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Neural network modeling of Herglotz—Wiechert inversion of multiparametric travel-time curves of seismic waves

机译:Herglotz神经网络建模—地震波多参数传播时间曲线的Wiechert反演

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摘要

Using artificial neural networks to solve a problem of plotting travel-time curves of seismic waves can create nonlinear travel-time model of P and S phases of seismic waves arrangement as a function of several arguments: source depth, magnitude, back azimuth and epicenter distance. Construction of three-dimensional travel-time relationships and their use for modeling of hadographs and their inversion are considered on examples of seismic records Ukrainian seismic stations. Examples of inversion locus within the model Herglotz—Wiechert and features of application of the model in a real environment for single seismic stations, and generalization for arbitrary coordinate of the source and the point of signal registration in the Black Sea region are given.
机译:使用人工神经网络解决绘制地震波传播时间曲线的问题,可以创建地震波排列的P相和S相非线性传播时间模型,该模型取决于以下几个参数:源深度,震级,后方位角和震中距离。乌克兰地震台站的地震记录实例中考虑了三维旅行时间关系的构建及其在航海图建模和反演中的应用。给出了Herglotz-Wiechert模型中反演轨迹的例子,以及该模型在单个地震台的实际环境中的应用特点,以及对黑海地区震源和信号记录点的任意坐标的概括。

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