基于集合卡尔曼滤波的海洋土孔隙率预测研究

汪明元; 张国; 潘孙珏徐; 陶袁钦

doi:10.13204/j.gyjzG22052707

基于集合卡尔曼滤波的海洋土孔隙率预测研究

doi: 10.13204/j.gyjzG22052707

1. 中国电建集团华东勘测设计研究院有限公司, 杭州 311122;
2. 深圳市盛业建筑科技集团有限公司, 广东深圳 518026;
3. 浙江工业大学土木工程学院, 杭州 310014

详细信息

作者简介:
汪明元,男,1972年出生,博士,正高级工程师。

通讯作者:
陶袁钦,女,1995年出生,博士,taoyuanqin@zju.edu.cn。

计量
- 文章访问数: 114
- HTML全文浏览量: 16
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-27
- 网络出版日期: 2023-08-18

Prediction of Marine Soil Porosity Based on Ensemble Kalman Filters

1. Powerchina Huadong Engineering Co., Ltd., Hangzhou 311122, China;
2. Shenzhen Shengye Construction Technology Co., Ltd., Shenzhen 518026, China;
3. College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China

摘要

摘要: 正确评估海洋土的物理力学性质是保障海洋工程安全的关键。基于集合卡尔曼滤波结合波阻抗测量数据和波阻抗-孔隙率转换式,提出了一种海洋土孔隙率的概率预测及不确定性量化的方法。它可同时考虑转换模型和状态转移的不确定性,提供孔隙率沿深度的取值及其不确定性。首先基于先验信息生成海洋土孔隙率估计的初始集合;然后通过由多传感器岩心记录仪取样测量的波阻抗数据和概率转换模型,对海洋土孔隙率进行预测和更新;最后分析转换模型误差、初始集合和观测数据量对孔隙率估计的影响,通过工程实例的验证,表明该方法可有效地估计海洋土孔隙率随深度的空间分布,并量化不确定性。
- 概率分析 /
- 集合卡尔曼滤波 /
- 海洋土 /
- 孔隙率 /
- 波阻抗
Abstract: To estimate parameters of physical and mechanical properties of marine soil correctly plays a significant role in ensuring the safety of ocean engineering. A probabilistic prediction method was proposed, based on the Ensemble Kalman Filters and combined with the measured date of acoustic impectance and acoustic impedance-porosity transfer model, to predict the porosity of marine soil and quantify the relevant uncertainty of the transfer model and transfer states. First, an initial set representing the primary porosity estimation was generated based on the prior information. Then, the soil porosity along the depth was predicted and updated by combining the measurements of acoustic impedance obtained by multi-sensor core loggers and the probabilistic transfer model. Finally, the influence of errors of the transfer model, the initial set and the numbers of observation data on the prediction results were analyzed. An example was given to illustrate and verify the proposed method. The result indicated that the proposed method could effectively predict the distribution of soil porosity along the depth and reasonably quantify the relevant uncertainty.
- probabilistic analysis /
- Ensemble Kalman Filter /
- marine soil /
- porosity /
- acoustic impedance

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参考文献(26)

