Evaluation of Copper Mineral Resource Potential Using Concentration–Area Fractal Model and Fuzzy Evidence Weighting: A Case Study of the Jiurui Region in Jiangxi
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摘要:
中国江西省的九瑞地区是长江中下游成矿带中最重要的铜矿产地之一,其中花岗闪长斑岩与铜成矿关系密切。基于水系沉积物与矿化相关的信息,采用因子分析(FA)、浓度–面积分形法(C–A)和模糊证据权方法(FWofE)相结合建立成矿潜力预测模型。使用因子分析处理包含32个元素的255份水系沉积物样本数据,找到能够指示铜矿化的组合元素(即主因子)。采用多重分形反距离加权插值法(MIDW)创建主因子得分栅格图并用C–A分形模型提取与铜矿化相关的地化异常。将得到和铜矿化相关的地球化学异常图与地质、遥感解译数据相结合,应用模糊证据权方法建立预测模型。结果表明:已知铜矿床位于圈定预测概率高值区,且受花岗闪长斑岩和断裂的分布共同控制;除已知铜矿床区域外,圈定的3个一级远景区域内也具有较高的概率,值得进一步铜勘查找矿工作的进行。
Abstract:The Jiurui region in Jiangxi Province, China, is one of the most significant copper mining areas in the middle and lower reaches of the Yangtze River mineralization belt, with a close relationship between granodiorite porphyry and copper mineralization. In this study, a predictive model for mineralization potential was established by combining factor analysis (FA), concentration-area (C-A) fractal method, and fuzzy weight of evidence (FWofE) based on information related to stream sediment and mineralization. ϕfactor analysis was applied to a dataset of 255 stream sediment samples containing 32 elements to identify combinations of elements (principal factors) indicative of copper mineralization. κ the principal factor scores were interpolated using the multiple inverse distance weighted (MIDW) method to create a raster map, and the C-A fractal model was employed to extract geochemical anomalies associated with copper mineralization. λ the geochemical anomaly map related to copper mineralization was integrated with geological and remote sensing interpretation data, and a predictive model was established using the fuzzy weight of evidence method. The results indicated that: known copper deposits are located within high-probability zones defined by the model and are influenced by the distribution of granodiorite porphyry and faults; in addition to the known copper deposit areas, three primary prospective areas identified within the defined regions also exhibit a high probability, meriting further exploration efforts for copper prospecting.
