,在热液流体富H2的情况下硫酸盐还原菌与产甲烷古菌能够共存,碳限制条件下能产生富13C (-11.8‰~+3.6‰PDB)的类脂物生标。类脂物生标对于揭示热液微生物种群与营养关系、指示热液环境条件、反映热液区甲烷代谢作用以及探索生命起源与进化过程具有重要的研究意义,今后还需加强其应用范围及相关指示性机理等方面的研究。Abstract: Lipid biomarker bears important information of precursor molecular structure, and is an important media for exploring the biological community in extreme environment, the origin of life and the interaction and co-evolution between life and environment. In this paper, we summarized the recent progresses of lipid biomarkers research for hydrothermal vents, and discussed the application of hydrocarbon, fatty acid and ether biomarkers. The deep-sea hydrothermal vent support a rich and unique life community, and the hydrothermal animals select chemosynthetic microbes as their food. Lipid biomarkers and individual isotopic reflected the presence and activity of thermophilic anaerobic ammonium-oxidizing bacteria and methane-oxidizing archaea. High hydrogen concentrations allow the concurrent growth of methanogens and sulfate-reducing bacteria. Under carbon-limited conditions, lipids of microbe can be very rich in 13C(-11.8‰~+3.6‰PDB). Lipid biomarkers has good implication for microbial community characteristics, hydrothermal conditions, methane-metabolized function group and origin and evolution of life, and we proposed that the application range and indicative mechanism of lipid biomarker should be enhanced during the future research.
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Keywords:
- hydrothermal vent /
- lipid /
- biomarkers /
- geomicrobiology
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海底作为一个具有重要意义的地质界面，一直都是海洋科学研究的热点。海底底质的开发和利用在许多领域上都具有重要意义，特别是海洋军事[1]、海洋资源勘探[2]、水下考古[3]、海洋工程建设[4]、海洋渔业[5]等重要领域。传统的海底底质分类通常采用箱式取样、重力取样、抓斗等方式，按一定网格离散现场区域，通过室内测试分析后进行底质类型划分，但是该方式效率低，取样有限，作业成本高，所需时间长，且只能获取离散的海底底质点数据，需通过内插或外延的方式才能获得连续的底质分布。随着声学技术的不断发展，出现了多波束、侧扫声呐等一系列非接触式的声学底质探测方法[6-10]，不仅改善了作业效率，而且明显减少了投入成本。目前应用较多的声学探测系统有多波束、侧扫声呐、浅地层剖面仪等。这些方法基本上都是基于沉积物类型与散射强度、回波波形等物理量的相关性，进行相关改正后再进行特征提取和统计分析[11-12]。多波束和侧扫声呐通过采集多角度反向散射信号来获取大面积的海底底质信息，多波束的回波强度数据往往侧重于统计特征参量的分类，而侧扫声呐的回波强度数据更倾向于图像纹理分类[13]。然而，海底以下的沉积层中包含了很多可以表征底质特征的声学参数，如声阻抗、声衰减等，由于多波束和侧扫仅能穿透海底表面以下数厘米的深度，无法提取这些特征信息[14-16]。
