Enrichment and constraints of critical metals in ferromanganese crusts from 13°20'N seamount of the southern Kyushu-Palau Ridge
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摘要: 铁锰结壳富集Co、Cu、Mn、Ni、Ti、V、REE、Y和Zn等关键金属，研究其富集于结壳的规律以及相关地质环境制约因素对于未来开发利用这些海底金属资源十分重要。本文对九州-帕劳海脊南部13°20′N新发现的铁锰结壳样品进行了矿物学、元素地球化学和电子探针微区分析，发现其成分较为均一，未遭受明显的磷酸盐化作用，属于单层型水生成因结壳。Co、Ni等高含量关键金属主要富集在水羟锰矿内，其中主要以晶格态形式存在的Co所经历的表面氧化还原反应是其累积富集的关键；而Ni除了与Co一样通过置换Mn或占据晶格空位而呈现富集特征外，还大量以吸附态形式存在。Ti、V和REY等通过表面络合、晶格进入以及共沉淀作用富集在以六方纤铁矿为主的铁羟基氧化物组分内。Cu、Zn的晶格进入能力不足，加之海水Cu含量偏低，Zn的弱吸附作用共同导致它们以相对低含量形式分散分布。基于Co经验公式揭示结壳的形成起始于晚中新世，未出现明显生长间断，但持续生长时间不足导致结壳的关键金属累积富集程度低于全球主要结壳成矿区。不过，研究区理想的水深条件、较低的沉积速率、稳定的构造环境、合适的最小含氧带水深分布和远离非成矿物质的大规模稀释影响，都是本区结壳未来持续性增生和进一步富集关键金属的有利条件。Abstract: Ferromanganese crusts are highly enriched in a wide variety of critical metals including Co, Cu, Mn, Ni, Ti, V, REE, Y, and Zn. Study of their enrichment in the crusts and the geological constraints is important for future development and utilization of them at seafloor. Recently, ferromanganese crust samples were acquired from 13°20′N seamount of the southern Kyushu-Palau Ridge, and analyzed in mineralogy and element geochemistry, as well as for electron probe microanalysis. Results show that the mineralogical and chemical composition of the samples are relatively uniform, and the crusts have not suffered from obvious phosphatization, which indicates that the crusts are characterized by one hydrogenetic crustal layer. Critical metals with high content such as Co and Ni are mainly enriched in vernadite. Co mainly exists in the lattice of vernadite due mainly to surface oxidation of vernadite. Ni is enriched in the crusts by replacing and occupying lattice vacancies of Mn as Co does, and a large amount of Ni exists in the form of adsorption. Ti, V, and REY are enriched in the iron oxyhydroxide components dominated by feroxyhyte by surface complexation, crystal lattice entry, and co-precipitation. Cu and Zn are lack of crystal lattice entry ability; the Cu content in seawater is very low and the adsorption of Zn is weak, thus resulting in their dispersed distribution and low content in the samples. This study reveals that the crusts started growing in the late Miocene and show no obvious growth break; the accumulated enrichment degree of critical metals in these samples is lower than that in the highest potential areas of the global ocean due to insufficient continuous growth time. However, the ideal water depth conditions, low deposition rate, stable tectonic environment, suitable water depth distribution of the oxygen minimum zone, and long distance from macroscale input of the non-metallogenic material into the study area are favorable for continuous growth and enrichment of critical metals in these crusts in the future.