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塔里木盆地发育大量板内中小尺度走滑断裂带,这些断裂带对奥陶系目的层碳酸盐岩储层发育、油气成藏和富集具有重要作用,是勘探评价的重要地质要素。由于盆地内走滑断裂带埋藏深度大多在7000 m以上,叠加复杂沙漠地表和地下多套火成岩覆盖等多重地震勘探影响因素,走滑断裂带成像精度低,难以满足预测描述和储层评价需求。
近年来,塔里木盆地北部顺北地区勘探接连取得了多个重大油气发现,勘探潜力巨大,勘探对象为断控碳酸盐岩缝洞型储集体,走滑断裂带是储集体发育的核心控制要素(鲁新便等,2015;焦方正,2018;漆立新,2020;李宗杰等,2020),那么,走滑断裂带精细描述和评价是油气勘探开发需要解决的关键问题之一。从三维地震资料看,7300 m以深奥陶系目的层地震主频低,不到20 Hz,有效频带窄,信噪比低,断裂带成像精度难以满足精细勘探评价需求。
笔者以顺北地区走滑断裂带三维地震资料为例,从资料保真保幅优化处理、频谱恢复提高分辨率、频谱分解、频率域断裂检测等叠后技术为核心,提高地震资料品质和走滑断裂带成像精度,为超深走滑断裂带描述和目标井位优化设计提供了高精度资料基础。
1. 频谱恢复地震特征增强处理成像技术
频谱恢复高清成像技术包括提高地震资料品质的道集优化处理技术、频谱恢复提高分辨率处理技术、频谱分解处理技术、频率域多尺度断裂检测技术(Qi J et al.,2013;Abbas et al.,2014)。
1.1 提高地震资料品质的道集优化处理技术
超深走滑断裂带精细刻画对地震资料精度要求非常高,原始地震资料一些小的噪音对预测结果都有很大影响。为了精细刻画超深走滑断裂带,主要通过双内核时变空变拉东滤波、倾角/方位角多道集优化边缘保持构造滤波等保幅保真优化处理,减少地震资料噪音,提高地震资料信噪比。
双内核时变空变拉东滤波技术:为了消除多次波以及在正常时差校正过程中产生的随机噪声,保留AVO效应以及一次反射的剩余时差,需要使用时变且保幅的滤波器。与传统拉东滤波器相比,双内核时变空变拉东滤波在拉东变换过程中同时考虑不同深度(时间)的变换和不同偏移距(空间)的变化,它具有两个优点,①使用了两套内核,能在振幅随炮检距变化过程中更好的捕捉AVO的变化。②双内核时变空变拉东滤波是滑动时窗的,它允许滤波参数随时间变化,在滤波中更有针对性。例如,在对道集上部进行轻微滤波的同时,可根据需要对道集底部进行强滤波,另外,可针对远、近偏移距地震道定义不同的滤波参数,更有效的进行滤波。
边缘保持构造滤波处理技术,边缘保持构造滤波方法主要用于改善地震数据道的信噪比。构造滤波通过空间滤波来抑制噪声,通过使用复杂一致性的方法保护断层和边界,增强地质界面的空间连续性。其结果是在不损失地质信息的前提下提高信噪比(图1)。
对于断裂发育地区地震资料,采用断层保持的构造滤波处理器,通过使用相邻地震道信息减少叠加地震道中的噪声,能在保持原地震道构造特征不变的情况下去掉噪声。该方法包含复杂的地震道混合过程,需要避免跨断层时的平滑问题。边缘保持构造滤波能对每个同相轴的几何结构进行识别,叠后地震资料、叠前地震道集均可以适用。
1.2 频谱恢复提高地震资料分辨率处理
每个物体都有自己的固有频率,地下地层同样也有自己的固有频率,称之为特征频率(奚先等,2005;张金伟等,2022)。频谱恢复提高分辨率技术就是对常规地震资料进行分频处理,获取包括薄层在内的所有地层频率信息,再利用频谱恢复方法从已经拾取的薄层的中低频信息,恢复高频信息,从而实现提高分辨率的目的。通过频谱恢复方法提高分辨率的原理(图2)。黑色带点曲线是一套厚地层的反射率谱曲线,蓝色曲线是一套薄地层的反射率谱曲线。厚地层的反射率谱曲线在常规地震资料频带范围内(图中2条红色虚线之间)为一完整周期函数曲线(图2),所以容易识别。薄地层的反射率谱曲线在常规地震资料频带范围内(红色虚线之间)只有不到四分之一部分(图2),无法识别。通过频谱恢复方法可以得到完整的薄地层反射率谱曲线,从而能识别薄地层。这种通过频谱恢复方法得到薄地层完整反射率谱曲线就能达到提高分辨率的目的。
1.3 频谱分解处理技术
频谱分解处理技术包括约束最小二乘法(陈珂磷等,2022)、粒子群匹配追踪法(蔡涵鹏等,2013;刘霞等,2015)、优化高斯频谱分解(刘汉卿等,2015)3种算法,在时间域和频率域同时具有较高分辨率。
