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Inversion of Density Interfaces Using the Pseudo-Backpropagation Neural Network Method

机译：使用伪抛弃神经网络方法反演密度接口

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摘要

This paper presents a new pseudo-backpropagation (BP) neural network method that can invert multi-density interfaces at one time. The new method is based on the conventional forward modeling and inverse modeling theories in addition to conventional pseudo-BP neural network arithmetic. A 3D inversion model for gravity anomalies of multi-density interfaces using the pseudo-BP neural network method is constructed after analyzing the structure and function of the artificial neural network. The corresponding iterative inverse formula of the space field is presented at the same time. Based on trials of gravity anomalies and density noise, the influence of the two kinds of noise on the inverse result is discussed and the scale of noise requested for the stability of the arithmetic is analyzed. The effects of the initial model on the reduction of the ambiguity of the result and improvement of the precision of inversion are discussed. The correctness and validity of the method were verified by the 3D model of the three interfaces. 3D inversion was performed on the observed gravity anomaly data of the Okinawa trough using the program presented herein. The Tertiary basement and Moho depth were obtained from the inversion results, which also testify the adaptability of the method. This study has made a useful attempt for the inversion of gravity density interfaces.
机译：本文介绍了一种新的伪逆产（BP）神经网络方法，可以一次反转多密度接口。除了传统的伪BP神经网络算术之外，新方法还基于传统的前向建模和反向建模理论。在分析人工神经网络的结构和功能之后，构建了使用伪BP神经网络方法的多密度接口的重力异常的3D反转模型。相应的空间场的相应迭代逆公式同时呈现。基于重力异常和密度噪声的试验，讨论了两种噪声对逆效应的影响，分析了对算术稳定性的噪声规模。讨论了初始模型对减少结果的歧义和改进反演精度的影响。通过三个接口的3D模型验证了该方法的正确性和有效性。使用本文呈现的程序对冲绳槽的观察到的重力异常数据进行3D反转。从反转结果获得三级地下室和Moho深度，这也证明了该方法的适应性。该研究对重力密度接口的反转进行了有用的尝试。

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