浅地层剖面仪，使用的是低频、高能量的正入射信号，能穿透至浅地层数十乃至数百米深度，获取这一深度区间内的高分辨率垂直剖面资料，其回波中包含更多浅地层沉积物信息，可用较高置信度推断底质类型[17-18]。关于浅地层剖面的底质分类方法主要有3种：一是组合系统分类，将浅地层剖面与多波束或侧扫声呐相结合来识别不同的底质特征；二是基于模型的声学参数底质反演分类，Shock [19-20]在Biot-Stoll模型的基础上计算了快波波速和衰减系数来预测表层沉积物的类型，反演方法在计算连续较深的沉积层性质时被证明是可靠的。郑红波等 [21]利用Biot-Stoll模型反演海底沉积物的孔隙度和渗透率，并计算平均粒径实现底质分类，结果表明，Biot-Stoll模型适用于软质海底沉积物的分类；三是无模型的回波信号统计特征量底质自动分类，Yegireddi等[22]利用灰度共生矩阵统计数据进行浅地层特征识别和纹理特征向量提取，并选择一种名为自组织映射的无监督神经网络算法进行分类，成功从海底图像中分离出4种不同底质类型的沉积层。陈佳兵[23]等提取图像的相关系数、角二阶矩、同质性等6个特征向量，并提出将粒子群优化算法与BP神经网络相结合，通过优化BP神经网络的初始权值和阈值提高底质分类的精度。本文基于最近在舟山群岛采集的高密度高分辨率浅地层剖面测线，从处理后的浅地层数据中提取用于底质分类研究的关键参数，在此基础上，用无模型的回波信号统计特征量反演海底表层沉积物类型，并与高密度侧扫声呐数据解释的地貌类型和实测海底沉积物类型进行对比，分析该反演方法的准确率和可靠性，并绘制海底底质分布类型图，作为一种海底沉积物类型反演的新方法探索，为后期开展相关研究提供参考。
1. 区域背景
研究区主要位于舟山群岛海域，舟山群岛是浙东天台山脉向海延伸的余脉。在10～8 ka前，由于海平面上升将山体淹没才形成今天的岛群。古近纪和新近纪沿海及海岛地区全面隆起，处于剥蚀、侵蚀构造环境。进入第四纪，气候明显变冷，早更新世浙江沿海及海岛地区仍处于上升阶段，遭受构造侵蚀，形成了低山丘陵地貌。第四纪以来，伴随着海平面的多次升降，沉积了海相砂砾层和淤泥滩堆积[24-25]。
舟山群岛及其附近海域海流主要由东海沿岸流、长江冲淡水、台湾暖流等组成，季节性变化显著。受沿岸流影响，长江口入海泥沙经舟山群岛向东南搬运到水深小于60 m的内陆架区域。舟山群岛海域为典型往复流，岛屿间泥沙输运沿水道方向，潮流作用复杂，以峡道沉积作用为主，泥沙输运具有北进南出特征。已有研究表明，舟山群岛海域沉积物类型主要有5种，包括粉砂、砾质砂、砂质粉砂、粉砂质砂、砂，其中粉砂含量最高，呈片状广泛分布于舟山群岛东部宽阔海域[26-28]。
2. 材料与方法
2021年7—8月中国地质调查局烟台海岸带地质调查中心在舟山海域开展了1 100 km浅地层剖面和523 km侧扫声呐测量（图1），作业过程中导航定位采用美国Trimble公司产SPS351-DGPS差分信标接收机，CGCS2000坐标系，投影方式采用高斯克吕格6°带投影。
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图 1 研究区内浅地层剖面和侧扫声呐测线图Figure 1. Deployment of shallow seismic profiles and side scan sonar lines in the study area浅地层剖面采集仪器为英国应用声学公司生产的AAE型电火花浅地层剖面仪，测线间距1 km×2 km，震源为CSP-D（50-2400 J），水下声源Squid 2000，水听器为20单元组合检波水听器，频率响应范围为145~7 000 Hz，探测地层垂向分辨率优于0.5 m。通过试验确定的采集参数为：激发能量750 J，激发间隔800 ms，带通滤波100~5 000 Hz，电火花震源距离船尾30 m，水听器与电火花震源5 m，数据记录格式为SEGY，记录量程200 ms。
侧扫声呐采用美国Klein公司生产的Klein4900型数字式双频侧扫声呐，主测线平行等深线，联络测线垂直主测线，主测线间距350 m，测量分两个区，金塘海域主测线共30条，联络测线共11条；定海海域主测线共17条，联络测线共11条。试验取得的剖面以具有较高分辨率和良好的记录面貌为原则，最终确定的侧扫声呐工作参数为：455 kHz低频采集，量程200 m，TVG选择自动，后拖时拖缆放长15 m，船速保持在5节左右。实际作业时根据回波信号的强度及声图质量，适时调整船速、量程等施工参数，确保声图能够清楚地反映海底的地貌特征。