笔者所用的频谱分解技术是通过约束最小二乘频谱分析法(CLSSA)实现的。与其他方法相比,CLSSA法是一个同时拥有高时间和频率分辨率的频谱分解方法(图3)。使用该方法能够将测井阻抗数据与低、中、高地震资料频带之间的互均衡子波分离出来。互均衡化作为一个反演流程执行,在与子波褶积时,宽频输出结果与输入数据拟合(最小二乘意义)。这个流程使用随时、空变化的子波,减少了超越传统时窗范围的频谱变化,输出的频谱在时间和空间上具有较好的稳定性。从不同频谱分解方法对比来看。最小二乘法频谱纵向分辨率高,而且和地震道能量对应紧密,频率域和时间域拖尾效应小(图3)。
1.4 频率域断裂检测技术
频率域断裂检测技术是在高分辨率频谱分解生成的一系列单频数据体基础上,求得相应的振幅体和相位体,对不同频率的振幅体和相位体进行边缘增强,识别不同频率上波形、振幅和相位等多种优选的不连续性属性,通过自适应主成分分析得到最终的断裂检测数据体。该技术是一种基于地震道频率分解技术及多种不连续属性对比分析技术的断层识别与检测技术,它的最大优势是增加了频率域的多种不连续属性信息,使地震反射中的不连续性得到更准确、清楚的反映和描述。频率域断裂检测技术能同时在平面和剖面上展示多尺度断裂系统,比相干(席桂梅等,2019)、曲率等传统断裂识别方法具有更高的分辨率和可靠性,断点辨识度更高,对高角度断层和小断层具有更强的识别能力(图4)。
2. 超深走滑断裂带地震特征增强处理效果分析
2.1 频谱恢复高分辨率处理效果分析
通过边缘保持构造滤波压制噪音,在滤波的同时保护断层和边界,既增强了地质界面的空间连续性,也在不损失地质构造信息的前提下大大提高了信噪比,为高分辨率处理提供优质基础数据。图1为过5号断裂带滤波优化处理前后剖面及残差,优化处理后,随机噪音和相干噪音得到了压制,地层反射结构和原始地震数据一致,断点、断面特征更清楚,地震资料品质得到了明显改善。
在保幅保真优化处理基础上,对顺北地区地震资料进行频谱恢复高分辨率处理。频谱恢复高分辨率处理前后的地震剖面效果对比图(图5),处理后的地震剖面无论是信噪比还是横向、纵向分辨率,都较原始地震剖面有较为明显的提高,层间信息丰富,目的层断裂特征更加明显,为后期开展断裂检测及缝洞体精细刻画提供较好的资料基础。目的层段处理前后的频谱曲线图(图6),可以看出,原始地震资料奥陶系目的层主频为20 Hz,频宽5~40 Hz,通过频谱恢复高分辨率处理后资料主频可达到约为30 Hz,有效频宽拓展到约为5~50 Hz。
2.2 频谱分解处理效果分析
根据顺北地区地震资料属性提取的断裂特征,采用最小二乘频谱分解法(CLSSA)技术对高分辨率地震数据进行频谱分解处理,提取不同频率的单频数据体(5 Hz、10 Hz、15 Hz、20 Hz、30 Hz、40 Hz、50 Hz),对比不同频率体断裂带检测效果。
提取T74层位向下0~50 ms均方根振幅属性平面图(图7a),从图中可以看出,5号断裂带在平面上表现为线性延伸或带状展布的特征,断裂带边界较易识别。同时,对比不同频率数据体剖面,发现不同频率数据体对断裂的识别效果有明显差别,低频数据体对大规模断裂识别效果较好,而对较小规模断裂识别效果不太明显,甚至无法识别。高频数据体较易识别主干断裂带内部断裂,对于全面刻画断裂带具有指导意义(图7b)。
3. 走滑断裂带频率域检测与精细描述
3.1 频率域检测
以高分辨率处理数据体为基础,以频率域断裂检测技术为核心,对5号断裂带中段地震资料进行断裂检测。
(1)首先分析高分辨率处理后地震资料频谱特征,以此为依据,应用复数域约束最小二乘法频谱分解技术(CLSSA)进行频谱分解,分解频带5~60 Hz,步长5 Hz,得到单频振幅体和相位体。
(2)定义时窗、选择属性体,对每个单频振幅体、相位体分别提取相干、曲率、方差、混沌等不连续属性体。
(3)采用主成分分析方法对不连续属性体进行PCA融合,得到断裂检测不连续性属性数据体。
从不同方法断裂带检测效果来看(图8),常规相干体包含一些脚印和沿层线性干扰,断裂聚焦性也不强,断裂识别精度低;频率域断裂检测数据体在去除脚印和沿层线性干扰的同时增强了地震数据的非连续性,断裂纵向线性特征更清晰、连续性更好,识别精度更高。
从频率域断裂检测结果与地震剖面叠合图来看,频率域断裂检测数据体不仅能清晰地刻画走滑断裂带的样式和剖面组合形态,还能清晰地展示断裂带内部细节,为合理准确解释断裂带不同断面和综合评价提供了有效指导。
3.2 走滑断裂带精细描述
基于频谱恢复高分辨率地震资料,通过精细层位标定,层位追踪解释,利用多尺度频率域断裂检测的成果,结合频谱分解、断裂属性等对不同级别断裂进行识别和平面组合,最后完成精细构造成图。
