三维地震属性参数在煤层厚度预测中的应用

依托“西部煤炭资源高精度三维地震勘探技术”项目工程,对晋城某矿南翼大巷东南区5m×5m×1ms的三维地震数据体,采用三维地震属性参数预测煤层厚度及其变化规律：沿3煤层、15煤层10ms时窗提取地震属性42种,根据钻孔资料,计算出煤厚与地震属性相关系数;从中优选出相关系数大于0．35的地震属性,其中3煤层9个、15煤层10个;然后进行地震属性互相关分析,优选出与3煤、15煤层厚度相关系数较大的4种属性,建立预测煤厚的BP神经网络模型,分别选取3煤层12个、15煤层4个实测数据作为学习训练和测试样本,以钻孔地震属性作为学习样本,对网络进行训练,最终获得全区煤层厚度。经与预留钻孔成果资料对比,预测精度较高,结果可用。     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS

In the context of tower measured radiation datasets.following the correction principle meetinga diagnostic equation in data quality control and in terms of a technique for model construction ondata and ANN(artificial neural network)retrieval for BP correction of radiation measurementswith rough errors available,a BP model is presented.Evidence suggests that the developed modelworks well and is superior to a convenient multivariate linear regression model,indicating its wideapplications.     

Application of genetic BP network to discriminating earthquakes and explosions

Introduction Artificial Neural Network (ANN) is an important branch of artificial intelligence. It is proposed on the foundation of the study on modern neural science, is a man-made network that can implement some functions based on the mans comprehensive understanding for cerebral neural network (HAN, WANG, 1997). ANN is a mathematical model of simplified human brain neural network and is used to simulate the structures and functions of human brain neural network. ANN is a complex netw…     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.     

Sedimentary cycles of trace elements in Salawusu River Valley since 150 ka BP

The paper makes some analyses on 11 trace elements in the Milanggouwan stratigraphical section in the Salawusu River valley, which is regarded as a prototype geology-palaeoclimate record since 150 ka BP. The results show that the content and variation of trace elements has experienced remarkably regular changes in the pace with coarse and fine sedimentary cycles of palaeo-aeolian sands to its overlying fluvio-lacustrine facies or/and palaeosols. The trace elements with chemical properties of relatively active (V, Sr, Cu, Ni, As) and relatively stable (P, Pb, Rb, Mn, Nb, Zr) are a manifestation of the corresponding 27 changeable cycles between peak and valley values, appearing a multi-fiuctuational process line of relative gathering and migration since then. The low numerical value distribution of these two types of trace elements in the aeolian sand facies represents erosion and accumulation under wind force during the cold-dry climate. Whereas their enrichments in both fluvio-lacustrine facies and palaeosols are related to the valley’s special low-lying physiognomic position between the Ordos Plateau and the Loess Plateau under the warm and humid climate conditions. The above relatively migrated and gathered change of the trace elements is the result of 27 climatic cycles of cold-dry and warm-humid, which is probably caused by repeated alternations of winter monsoon and summer monsoon in the Mu Us Sandy Land influenced by the climate vicissitudes in northern hemisphere during glacial and interglacial periods since 150 ka BP.     

基坑支护结构水平变形预测的遗传神经网络方法

采用遗传算法和误差反向传播算法相结合的混合算法来训练前馈人工神经网络,先用遗传学习算法进行全局训练,再用BP算法进行精确训练。就遗传算法过程中的选择、变异进行了探索,提出了用BP网络训练产生变异的遗传算法。作为实例,将该方法应用于预测基坑支护结构水平变形中。结果表明,该方法有收敛速度较快、预测精度高等优点。     

巴彦浩特盆地火成岩的综合地球物理解释

采用综合地球物理方法识别巴彦浩特盆地火成岩,首先利用高精度重磁资料和大地电磁测深资料,初步确定火成岩的平面位置和深度,然后利用钻遇火成岩的已知测井资料,建立人工合成地震记录,在地震剖面上标定出火成岩并分析其波组特征,采用BP神经网络进行火成岩的精细识别.研究表明,综合地球物理解释是提高反演解释精度的有效途径.     

6 000 a BP以来长江下游地区古洪水与气候变化关系初步研究

通过对埋藏古树、泥炭、以及海相贝壳测年资料进行搜集和整理，结果表明：长江下游地区6000 a BP以来古洪水的发生与气候变化有着密切的联系。由于长江下游地区地势低平这一地貌特点，使得海面变化对于研究区洪水发生有着重要的影响，气候变化导致的海面上升对长江下游河段径流的顶托作用导致河流上溯以及地面排水不畅，致使洪水发生频率加大以及洪水危害的程度加强，出现“小水大灾”的现象，长江三角洲地区古洪水发生频率与美洲地区古洪水发生频率的对比研究表明，长江三角地区乃至整个长江流域在大的气候变化趋势上与全球其它地区是相似的，既有全球气候变化特点的同时又具有区域响应的特点，这对于未来研究区洪水发生的预测有着重要意义。     

基于人工神经网络的赤潮卫星遥感方法研究

根据赤潮的卫星遥感探测机理，应用人工神经网络技术，建立和利用NOAA AVHRR可见光和热红外波段遥感数据的BP神经网络赤潮信息提取模型。应用实例显示。基于该人工神经网络方法可以提取赤潮发生地点和范围等信息，赤潮探测正确率达到78.5%。研究结果表明，应用人工神经网络方法提取赤潮信息是可行的。本文中建立的BP赤潮信息提取模型适当修改后可移植应用于其它传感器遥感数据进行赤潮信息提取。     

BP神经网络模型在重庆伏旱预测中的应用研究

采用气象要素定义伏旱指数，利用小波分析等方法分析重庆地区伏旱变化特征，最后采用BP神经网络模型对伏旱进行预测试验，结果表明，重庆伏旱变化具有明显的阶段性特征，而基于BP神经网络模型的伏旱预测模型预测效果良好，可以应用于实际预测。