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Keywords:
- ferromanganese crusts /
- critical metals /
- enrichment principles /
- constraints /
- Kyushu-Palau Ridge
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1. 研究背景
在海洋资料中，多次波干扰非常发育并且种类也较多，有海水的鸣震、强海底尤其是崎岖海底产生的海底相关多次波、强反射界面产生的层间和长周期多次波等，这些多次波会造成地震记录中有效反射能量被压制，信噪比降低。因此，多次波的压制一直是海洋地震数据处理中的难点问题，也是海上资料处理的主要任务[1]。
深水海域地震资料数据处理是深水油气勘探的重要环节，其中多次波的压制又是重中之重，它直接影响到地震资料的品质，因此在偏移之前，尽可能地压制或衰减多次波。在深水海域，存在的多次波主要是自由表面多次波，该类多次波定义为地下介质反射的地震波到达自由表面后，至少发生一次下行反射，然后经一定传播路径后重新返回自由表面所接收的地震波[2-3]。可以说，在深水海域，如果能够压制自由表面多次波，也就压制了大部分的多次波干扰，因此自由表面多次波的压制是整个多次波压制的重点。针对此类多次波，学者们提出了很多压制的方法，有CMP叠加、f-k滤波法、Radon变换、聚束滤波法、预测反褶积和基于波动理论的多次波预测相减法等，其中目前最为广泛应用的是广义自由表面多次波预测技术（General-Surface Multiple Prediction，GSMP），相比于传统的二维自由表面多次波压制技术（Surface-Related Multiple Elimination，SRME），该技术预测的多次波模型更准确。同时，海上二维采集过程中电缆中—远偏移距难免受海流影响而偏离设计测线方向形成羽角，这是海上二维地震资料采集的固有特点。羽角的存在使共反射点发散无法满足SRME技术对规则化采集的要求，从而影响后续的多次波预测。因此，在本次多次波压制中，我们采用的是GSMP技术，但是在印度洋深水海域，海底相关多次波能量强，频带宽，常规的GSMP技术也不能得到很好的压制，因此，本文利用曲波变换，将多次波模型进一步优化，得到更加精确的多次波模型，从而使多次波的压制效果更好[4-9]。
2. 方法原理
2.1 广义自由表面多次波预测技术
广义自由表面多次波预测技术是近几年来逐渐兴起并广泛应用于海洋地震资料数据处理中的一项新技术。在理论上，该技术可以预测并衰减所有与地表相关的多次波，并且无需地下任何的先验信息，如速度、地层和构造等信息，是基于数据驱动的。广义自由表面多次波预测是通过模型建立和自适应减去法实现的，具体的实现途径为波动方程建模法，是在地表一致性褶积法的基础上进行改进的，通过波动方程外推来实现对多次波的模拟，该技术能适应任意观测系统，并且不受炮检点位置的约束。具体过程如下：首先对单炮数据进行时间反转，然后再向下外推，并与海底的反射系数进行褶积，再做向上的外推处理，最后完成整个单炮的多次波建模[6-8]。
2.2 曲波域多次波模型优化
广义自由表面多次波预测产生多次波模型，然后将地震数据和模型数据转换到曲波域，对多次波模型进一步优化，最后利用原始数据与多次波模型相减，对多次波进行压制。曲波变换使用的是第二代曲波变换，解决了第一代曲波变换大量数据冗余的问题，使曲波变换的实现更简单，运算效率更高。第二代曲波变换的公式为
$$ {\rm{c}}\left( {j,k,l} \right) = \left\langle {f,{{\rm{\varphi }}_{j,k,l}}} \right\rangle = \mathop \int \nolimits_{{R^2}}^{} f\left( x \right)\overline {{{\rm{\varphi }}_{j,k,{{l}}}}\left( x \right)} {\rm{d}}x $$ 其中，f（x）表示输入的原始地震信号或者多次波模型数据；φj,k,l为曲波函数，c（j,k,l）为曲波系数，其中j为尺度，l为方向，k为尺度j在l方向上的矩阵系数[10-13]。
具体的模型优化流程见图1，将地震数据和广义自由表面多次波预测产生的模型数据分为两部分，一部分是低频数据，一部分是高频数据，其中低频数据利用常规自适应减的方法得到低频多次波模型；高频数据动校后转换到曲波域，在曲波域中，比较不同尺度、不同角度的信号与多次波的振幅和相位差异（图2），具体的做法是：当信号与多次波的模型比较大于门槛值时，认为是信号，小于门槛值时，认为是多次波，依次来优化高频多次波模型，从而得到更加精确的多次波模型，再进行反动校（图3），最后用地震数据减去多次波模型，达到压制多次波的目的[14-17]。分高低频的主要原因是，在曲波域中，低频部分无法分角度和尺度对数据进行比较，见图4（分三个尺度）中Scale1，对低频模型无法进行优化，因此低频数据采用常规的自适应减，在高频数据中采用曲波变换对模型进行优化。高低频分界点的选取要稍大于Scale1的频率，低于Scale2的频率。
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图 4 曲波变换示意图ω为频率，KN为空间奈奎斯特频率，N为尺度。Figure 4. Schematic diagram of curvelet transformω is the frequency, KN is the space Nyquist frequency, N is the scale.3. 实例分析
选取印度洋某深水海域的地震资料，该地区海底地形总体较为平坦，最大水深为5258 m。从原始炮集（图5）上可以看出，多次波主要是海底相关的多次波，图6是有效波与多次波频谱图的对比，其中红色是有效波频谱图，蓝色是多次波的频谱图，从图中可以看出，多次波能量强，频带宽，与有效波频谱基本一致。首先利用常规的广义自由表面多次波压制方法对其压制，图7是利用广义自由表面多次波压制方法得到的多次波模型，图8是压制后的炮集，可以看出多次波压制不干净，仍有较多残留。图9是利用本文方法，分4个尺度进行曲波变换，计算Scale1的频率为15.75 Hz，因此本文将原始数据和模型数据以20 Hz为界分为高频数据和低频数据，低频数据利用常规的自适应减的方法优化低频多次波模型，高频数据转到曲波域，在曲波域中根据不同尺度不同角度的信号与多次波的振幅和相位差异来优化高频多次波模型，然后将低频模型和高频模型相加得到优化后的多次波模型。为了更清晰地比较优化前后的多次波模型，将原始炮集的多次波与优化前后的多次波模型放大并进行比较，图10可以明显地看出，由浅至深，优化后的多次波模型与原始炮集的多次波更吻合，多次波模型的精确度更高。最后利用原始数据直接减去多次波模型，得到压制后的炮集，可以看出压制后炮集更干净，信噪比更高（图11）[18-21]。