3. 浅地层剖面数据处理与地震振幅属性提取
浅地层剖面数据处理采用集成开发的运行在Windows平台的处理系统，在浅地层剖面数据处理方面，有针对性地编写了特有模块和算法，目前成熟的模块有：能量分析、频谱分析、频率域滤波、时变滤波、真振幅恢复、道间能量均衡、非相干及相干噪音压制、水体噪音压制、鬼波压制、海底多次波压制、涌浪改正、潮位改正、道坐标归算等。根据浅地层剖面特点，本次使用的模块包括频谱分析、频率扫描、频率域滤波、振幅恢复、能量均衡、层位平滑、多次波衰减、噪音衰减等，通过数据处理，压制了噪音和多次波，突出了有效波，提高了信噪比，并加强了层位连续性，方便后续属性数据的提取。
3.1 浅地层剖面数据处理
对原始SEGY数据进行前处理，包括滤波、真振幅恢复、振幅衰减补偿、振幅校正、振幅属性提取、多次波提取及反射系数计算等，方便后续属性数据的提取[29]。
3.1.1 滤波
通过对原始数据进行频率扫描，频谱分析等，大致确定数据资料的频率范围，以确定频率域滤波参数，通过分析对比，本次数据资料的有效频带范围大致在150～1 800，根据分析结果进而选择相应的滤波参数，滤波后高频和甚低频干扰噪音都得到了压制，同时也避免了噪音对后期海底振幅属性提取的干扰（图2）。
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图 2 带通滤波前（a）和滤波后（b）海底振幅属性对比Figure 2. Comparison of seafloor amplitude properties before (a) and after (b) bandpass filtering3.1.2 浅深层振幅分析及补偿
地震波在传播过程中，受波前扩散、大地滤波、吸收、散射、投射损失等多种因素影响，后处理过程中使用振幅恢复模块对地震波能量进行补偿和校正，以恢复较深层的弱反射能量，处理效果及补偿前后能量衰减对比见图3，从剖面图和能量曲线上可以看出，振幅补偿后深层能量得到有效恢复。
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图 3 振幅补偿前（左）和补偿后（右）剖面对比Figure 3. Amplitude compensation Profiles comparison before (left) and after (b) profiles comparison amplitude compensation3.1.3 水平能量均衡
外业采集过程中接收端能量往往受电缆沉放深度、震源深度、激发能量、海况等多种因素的影响，反映到资料剖面上，各道能量出现不均衡现象，同时也影响了海底反射能量，为减少这方面的影响，后处理过程中使用能量均衡模块，恢复因不同激发能量等因素引起的海底能量不一致性。通过互相关、能量匹配等方法对主要反射层位进行跟踪分析，采用拟合平滑局部层位以提高连续性、横向分辨率等。
3.1.4 多次波衰减
针对测区剖面上的短程多次波、海底多次波，采用预测反褶积模块对多次波进行衰减，特别是针对海底振幅能量有影响的鬼波，在提取能量前进行去鬼波处理（图4）。
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图 4 海底多次波处理前（左）和处理后（右）效果对比图Figure 4. Before (left) and after (right) seabed multiple multi-wave processing3.2 地震振幅属性提取分析
3.2.1 属性提取
通过浅地层剖面数据处理，对振幅进行校正后，先拾取海底反射（图5），再根据剖面判读反射特征与子波波形，推测实际地震子波长度大约为2 ms（图6），然后分别计算海底反射所在的波段和2 ms长度（下面简称区段）其对应的多个振幅属性，包括振幅最大值Max，振幅平均值Average及振幅均方根RMS等属性值。
3.2.2 异常振幅整理
对于异常振幅段要进行剔除，如震源无激发的记录道（图7），海底过浅以致海底反射受直达波影响的记录道，这类异常一般出现在测线开始或结尾处。
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图 7 测线3500—3670炮震源无激发记录Figure 7. Source record of no excitation from 3500 to the 3670 shot3.2.3 振幅解释分析成图
