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首页> 外文期刊>Hydrology and Earth System Sciences >Extended power-law scaling of heavy-tailed random air-permeability fields in fractured and sedimentary rocks
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Extended power-law scaling of heavy-tailed random air-permeability fields in fractured and sedimentary rocks

机译:裂隙和沉积岩中重尾随机透气度场的扩展幂律定标

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We analyze the scaling behaviors of two field-scale log permeability data sets showing heavy-tailed frequency distributions in three and two spatial dimensions, respectively. One set consists of 1-m scale pneumatic packer test data from six vertical and inclined boreholes spanning a decameters scale block of unsaturated fractured tuffs near Superior, Arizona, the other of pneumatic minipermeameter data measured at a spacing of 15 cm along three horizontal transects on a 21 m long and 6 m high outcrop of the Upper Cretaceous Straight Cliffs Formation, including lower-shoreface bioturbated and cross-bedded sandstone near Escalante, Utah. Order iq/i sample structure functions of each data set scale as a power iξ(q)/i of separation scale or lag, is/i, over limited ranges of is/i. A procedure known as extended self-similarity (ESS) extends this range to all lags and yields a nonlinear (concave) functional relationship between iξ(q)/i and iq/i. Whereas the literature tends to associate extended and nonlinear power-law scaling with multifractals or fractional Laplace motions, we have shown elsewhere that (a) ESS of data having a normal frequency distribution is theoretically consistent with (Gaussian) truncated (additive, self-affine, monofractal) fractional Brownian motion (tfBm), the latter being unique in predicting a breakdown in power-law scaling at small and large lags, and (b) nonlinear power-law scaling of data having either normal or heavy-tailed frequency distributions is consistent with samples from sub-Gaussian random fields or processes subordinated to tfBm or truncated fractional Gaussian noise (tfGn), stemming from lack of ergodicity which causes sample moments to scale differently than do their ensemble counterparts. Here we (i) demonstrate that the above two data sets are consistent with sub-Gaussian random fields subordinated to tfBm or tfGn and (ii) provide maximum likelihood estimates of parameters characterizing the corresponding Lévy stable subordinators and tfBm or tfGn functions.
机译:我们分析了两个现场规模的对数渗透率数据集的缩放行为,分别显示了在三个和两个空间维度上的重尾频率分布。一组包括1米尺度的气动封隔器测试数据,这些数据来自六个垂直和倾斜的钻孔,横跨亚利桑那州苏必利尔附近不饱和裂缝凝灰岩的十米刻度块,另一组气动微型渗透率数据沿三个水平样条以15厘米的间距测量上白垩统直悬崖组长21 m,高6 m的露头,包括犹他州埃斯卡兰特附近的下海岸面生物扰动和交叉层状砂岩。将每个数据集标度的 q 样本结构函数排序为有限范围内的分离标度或滞后数 s 的幂ξ(q)的 s 。称为扩展自相似(ESS)的过程将此范围扩展到所有滞后,并在ξ(q)和 q 之间产生非线性(凹形)函数关系。尽管文献倾向于将扩展和非线性幂律定标与多重分形或分数阶拉普拉斯运动相关联,但我们在其他地方已经表明:(a)具有正常频率分布的数据的ESS在理论上与(高斯)截断(加法,自仿射)一致(tfBm)分数布朗运动(tfBm),后者在预测小滞后和大滞后的幂律定标崩溃时是独一无二的,并且(b)具有正态或重尾频率分布的数据的非线性幂律定标是与来自次高斯随机场或从属于tfBm或截断的分数高斯噪声(tfGn)的过程中得出的样本一致,这是由于缺乏遍历性而导致的,样本矩的缩放比例与集合体的缩放比例不同。在这里,我们(i)证明上述两个数据集与从属于tfBm或tfGn的亚高斯随机字段一致,并且(ii)提供表征相应的Lévy稳定从属变量和tfBm或tfGn函数的参数的最大似然估计。

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