用人工神经网络计算薄层厚度

薄层厚度与薄层地震反射特征之间存在着复杂的关系,这种关系可通过一个3层人工神经网络来描述。选取地震道中时窗内的最大振幅值、振幅谱的最大值及对应的频率、自相关函数极大值与极小值之比、中心频率和低频能量这6个地震反射特征值作为网络的输入,将网络的输入值和输出值作适当的数值转换,就可以根据地震资料求出薄层的厚度。对模型进行试算,当地震资料的信噪比较高时,用文中的神经网络可正确地计算出薄层的厚度；用信噪比较高的样本道训练的神经网络也可正确地计算出信噪比较低的地震资料的薄层厚度。用该方法处理实际资料的效果令人满意。

Thin-bed thickness estimation using neural network

There is a complicated relation between thin-bed thickness and the corresponding seismic reflection characteristics, and the relation can be described by usingthree-layer neural network. We can derive thin-bed thickness from seismic data byinputting into the network 6 seismic characteristic values (the maximum amplitudevalue in time window, the maximum amplitude-spectrum value, frequency corresponding to the maximum amplitude-spectrum value,the ratio of the maximum autocorrelation-function value to the minimum one,the center frequency and low-frequency energy),and then by making appropriate conversions of both the input values and the output values in the neural network. In trial modeling computation,theneural network recommended here can be used to estimate correct thin-bed thickness when the signal/noise ratio of seismic data is high enough. But when the seismic signal/noise ratio is low,we can correctly estimate a thin-bed thickness by usingthe neural network trained with the use of the sample trace that has higher signal/noise ratio. This method has been used in real data processing to bring satisfactoryresult.