根据高密度高分辨率浅地层剖面数据提取的各振幅属性值，包括波段Max、波段Average、波段RMS、区段Max、区段Average、区段RMS，见图8。对各属性体采用克里金栅格化后形成的等值线如图9所示。振幅属性值越大对应海底沉积物越硬，反之，值越小对应海底沉积物越软。
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图 8 根据浅地层剖面数据提取的各振幅属性值a：波段Max，b：波段Average，c：波段RMS，d：区段Max，e：区段Average，f：区段RMS。Figure 8. Amplitude attribute values extracted from shallow seismic profilesa:Band Max, b: band Average, c:band RMS, d:section Max, e:section Average, f:section RMS.![]()
图 9 各振幅属性值克里金栅格化后等值线图a：波段Max，b：波段Average，c：波段RMS，d：区段Max，e：区段Average，f：区段RMS。Figure 9. Each amplitude properties values Kriegin rasterized contour mapContour map of each amplitude attribute value after Kriging rasterizationa: Band Max; b: band Average; c: band RMS; d: section Max; e: section Average; f: section RMS.4. 声呐数据解释
通过对所有侧扫声呐测线的地貌进行分析，发现测区范围内主要存在冲刷沟槽（潮道）和海底平原地貌类型。在冲刷沟槽中分布大量的浅埋基岩和出露基岩、沙波、岩石台地和滑坡体等（图10），海底平原地区发育大量沙波以及人类活动留下的痕迹等，其中人类活动留下的痕迹又包括拖痕区、采砂区、渔网等（图11），测区侧扫声呐数据解释获得的地貌分类及其分布见图12。
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图 10 侧扫声呐数据揭示的潮道底部出露的基岩高出海底近50 m。Figure 10. Bedrock outcrop at the bottom of the tidal channel revealed by side-scan sonar dataNearly 50 m above the sea floor.![]()
图 12 侧扫声呐数据解释的地貌分类及其分布Figure 12. Geomorphic classification and distribution interpreted by side scan sonar data4.1 基岩/风化壳
出露基岩在本次调查范围内主要有两种，基本分布在冲刷沟槽（潮道）底部和潮道边缘，一种是在声呐图像上主要表现为反射深浅相间在水深100 m左右，由于拖鱼距离海底较大，声呐反射成像较差，但是岩石纹理仍然清晰，此类型在本次调查范围内的冲沟底部大面积出露，另一种是出露基岩表现为海底高高突起（图10），在声呐图像上的表现为海底水深线剧烈起伏，垂直拖鱼航向上近拖鱼位置反射强，随后为阴影暗反射区，基岩/风化壳分布范围见图12中红色区域所示。
4.2 沙波
沙波一般是指浅水区河床中的泥沙质堆积地貌，在浅水区，水面受河床底部起伏影响呈波形，水流流速受上坡和下坡影响存在差异，进而导致沙波背水坡泥沙被侵蚀，而被侵蚀的泥沙会在下一个沙波的迎水坡堆积[30]。从平面上看，沙波的波峰大致互相平行，并与水流方向垂直或略显斜交。有时，它们呈时断时续的蛇曲形状或显弧形。测区范围内存在3处明显的沙波（图12中黄色范围），册子岛南边海域仅观察到少量沙波分布，估计是受挖沙影响，沙波沉积遭到破坏。大榭岛正北及东北海域的沙波，其沙波长达数百米，波高可达2～5 m（图11）。
5. 底质类型反演
5.1 多次波识别和海底反射系数计算
针对测区剖面上的海底多次波，采用预测反褶积模块对多次波进行求取（图13）。
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图 13 多次波提取前后剖面对比a：去多次波前，b：去多次波后，c：多次波。Figure 13. Profile comparison before and after multiplex extractiona: Before multiplex extraction; b: after multiplex extraction; v: the multiplex.提取多次波后，再利用去多次波模块计算获得反射系数，图14为计算得到的反射系数属性体图。值越大对应海底沉积物越硬，反之，值越小反映海底沉积物越软。
