BP神经网络在随钻超声成像测井数据处理中的方法研究

Backpropagation neural network method in data processing of ultrasonic imaging logging-while-drilling

采用现有方法提取超声回波的到时和幅度不能有效避免随钻超声信号的局部干扰,导致随钻超声成像测井仪在井下自主提取的回波到时和幅度精度较差。本文采用解调算法对随钻超声模拟信号进行预处理,然后设计BP神经网络模型来拟合波形数据与幅度和到时之间的关系。本文建立超声成像测井模型,利用有限元仿真软件进行正演模拟,通过改变模型参数来模拟不同的测量条件时的响应,将其作为神经网络模型的输入层;将超声回波信号看作是由高频载波信号调制而成的低频信号,设计低通滤波器将高频信号去除,得到低频包络信号;随后提取包络信号的幅度及其对应的时间作为人工神经网络模型的输出层。对比了6种训练方法的应用效果,认为共轭梯度下降法最适合用于求解该神经网络模型。通过11组模拟测试数据对该神经网络的性能进行了测试,验证了该模型的有效性,为进一步实际应用奠定了基础。

The existing methods for extracting the arrival time and amplitude of ultrasonic echo cannot eff ectively avoid the local interference of ultrasonic signals while drilling,which leads to poor accuracy of the echo arrival time and amplitude extracted by an ultrasonic imaging logging-while-drilling tool.In this study,a demodulation algorithm is used to preprocess the ultrasonic simulation signals while drilling,and we design a backpropagation neural network model to fit the relationship between the waveform data and time and amplitude.An ultrasonic imaging logging model is established,and the finite element simulation software is used for forward modeling.The response under diff erent measurement conditions is simulated by changing the model parameters,which are used as the input layer of the neural network model;The ultrasonic echo signal is considered as a low-frequency signal modulated by a high-frequency carrier signal,and a low-pass fi lter is designed to remove the high-frequency signal and obtain the low-frequency envelope signal.Then the amplitude of the envelope signal and its corresponding time are extracted as an output layer of the neural network model.By comparing the application eff ects of the various training methods,we fi nd that the conjugate gradient descent method is the most suitable method for solving the neural network model.The performance of the neural network model is tested using 11 groups of simulation test data,which verify the eff ectiveness of the model and lay the foundation for further practical application.