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图 6 有效波与多次波频谱图对比红色是有效波频谱，蓝色是多次波频谱。Figure 6. The spectrum of the effective wave compared with that of the multiple wavewhere red is the spectrum of the effective wave and blue is the spectrum of the multiple wave.![]()
图 10 多次波模型对比图从左到右依次为：原始数据多次波，常规方法得到的多次波模型，曲波域优化后的多次波模型。Figure 10. Multiples model comparison chartFrom left to right: multiples of raw data, multiples model obtained by conventional method, multiples model obtained by curvelet transform.![]()
图 11 利用优化后模型多次波压制效果Figure 11. Suppression of multiple waves using the optimized model下面从叠加剖面上看常规方法和本文方法的压制效果。选取印度洋该深水海域两条测线，图12是A测线原始剖面，图13是利用常规方法压制后的效果，可以看出压制效果不理想，多次波残留较为严重（图中箭头所指的地方）；图14 是利用本文方法压制后的效果，可以看出，压制效果较好，多次波去除的较为干净，剖面信噪比高，并且未损害有效信号，时间10.2 s的位置波组特征更加清晰，有利于后期地震资料的偏移和解释[22-25]。图15—17是B测线的原始剖面及利用常规方法和本文方法压制后的效果图，同样可以看出，利用本文方法压制多次波的效果更好，压制后的剖面信噪比更高，说明本文方法更适用于深水海域海底相关多次波的压制。
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图 13 A测线常规方法压制后的叠加剖面Figure 13. The superimposed profile after conventional method of line A![]()
图 14 A测线利用曲波域优化模型压制的叠加剖面Figure 14. The stacked profile after optimization model in curved wave domain of line A![]()
图 17 B测线利用曲波域优化模型压制后的叠加剖面Figure 17. The stacked profile after optimization model in curved wave domain of line B![]()
图 16 B测线常规方法压制后的叠加剖面Figure 16. The superimposed profile after conventional method of line B4. 结论
本文通过在实际资料中的应用可以看出，多次波的压制效果较好，剖面的信噪比得到了较大的提高，同时压制后有效信号得到了凸显，波组特征更加清晰，有利于后期层位的识别和追踪。
该技术适用于海底地形较为平坦的深水海域，同时值得注意的是，本文方法在曲波域中对高频模型进行优化时，是根据信号和模型数据在不同尺度、不同角度上的振幅和相位差异，即当信号与多次波的模型比大于门槛值时，认为是信号，小于门槛值时，认为是多次波，因此门槛值的选择非常重要，直接决定优化后模型的精确度。门槛值的选择是选取有代表性的炮集，计算不同尺度、不同角度的振幅和相位差异，从而确定门槛值。
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图 3 结壳样品X射线衍射图谱
Cal：方解石，Fel：长石，Fer：六方纤铁矿，Q：石英，Ver：水羟锰矿。
Figure 3. Pattern of X-ray diffraction of the ferromanganese crusts
Cal: calcite, Fel: feldspar, Fer: feroxyhyte, Q: quartz, Ver: vernadite.
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图 4 结壳样品REY的PAAS标准化配分模式
为便于显示，将海水的REY值扩大106倍；PAAS的REY含量引自文献[20]。海水的REY含量数据选择与本研究区临近且水深层位相近的海水的值，其中REE数据引自文献[21]，采样区域为本研究区东面的西太平洋，水深2000 m；Y数据引自文献[22]，采样区域为西南太平洋东加罗林海盆，水深1980 m。
Figure 4. Shale normalized rare earth elements and yttrium contents of the ferromanganese crusts
To facilitate the display in the diagram, the REY contents of the seawater are expanded by 106 times; PAAS data are from the reference [20]. The REY data of the seawater is from the reference [21], the sampling area with the water depth of 2000 m is in the western Pacific Ocean close the study area, which is similar to the distribution depth of the samples in this paper. Y data of the seawater is from the reference [22]. The sampling area was in the east Caroline Basin of the southwest Pacific Ocean, in water depth of 1980 m.