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图 14 研究区浅地层剖面测线反射系数属性体（上）及等值线图（下）Figure 14. Reflection coefficient properties (up) and contour map (down) of shallow seismic profiles由于测区范围内浅层气特别发育，除了基岩出露的部分测线段以外，几乎遍布整个测区，以致计算所获的反射系数整体偏高（图15）。
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图 15 测线反射系数（a）与RMS属性（b）对比图Figure 15. Comparison of reflection coefficient (a) and RMS attributes (b)5.2 底质类型反演及与实测数据的对比
结合基岩出露、侧扫声呐资料解释后的沉积分区（图12）进行对比分析，可以明显看出，振幅属性对底质的刻画，特别是潮道区，区段振幅属性要优于波段振幅属性，3个区段振幅属性整体上差别不大。再根据2015年收集的实测表层样资料[31-32]，进行综合对比（图16），并结合以往属性计算经验，最终采用区段RMS属性进行海底底质反演。为了方便对比，最终对区段RMS属性进行归一化处理。根据RMS属性值和粒度分析的相关关系，推测海底沉积物类型，研究区海底沉积物类型见图17，反演质量整体上较好。
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图 16 研究区实测表层沉积物类型及浅地层剖面区段振幅属性对比2015年实测沉积物类型：◆黏土质粉砂 ◆粉砂 ◆砂质粉砂。Figure 16. Comparison of measured surface sediment types and amplitude attributes of shallow seismic profiles in the study areaMeasured sediment types in 2015：◆clayey silt ◆silt ◆sandy silt.![]()
图 17 根据浅地层剖面RMS振幅属性反演的海底表层沉积物类型Figure 17. Seafloor surface sediment types derived from RMS amplitude attributes based on shallow seismic profiles部分推测区与表层样存在不符合的情况，研究区东北角反演推测的粉砂区，有2个黏土质粉砂表层样及2个砂质粉砂表层样落在此范围，1个砂质粉砂落在推测的黏土质粉砂范围内；另有桃花岛北边2个黏土质站位落在潮道边缘，推测为砂质区，全部29个站位中，其余22个站位（占总站位的72.41%）与推测的底质类型一致。
反演推测区与表层样存在不符合的情况，原因可能为：一是表层样取样时间是2015年，地球物理测线采集是2021年，期间相隔6年，舟山海区流速大、沉积物源丰富，水动力（波浪、恒流、潮汐等）强，都会引起局部沉积物的成分变化，对比相关海域已公开发表的资料，可以发现不同年份的取样其底质分析结果也存在些许差异[33]；二是研究区范围内浅层气特别发育，除基岩出露的区域外，浅层气几乎遍布其他区域，对沉积物类型反演有一定影响；三是受测线稀疏程度的影响，反演得到的海底底质分类的分辨率有限[34-35]。
6. 结论与建议
本文探索了一种利用高密度高分辨率浅地层剖面资料振幅属性反演海底表层沉积物类型的新方法，利用地震数据前处理、振幅提取等技术，提取了浅地层剖面波段Max、波段Average、波段RMS、区段Max、区段Average、区段RMS等多个振幅属性值，对比分析发现区段RMS属性可较准确地反演沉积物类型。利用最近获得的浅地层剖面数据振幅RMS属性值反演出舟山群岛的沉积物类型主要有黏土、黏土质粉砂、粉砂、砂和基岩5种类型，通过与侧扫声呐数据解释的地貌单位和实测海底表层沉积物类型数据对比，初步估算准确率在72%以上，该反演方法在研究区可行。
同时，该反演方法准确率受测线稀疏程度、数据原始采集质量等因素影响，因此结合本次资料处理及反演过程，为使后期提取的振幅属性更真实、多次波的计算更准确，在外业采集过程中应提高外业采集质量，保证记录长度超过多次波的到达时间在30 ms以上，尽量减小背景噪音，电缆沉放深度可以适当加大，可以减少水面噪音等。
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