地下水封洞库单裂隙花岗岩纵波速度变化规律与预测模型

曹洋兵; 吴阳; 张朋; 江志豪; 张思怡; 黄真萍

doi:10.19509/j.cnki.dzkq.tb20220279

地下水封洞库单裂隙花岗岩纵波速度变化规律与预测模型

doi: 10.19509/j.cnki.dzkq.tb20220279

曹洋兵^{1, 2,},
吴阳¹,
张朋³,
江志豪¹,
张思怡¹,
黄真萍^{1, 2}

1.
福州大学紫金地质与矿业学院, 福州 350108
2.
自然资源部丘陵山地地质灾害防治重点实验室 (福建省地质灾害重点实验室), 福州 350116
3.
万华化学集团股份有限公司, 山东烟台 264002

基金项目:

福建省自然科学基金项目 2023J01424

岩土钻掘与防护教育部工程研究中心开放基金项目 201702

自然资源部丘陵山地地质灾害防治重点实验室(福建省地质灾害重点实验室)开放基金项目 FJKLGH2022K002

贵州省地质矿产勘查开发局地质科研项目黔地矿科合[2020]1号

详细信息

作者简介:
曹洋兵(1987—), 男, 副教授, 主要从事岩体力学特性与稳定性评价工作。E-mail: ybcao@fzu.edu.cn

中图分类号: TU45
计量
- 文章访问数: 533
- PDF下载量: 51
- 被引次数: 0
出版历程
- 收稿日期: 2022-06-17
- 录用日期: 2022-07-27
- 修回日期: 2022-07-23

Prediction model and variation law of P-wave velocity of single fracture granite in an underground water-sealed storage cavern

1.
Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350108, China
2.
Key Laboratory of Geohazard Prevention of Hilly Mountains, Ministry of Natural Resources(Fujian Key Laboratory of Geohazard Prevention), Fuzhou 350116, China
3.
Wanhua Chemical Group Co., Yantai Shandong 264002, China

摘要

摘要:
揭示裂隙岩体纵波速度变化规律对工程岩体质量分级与稳定性评价具有重要意义。以某地下水封洞库无充填型单裂隙花岗岩为研究对象, 基于钻孔电视成像、水压致裂法地应力测试与声波全波列测井, 获取了384组单裂隙花岗岩的几何特性、受力状态与纵波速度, 构建起了预测单裂隙花岗岩纵波速度的进化-神经网络模型, 分析了关键指标影响下单裂隙花岗岩纵波速度的变化规律。研究表明: 该水封洞库单裂隙花岗岩纵波速度分布于4 300~5 330 m/s之间, 82.3%的纵波速度在4 700~5 200 m/s之间; 选取裂隙法向应力、平均张开度与倾角作为单裂隙花岗岩纵波速度的预测指标是合理可行的; 将现场测试数据分为训练样本与测试样本, 基于遗传算法优化神经网络权值、阈值的进化-神经网络模型构建出单裂隙花岗岩纵波速度预测模型, 其测试误差最大仅为2.9%, 85%的样本测试误差不超过1.5%, 预测模型精度较高。分析了纵波速度变化规律, 发现单裂隙花岗岩纵波速度随裂隙法向应力增大而增大, 但当法向应力增至5 MPa后的纵波速度增大速率逐渐减小, 纵波速度随裂隙张开度增大而逐渐减小, 纵波速度在裂隙倾角小于40°时无明显变化, 此后纵波速度随倾角增大而增大。
- 水封洞库 /
- 单裂隙花岗岩 /
- 纵波速度 /
- 神经网络模型 /
- 声波测试
Abstract: Objective
Revealing the variation behavior of P-wave velocity in fractured rock masses is of great significance for the quality grading and stability evaluation of rock masses for engineering purposes.
Methods
The nonfilling single fracture granite of an under ground water sealed storage cavern was taken as the research object. Based on borehole television images, hydraulic fracturing geostress tests, and ultrasonic full waveform logging, the geometric characteristics, stress state, and P-wave velocity of 384 groups of single fracture granites were obtained. An evolutionary neural network model for the prediction of the P-wave velocity of granite with a single fracture was constructed, and the variation behavior of the key indexes affecting the P-wave velocity of granite with a single fracture was analyzed.
Results
The study shows that the P-wave velocity of a single fracture granite in the water-sealed storage cavern is distributed around 4 300-5 330 m/s, and 82.3% of the P-wave velocity varies within 4 700-5 200 m/s. It is reasonable and feasible to select the fracture normal stress, average aperture, and dip angle as prediction indexes of the P-wave velocity of granite with a single fracture. The field test data sets are divided into training samples and test samples. The P-wave velocity prediction model of granite with a single fracture, based on the evolutionary neural network model, is constructed. The neural network weight and threshold are optimized by the genetic algorithm.The maximum test error of the prediction model is only 2.9%, and the test error of 85% of the samples is less than 1.5%.The prediction model thus yields high accuracy.
Conclusion
The variation feature of the P-wave velocity revealed that the P-wave velocity of granite with a single fracture increases with increasing normal stress on the fracture. However, the increase in the P-wave velocity decreases gradually when the normal stress increases to 5 MPa. The P-wave velocity decreases with an increasing fracture aperture. The P-wave velocity increases with an increasing dip angle. However, no difference occurred considering that the fracture dip angle is less than 40°.
- water-sealed storage cavern /
- single fracture granite /
- P-wave velocity /
- neural network model /
- ultrasonic test
注释:

