Multi-stream based marked point process
Multi-stream based marked point process
复制标题
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Sujun Hong;Hirotaka Hachiya
中科院分区：
文献类型：
--
作者：
Sujun Hong;Hirotaka Hachiya
当使用点过程时，需要基于有关数据的物理和数学先验知识，为强度函数设计特定形式的模型。最近，一种完全可训练的基于深度学习的方法已经被开发用于时间点过程。该方法模拟了累积危险函数（CHF），它能够以数据驱动的方式系统地计算自适应强度函数。然而，这种方法没有考虑事件的属性信息，尽管点过程的许多应用产生了各种标记信息，如地震活动的位置，震级和深度。为了克服这一限制，我们提出了一种完全可训练的标记点处理方法，使用多流深度神经网络对时间和标记的分解CHF进行建模。此外，我们还建议将多个标记的信息编码到一个单一的图像和提取必要的信息自适应没有详细的知识的数据。通过对模拟玩具数据和真实的地震数据的实验，证明了该方法的有效性。
When using a point process, a specific form of the model needs to be designed for intensity function, based on physical and mathematical prior knowledge about the data. Recently, a fully trainable deep learning-based approach has been developed for temporal point processes. This approach models a cumulative hazard function (CHF), which is capable of systematic computation of adaptive intensity function in a data-driven manner. However, this approach does not take the attribute information of events into account although many applications of point processes generate with a variety of marked information such as location, magnitude, and depth of seismic activity. To overcome this limitation, we propose a fully trainable marked point process method, modeling decomposed CHFs for time and mark using multi-stream deep neural networks. In addition, we also propose to encode multiple marked information into a single image and extract necessary information adaptively without detailed knowledge about the data. We show the effectiveness of our proposed method through experiments with simulated toy data and real seismic data.