构造解释过程中,充分运用三维可视化技术,通过调整颜色、增益、透明度等各种参数,从多方位、多角度,点、线、面、体进行控制,实时对三维数据体内部的各类信息进行识别,对解释结果的合理性与可靠性进行检查质控。
主干断裂具有断距大、容易识别、剖面特征显著等特点。解释手段主要采用全三维可视化方法,借助5 Hz、10 Hz和15 Hz RGB融合体和频率域多尺度断裂检测地震数据体,大大提高了主干断裂解释精度和效率。
断裂带内部小断层,延伸长度普遍小于3 000 m,断距较小。高分辨率地震数据体目的层主频约30 Hz,地震波速度约为6000 m/s,T74界面同相轴时间厚度约为10 ms,对应厚度约为15 m。对于断距小于15 m小断层,地震反射主要表现为同相轴微小扭曲、变形,但剖面上显示的同相轴扭曲变形特征也可能是地层变形,而非断裂。因此,需要在解释过程中借助频率域多尺度断裂检测处理地震数据体及断裂密度等地震属性,充分结合断点、断层组合类型和发育样式等,利用三维可视化等技术进行精细解释。
优选频率域多尺度断裂检测处理地震数据体、断裂密集度属性体以及高分辨率瞬时振幅属性体进行RGB融合(图9),主干断裂带边界清晰,内部小断层可辨识,不同级别断裂带之间的关系也更加明朗。
4. 结论
(1)保幅保真优化处理可大大改善地震资料的品质,频谱恢复提高分辨率处理可有效拓展地震资料频带宽度,提高地震资料分辨率。
(2)频谱分解处理提升了地震资料对不同尺度断裂识别能力,低频资料对大尺度断裂刻画更清晰,中高频资料对小断层具有较好的识别能力。
(3)通过高、中、低频数据RGB融合可以为超深走滑断裂带解释描述提供可靠依据和指导。
(4)频谱恢复提高分辨率处理、频谱分解处理、频率域多尺度断裂检测技术组合是提高超深走滑断裂带预测精度的有效手段。
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图 1 长江中下游成矿带简易地质图(据Pan et al.,1999修)
Figure 1. Simplified geological map of the Mid-Lower Yangtze metallogenic belt
图 2 九瑞铜矿区地质图(据Yang et al.,2011修)
Figure 2. Geological map of the Jiurui region ore copper district
表 1 32种元素的检出限表
Table 1 Detection limits of 32 elements
序号 元素 检出限 序号 元素 检出限 1 Ag 0.01 17 Mo 0.5 2 As 2.82 18 Nb 5 3 Au 0.0003 19 Ni 5 4 B 5 20 P 30 5 Ba 10 21 Pb 5.4 6 Be 0.5 22 Sb 0.2 7 Bi 0.16 23 Sn 0.14 8 Cd 0.1 24 Sr 5 9 Co 1 25 Th 5.1 10 Cr 7.2 26 Ti 30 11 Cu 1 27 U 1 12 F 13 28 V 2 13 Hg 0.01 29 W 0.5 14 La 10 30 Y 10 15 Li 5 31 Zn 10 16 Mn 30 32 Zr 10 注:元素含量为10-6。 表 2 R型因子分析的正交旋转因子载荷矩阵表
Table 2 Orthometric rotating factor loading matrix for R-factor analysis
变量 因子载荷 F1 F2 F3 F4 F5 F6 F7 Ag 0.101 −0.031 0.954 −0.065 0.030 0.039 −0.062 As 0.059 0.329 −0.099 0.854 −0.039 −0.027 −0.203 Au −0.012 0.717 −0.041 0.007 −0.026 0.005 −0.069 B −0.303 −0.072 −0.021 −0.072 0.716 0.163 −0.104 Ba 0.101 −0.031 0.954 −0.065 0.030 0.039 −0.062 Be 0.910 −0.023 0.175 0.068 0.015 0.170 −0.016 Bi −0.025 0.941 −0.006 0.062 −0.017 −0.032 0.007 Cd 0.055 0.249 −0.098 0.917 −0.099 −0.014 0.102 Co 0.767 −0.006 0.093 0.435 0.224 −0.021 −0.112 Cr 0.831 0.001 0.007 −0.061 −0.013 0.082 0.036 Cu −0.089 0.786 0.022 0.324 −0.022 0.014 0.288 F 0.835 0.043 −0.057 −0.051 −0.087 0.102 0.123 Hg 0.063 0.221 0.023 0.196 0.100 0.173 −0.443 La 0.235 −0.093 0.068 −0.125 0.153 0.760 0.047 Li 0.890 −0.019 0.124 0.013 0.059 0.177 −0.038 Mn 0.489 0.005 0.065 0.328 0.198 0.016 −0.212 Mo −0.009 0.489 0.015 0.229 0.015 0.044 0.679 Nb 0.254 0.104 0.118 −0.045 0.611 0.123 0.029 Ni 0.875 −0.069 0.049 −0.070 −0.026 0.181 0.052 P 0.464 −0.049 0.417 0.025 −0.226 −0.218 0.100 Pb 0.075 0.918 −0.014 0.050 −0.002 −0.026 −0.157 Sb 0.048 0.926 −0.062 0.169 0.022 −0.059 −0.053 Sn 0.274 0.287 0.064 0.053 −0.094 0.572 0.068 Sr 0.209 0.058 0.736 −0.077 −0.411 −0.093 0.068 Th 0.332 −0.015 −0.241 0.010 0.603 −0.116 0.094 Ti 0.569 0.011 0.586 0.006 0.252 −0.002 −0.110 U 0.170 −0.022 −0.193 −0.058 0.073 0.089 0.289 V 0.931 0.042 0.146 0.049 0.036 0.134 0.032 W −0.077 0.657 0.171 0.144 0.064 −0.022 0.444 Y 0.108 −0.216 −0.181 0.031 0.152 0.739 −0.141 Zn 0.026 0.049 −0.017 0.959 −0.055 −0.023 0.041 Zr −0.570 −0.070 −0.166 −0.098 0.546 0.097 0.007 注:该因子分析采用的提取方法为主成分分析法,旋转方法为Kaiser标准化最大方差法,旋转在七次迭代后已经收敛。 表 3 各证据层隶属度表(MSF)及模糊证据权重计算表
Table 3 Table of membership of each evidence layer (MSF) and calculation of fuzzy weights of evidence
缓冲距离分类 主要赋矿地层 断裂 花岗闪长斑岩 绿泥石化蚀变 地球化学异常分类 C-A分形模型 分类值 缓冲
距离(m)隶属度 证据
权重隶属度 证据
权重隶属度 证据
权重隶属度 证据
权重分类值 C-A分形 隶属度 证据
权重1 100 1 0.60 1 0.37 1 4.06 1 2.05 1 高异常 1 2.06 2 200 1 0.60 1 0.37 0.67 3.46 1 2.05 2 异常 0.67 2.00 3 300 1 0.60 1 0.37 0.33 2.64 0.8 1.99 3 弱异常 0.33 1.83 4 400 1 0.60 1 0.37 0 0.69 0.6 1.91 4 背景 −0.37 −0.37 5 500 0.8 0.58 0.86 0.36 0 0.69 0.4 1.78 5 − − − 6 600 0.6 0.55 0.71 0.36 − − 0.2 1.56 6 − − − 7 700 0.4 0.38 0.57 0.35 − − 0 1.03 7 − − − 8 800 0.2 −0.03 0.43 0.34 − − 0 1.03 8 − − − 9 900 0 −0.03 0.29 0.32 − − − − 9 − − − 10 1000 0 − 0.14 0.30 − − − − 10 − − − 11 1100 − − 0 0.26 − − − − 11 − − − 12 1200 − − 0 0.26 − − − − 12 − − − 13 1300 − − 0 0.26 − − − − 13 − − − 注:“−”为空值。 -
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