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图 6 本文研究区与全球结壳主要成矿区内样品的关键金属平均含量对比
PCZ、NPCZ、南太平洋、印度洋和大西洋铁锰结壳样品的成分数据引自文献[6]。
Figure 6. Mean contents of the critical metals in the ferromanganese crusts from the research area and the highest potential areas of the global ocean
The contents of the critical metals in the ferromanganese crusts of the PCZ, NPCZ, South Pacific, Indian, and Atlantic Ocean are from the reference[6].
表 1 结壳样品元素含量特征
Table 1 Chemical composition of the ferromanganese crusts
元素 外层 中间层 内层 基质 Al/% 1.58 1.69 1.98 7.97 Ca/% 2.31 2.36 2.41 4.26 Fe/% 17.70 18.78 18.52 11.20 Mn/% 20.68 19.44 18.67 1.35 P/% 0.23 0.23 0.22 0.09 Si/% 5.92 6.47 7.01 21.29 Ti/% 0.94 1.08 1.03 0.76 Ce/10−6 692 764 717 94 Co/10−6 3400 3220 3090 156 Cu/10−6 1120 853 927 373 Ni/10−6 3400 2620 2720 291 V/10−6 547 515 491 261 Zn/10−6 512 463 491 276 LREY/10−6 1230 1322 1202 173 HREY/10−6 274 258 229 60 REY/10−6 1503 1579 1431 233 
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表 2 样品不同层位铁锰氧化物的电子探针微区成分数据
Table 2 Element contents in the ferromanganese oxides layers from different parts of the sample revealed in electron probe microanalysis
元素 Al Ca Ce Co Cu Fe Mn Ni P Si Ti V Zn 外层
(n=13)最大值/% 1.62 3.04 0.16 0.73 0.18 24.37 28.61 1.10 0.45 3.70 1.32 0.13 0.10 最小值/% 0.43 1.62 0.05 0.24 0.09 16.84 22.98 0.42 0.30 2.07 0.64 0.07 0.05 平均值/% 0.64 2.47 0.13 0.59 0.14 19.25 26.81 0.65 0.36 2.62 1.16 0.09 0.08 离散系数/% 45.98 16.64 21.87 23.60 19.89 10.58 6.06 24.58 12.97 17.90 14.80 17.29 17.38 中间层
(n=20)最大值/% 1.22 2.89 0.16 0.74 0.22 24.81 32.27 0.82 0.47 4.93 1.38 0.12 0.11 最小值/% 0.34 2.05 0.09 0.26 0.07 17.01 22.72 0.40 0.30 1.59 1.00 0.07 0.01 平均值/% 0.67 2.54 0.13 0.50 0.14 21.06 26.50 0.58 0.38 2.92 1.21 0.10 0.07 离散系数/% 29.34 7.88 14.57 25.34 24.59 11.22 9.67 23.00 14.48 26.75 8.97 13.66 33.77 内层
(n=20)最大值/% 1.77 2.61 0.19 0.75 0.20 35.47 30.66 0.75 0.48 6.40 3.14 0.14 0.14 最小值/% 0.49 1.05 0.10 0.20 0.08 16.36 11.02 0.15 0.28 1.88 1.13 0.07 0.06 平均值/% 0.85 2.24 0.15 0.42 0.13 23.71 23.83 0.43 0.39 3.62 1.44 0.11 0.09 离散系数/% 41.40 17.22 16.12 27.84 22.63 15.34 15.92 29.60 13.75 25.83 29.81 15.91 25.82
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表 3 微区铁锰氧化物纹层内各元素间的相关系数矩阵（n=53）
Table 3 Pearson correlation coefficient matrix for major and valuable metal elements contained in the ferromanganese oxide layers (n=53)
Al Ca Ce Co Cu Fe Mn Ni P Si Ti V Ca −0.78 Ce 0.12 −0.07 Co −0.67 0.57 −0.21 Cu −0.01 0.18 −0.13 −0.04 Fe 0.49 −0.43 0.60 −0.69 −0.06 Mn −0.72 0.67 −0.41 0.75 0.06 −0.90 Ni −0.48 0.45 −0.45 0.65 0.13 −0.82 0.77 P 0.09 0.08 0.35 −0.47 −0.01 0.68 −0.52 −0.64 Si 0.78 −0.64 0.38 −0.72 −0.04 0.87 −0.94 −0.76 0.52 Ti 0.53 −0.55 0.53 −0.37 −0.04 0.76 −0.76 −0.55 0.18 0.72 V −0.03 0.06 0.41 −0.30 0.11 0.68 −0.49 −0.50 0.63 0.46 0.40 Zn 0.17 −0.10 0.12 −0.45 0.15 0.44 −0.39 −0.47 0.50 0.40 0.12 0.45
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