[1]	李典庆, 吕天健, 唐小松. 基于多维Gaussian Copula的岩土体设计参数概率转换模型构建方法[J]. 岩土工程学报, 2021,43(9):1592-1601.
[2]	唐小松, 李典庆, 周创兵, 等. 不完备概率信息条件下边坡可靠度分析方法[J]. 岩土工程学报, 2013,35(6):1027-1034.
[3]	张广文, 刘令瑶. 确定随机变量概率分布参数的推广Bayes法[J]. 岩土工程学报, 1995,17(3):91-94.
[4]	张博庭. 用有限比较法进行拟合优度检验[J]. 岩土工程学报, 1991,13(6):84-91.
[5]	LI D Q, T X S, ZHOU C B, et al. Characterization of uncertainty in probabilistic model using bootstrap method and its application to reliability of piles[J/OL]. Applied Mathematical Modelling, 2015,39(17)[2022-05-27]. https://doi.org/10.1016/j.apm.2015.03.027.
[6]	T X S, LI D Q, RONG G, et al. Impact of copula selection on geotechnical reliability under incomplete probability information[J/OL]. Computers and Geotechnics, 2013,49[2022-05-27]. https://doi.org/10.1016/j.compgeo.2012.12.002.
[7]	International Organization for Standardization(ISO).General Principles on Reliability for Structures: ISO2394:2015[S]. Geneva:ISO,2015.
[8]	毛华晋. 孔隙结构特征及其对岩石力学性能的影响分析[J]. 四川水泥, 2020(11):320-321.
[9]	RICHARDSON M D, BRIGGS K B. Empirical predictions of seafloor properties based on remotely measured sediment impedance[C]//AIP Conference Proceedings 728. 2004:12-21.
[10]	ENDLER M, ENDLER R, BOBERTZ B, et al. Linkage between acoustic parameters and seabed sediment properties in the south-western Baltic Sea[J]. Geo-Marine Letters, 2015,35(2):145-160.
[11]	PHOON K, KULHAWY F H. Characterization of geotechnical variability[J]. Canadian Geotechnical Journal, 1999,36(4): 612-624.
[12]	CHO S E, PARK H C. Effect of spatial variability of cross-correlated soil properties on bearing capacity of strip footing[J]. International Journal for Numerical and Analytical Methods in Geomechanics, 2010,34(1):1-26.
[13]	CAO Z J, WANG Y, LI D Q. Quantification of prior knowledge in geotechnical site characterization[J/OL]. Engineering Geology, 2016,203[2022-05-27]. https://doi.org/10.1016/j.enggeo.2015.08.018.
[14]	FENTON G A, GRIFFITHS D V. Risk Assessment in Geotechnical Engineering[M]. New York: John Wiley & Sons, Inc., 2008.
[15]	VANMARCKE E.随机场: 分析与综合[M]. 扩展版. 陈朝晖, 范文亮.译.北京: 高等教育出版社, 2017.
[16]	EVENSEN G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J]. Journal of Geophysical Research, 1994,99(C5):10143-10162.
[17]	陶袁钦, 孙宏磊, 蔡袁强. 考虑约束的贝叶斯概率反演方法[J]. 岩土工程学报, 2021,43(10):1878-1886.
[18]	JU L Y, MIAO C, CAO Z J, et al. Uncertainty quantification of soil total unit weight based on random field model and linear dynamic system: a comparative study//[C]. Geo-Congress 2020. 2020.
[19]	CHEN J, VISSINGA M, SHEN Y, et al. Machine learning:based digital integration of geotechnical and ultrahigh-frequency geophysical data for offshore site characterizations[J/OL]. Journal of geotechnical and geoenvironmental engineering, 2021,147(12) [2022-05-27].https://doi.org/10.1061/(asce)gt.1943-5606.0002702.
[20]	HOU Z Y, CHEN Z, WANG J Q, et al. Acoustic impedance properties of seafloor sediments off the coast of Southeastern Hainan[J]. Journal of Asian Earth Sciences, 2018,154(1):1-7.
[21]	Wang J, Guo C, Liu B, et al. Distribution of geoacoustic properties and related influencing factors of surface sediments in the southern South China Sea[J]. Marine Geophysical Research, 2016,37(4):1-12.
[22]	WANG X, WU S, LEE M, et al. Gas hydrate saturation from acoustic impedance and resistivity logs in the Shenhu area, South China Sea[J]. Marine & Petroleum Geology, 2011,28(9): 1625-1633.
[23]	LI G, WANG J, MENG X, et al. Relationships between the sound speed ratio and physical properties of surface sediments in the South Yellow Sea[J]. Acta Oceanologica Sinica, 2021,40(4):65-73.
[24]	SHUMWAY G. Sound speed and absorption studies of marine sediments by a resonance method:part II[J]. Geophysics, 1960,25(2):451-467.
[25]	郑栋. 土性参数概率模型的贝叶斯表征方法和边坡可靠度分析[D]. 武汉:武汉大学, 2018.
[26]	KUZIN D, YANG L, ISUPOVA O, et al. Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning[C]//Proceedings of 201821st International Conference on Information Fusion (FUSION). 2018.