(所有作者声明不存在利益冲突)

HTML全文

图 1 单裂隙花岗岩纵波速度频率分布直方图

Figure 1. Frequency histogram of the P-wave velocity in granite with a single fracture

下载: 全尺寸图片幻灯片

图 2 单裂隙岩体等效弹性模型示意图

F_n为法向力(N); t′为裂隙厚度(m)；σ_n为法向应力(Pa); A为裂隙接触面积(m²); K_n为裂隙法向刚度(Pa/m)；t₁, t₂分别为上、下岩块厚度(m)；E₁，E₂分别为上、下岩块弹性模量(Pa); k₁, k₂分别为上、下岩块等效弹簧刚度(N/m)；k′为裂隙的等效弹簧刚度(N/m)

Figure 2. Schematic diagram of the equivalent elastic model of a single fracture rock mass

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图 3 质点速度沿模型Z轴方向分布图

Figure 3. P-wave velocity distribution among the Z-axis of the model

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图 4 纵波速度随法向刚度变化规律

Figure 4. P-wave velocity varies with normal stiffness

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图 5 纵波速度随裂隙倾角的变化规律

Figure 5. P-wave velocity varies with fracture dip angle

下载: 全尺寸图片幻灯片

图 6 现场试验数据统计分析

a.不同法向应力区间的裂隙数量分布直方图; b.不同张开度区间的裂隙数量分布直方图; c.不同倾角区间的裂隙数量分布直方图

Figure 6. Statistical analysis of field test data

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图 7 进化-神经网络模型预测值与实测值对比

Figure 7. Comparison between the measured and predicted based on the evolutionary neural network model

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图 8 裂隙法向应力对纵波速度的影响

Figure 8. Variation feature related to obtained P-wave velocities derived from different fracture normal stresses

下载: 全尺寸图片幻灯片

图 9 裂隙张开度对纵波速度的影响规律

Figure 9. Variation feature related to obtained P-wave velocities derived from different fracture opening widths

下载: 全尺寸图片幻灯片

图 10 裂隙倾角对纵波速度的影响规律

Figure 10. Variation feature related to obtained P-wave velocities derived from different fracture dip anyles

下载: 全尺寸图片幻灯片

表 1 岩体物理力学参数

Table 1. Physical and mechanical parameters of the rock mass

材料	参数	取值
岩块	密度ρ/(kg·m^-3)	2 700
	动弹性模量E/GPa	30
	动泊松比μ	0.25
裂隙	法向刚度K_n/(GPa·m^-1)	15
裂隙	切向刚度K_s/(GPa·m^-1)	6

下载: 导出CSV

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