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1. 发明授权

US5373486A Seismic event classification system 失效
标题翻译：地震事件分类系统
公开(公告)号：US5373486A
公开(公告)日：1994-12-13
申请号：US13268
申请日：1993-02-03
申请人： Farid U. Dowla , Stephen P. Jarpe , William Maurer
发明人： Farid U. Dowla , Stephen P. Jarpe , William Maurer
IPC分类号： G01V1/00 , G06F17/00 , H04B1/06
CPC分类号： G01V1/003
摘要： In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.
摘要翻译：在计算机解析地震资料时，关键的第一步是确定一个未知事件的一般类。例如，分类可能是：远地震，区域，地方，车辆或噪音。自组织神经网络（SONN）可用于对这些事件进行分类。 Kohonen和自适应共振理论（ART）SONNs都是有用的。给定地震事件的检测和相应的信号，计算时间频率分布，二进制表示，最后是移位不变表示，它是二维傅立叶变换（2-D FFT）二进制时频分布。该预处理的输入被馈送到SONN中。这些神经网络能够组合看起来相似的事件。 ART SONN在分类事件方面具有优势，因为不需要预先定义集群组的类型。然后将SONN的结果与专家地震学家的分类结果一起用于导出事件分类概率。

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