Reflection Seismic Data Processing by Use of Neural Network

提出了一种基于神经网络的反射地震数据处理算法。在各种数据处理技术中，神经网络算法被应用于初至信号的读取、地震道的波形识别和等速扫描结果的自动拾取。一个有正确答案的层次化网络，所谓的老师信号，在训练阶段采用误差反向传播算法。利用神经网络处理反射地震数据的一般过程如下：1)建立最适合于目标处理的网络；2)设置各层单元和教师信号的权值；3)通过激活网络来计算输出层的输出信号；4)根据实际输出信号和教师信号之间的误差能量估计学习信号。5)利用学习信号计算权值的变化，以使权值的变化最小6)重复步骤3)至5)，直到误差落入指定的限度或达到指定的学习计数。作为模型研究的结果，实验结果表明，该算法能够较好地实现初至信号的读取、波形识别和速度分析结果的自动提取，具有较高的精度。

The present reasearch proposed an algorithm for data processing of reflection seismic data using of neural networks. A neural network algorithm was applied to the reading of arrival time of first break signal, the recognition of waveform in seismic trace and the automatic picking of result of constant velocity scan among the various data processing techniques. A layred network with the correct answer, so called, teacher's signal, in training period by the error back propagation algorithm was used.The general procedure of processing of reflection seismic data by use of neural network is as follows.1) Constitution of the most suitable network for the target processing.2) Setting of the weight values to all units in the layrs and the teacher's signal.3) Calculation of the output signals from the output layr by activating the network.4) Estimation of learning signal from the energy of errors between the actual output signal and the teacher's signal.5) Calculation of the change of weight values by using learning signal so as to minimize the energy of errors between the actual signal and the teacher's signal.6) Steps 3) to 5) are repeated till the errors fall into the designated limitation or the designated learning count is reached.As a result of model studies, it was determined that the proposed algorithm performed the readings of arrival time of first break signal, the waveform recognition and the automatic picking of velocity analysis result with good accuracy.