煤层厚度

  • 网络coal-seam thickness;thickness of coal seam
煤层厚度煤层厚度
  1. 讨论了淮南煤田第四含煤段砂体的演化特征及其对13-1煤层厚度的影响。

    This paper discussed the evolvement characteristics of sand bodies of the fourth coal bearing interval and its influence on No. 13-1 coal-seam thickness in Huainan coal field .

  2. 利用灰色GM(1,1)模型,结合生产资料对磨心坡深部煤层厚度进行预测。

    Combining with the production data , grey GM ( 1,1 ) model is used to predict deep thickness of coal seam in Moxinpo .

  3. 地震多参数BP神经网络预测煤层厚度

    Forecasting coal layer thickness by BP neural network from multiple seismic parameters

  4. 煤层厚度的调谐作用对其AVO特征有明显的影响。

    It also shows that the tuning effect of coalbed thickness can impair its AVO response .

  5. 利用C++语言基于Windows操作环境开发了适用于煤田地震资料解释的煤层厚度辅助解释系统。

    The seismic coal thickness assistant interpretation system , which is suit for coal seismic data interpretation , is worked out by using C + + language on the base of Windows .

  6. 通过试验与分析,发现适当降低水煤气发生炉炉温与煤层厚度,有利于控制回收烟气中O2及CO的体积分数。

    It is found that the appropriate lowering of the chamber temperature of water gas producer and the thickness of coal bed is favorable to controlling the volume fraction of O_2 and CO in the recovered flue gas .

  7. 利用天然电磁波研制的DTY型地电探测仪,多年来一直在地面探查煤层厚度和附近小构造。

    The DTY type natural electromagnetic wave detector has been usually applied to operation on ground level to probe the thickness of coal beds and surrounding minor structures .

  8. 选取8组学习样本,利用4层BP(BackPropagation)人工神经网络模型,采用动量法和自适应调整的改进算法,训练BP网络,用训练好的BP网络预测煤层厚度。

    Eight groups of studying samples , made use of BP ( Back Propagation ) neural network of four layers improved by adopting momentum algorithm and self adaptive adjusting learning rate algorithm to train the BP neural network , and used the trained BP network to forecast coal layer thickness .

  9. 含气饱和度越大,AVO斜率的绝对值G越大。一维正演模型研究表明,煤层厚度变化能引起振幅的明显变化;而砂岩厚度增大或减小时,振幅变化不明显。

    The higher the gas saturation was , the bigger the absolute value | G | of AVO was with offset increases.1D forward modeling study shows that , thickness variation of the coal can cause to significantly change of amplitude , but the sand can not .

  10. 在煤层厚度0&20cm,总的γ辐射强度随厚度变化比较显著;而在20cm以上,则变化较为缓慢,但低能区γ强度仍有较显著变化。

    The total intensity of γ - rays decreases obviously with the thickness up to 20 cm , and slowly above the thickness : But the intensity for γ - rays in low energy region changes still rather obviously even for greater thickness .

  11. 煤层厚度6.0~7.0m时,这种方法的经济效益接近大采高及液压支架放顶煤采煤方法的相应指标。

    When seam thickness is 6.0 ~ 7.0 m , the economic profit of inclined slicing is close to those of caving method with extraction height and sub-level caving method .

  12. 陕北侏罗纪煤田煤层厚度变化范围大(0~14m),而其调谐厚度为8m,故以往基于薄层理论的煤厚解释方法对其不完全适用。

    Due to the variational thickness ( 0 ~ 14m ) and the tune-thickness ( only 8m ) of the coal layer in Jurassic coal field of Northern Shaanxi province , so the traditional method on coal layer thickness interpretation that based on folium theory is inapplicable .

  13. 定量预测煤层厚度的道积分法研究

    Study of the Trace Integration Method for Predicting Coal Seam Thickness

  14. 兖州济宁煤田煤层厚度探采对比

    Results comparison between exploration and mining in Yanzhou and Jining coalfield

  15. 大同煤田王村井田14-3煤层厚度变化原因分析

    Analysis on Thickness Change of 14-3 Coal Bed of Datong Coalfield

  16. 地震属性及其在煤层厚度预测中的应用

    Seismic Attributions Analysis And it 's Application in Predicting Thickness of Coal

  17. 煤层厚度变化地质成因分析

    Analysis on the Coal Seam Thickness Change and Geological Formation

  18. 基于地震属性的煤层厚度预测模型及其应用

    Prediction models of coal thickness based on seismic attributions and their applications

  19. 利用小波变换提高煤层厚度的分辨能力

    Using wavelet transform to enhance the ability of coal-thickness recognition

  20. 波阻抗反演在煤层厚度预测中的尝试

    Application of wave impedance inversion in coal seam thickness prediction

  21. 地震反射波检测煤层厚度的直接反演方法

    The direct inverse method of coal seam thickness detection by seismic reflected wave

  22. 并且通过对西山窑组中段煤层厚度和地层厚度的分析,对该区的聚煤规律和控煤因素进行了研究。

    Coal - accumulating patterns and controlling factors in the area were analyzed .

  23. 用综合地震特征参数解释煤层厚度

    Depth interpretation of coal beds using synthetic seismic parameter

  24. 应用人工神经网络解释煤层厚度

    Thickness interpretation of coal beds using artificial neural network

  25. 滤波处理提高道积分法反演煤层厚度的精度

    Filtering improving the accuracy of predicted coal seam thickness

  26. 平顶山二矿山西组沉积环境对煤层厚度的影响

    The influence of Shanxi Formation sedimentary environment in No.2 Coal Mine of Pingdingshan

  27. 兴安矿煤层厚度变化对生产的影响及处理方法

    Seam Thickness Change Effected on Production and Processing Methods in Xingan Coal Mine

  28. 波阻抗约束反演技术预测煤层厚度

    Exploration logging - constrained inversion technique predicting coal-thickness

  29. 煤层厚度大;

    The thickness of coal bed is large ;

  30. 煤层厚度局部变化区域地应力场分布的数值模拟

    Numerical simulation on stress field distribution in partial transformation area of coal seam height