CAJ | 학술논문

栅格计算因其具有简单的构架成为目前地学分析的主流模型，然而，由于栅格计算平均分配计算和存储资源的弱点，不仅容易产生冗余，更重要的是难以凸显研究对象的突变部分，从而使研究者有可能忽略地学现象的变化特征。为此，本文提出将时空点过程模型应用于地学研究。时空点过程不仅适用于模拟以点事件为基本单元的地学现象，而且由于大多数地学过程可以转化为时空点过程，故其具有更广泛的应用范围。因此，时空点过程不仅是一种数据模型，同时也是地学问题的分析方法，更是观察和理解地学问题的一种新视角。为了实现从点过程数据中提取模式，作者经过多年研究提出了时空点过程层次分解理论框架，该理论与信号处理理论中的谱分析思路类似，首先，假设任意点集为有限多个均匀点过程的叠加，然后，通过点局部密度表达工具K阶邻近距离，将空间点转换为混合概率密度函数，再应用优化方法将混合密度函数进行分解得到丛集点和噪声，最终利用密度相连原理从丛集点中提取模式。该理论框架可适用于绝大多数点集数据，初步实现了点集数据的“傅里叶变换”。
책격계산인기구유간단적구가성위목전지학분석적주류모형，연이，유우책격계산평균분배계산화존저자원적약점，불부용역산생용여，경중요적시난이철현연구대상적돌변부분，종이사연구자유가능홀략지학현상적변화특정。위차，본문제출장시공점과정모형응용우지학연구。시공점과정불부괄용우모의이점사건위기본단원적지학현상，이차유우대다수지학과정가이전화위시공점과정，고기구유경엄범적응용범위。인차，시공점과정불부시일충수거모형，동시야시지학문제적분석방법，경시관찰화리해지학문제적일충신시각。위료실현종점과정수거중제취모식，작자경과다년연구제출료시공점과정층차분해이론광가，해이론여신호처리이론중적보분석사로유사，수선，가설임의점집위유한다개균균점과정적첩가，연후，통과점국부밀도표체공구K계린근거리，장공간점전환위혼합개솔밀도함수，재응용우화방법장혼합밀도함수진행분해득도총집점화조성，최종이용밀도상련원리종총집점중제취모식。해이론광가가괄용우절대다수점집수거，초보실현료점집수거적“부리협변환”。
The gridding computation is a major model in current geoscientific research due to its simplicity in or-ganizing data resources. However, because the gridding computation equally distributes computational resourc-es, it brings redundancy to the computational process and neglects catastrophe points in geoscientific phenome-na, which might overlook the important patterns and bring more uncertainties to the research result. To overcome this weakness, this paper proposes to use the spatial point process model in geoscientific research. The spatial point process model is used to model spatial point based geoscientific phenomenon, also is applied to most of the other geoscientific processes (because they can be transformed into spatial point processes). In this regard, the spatial point process is not only a data model, but also an analysis tool for geoscientific problems. Moreover, it provided a new angle of view for observing geoscientific problems. To extract patterns from point process data, the authors propose the frame of multilevel decomposition of spatiotemporal point process. This frame is similar to the basic idea of signal decomposition. We first assume that any point data set is the overlay of an unknown number of homogeneous point processes. Then, the points are transformed into a mixture probability density function of the Kth nearest distance of each point. After that, the optimization method is used to separate cluster-ing points from noise. Finally, the patterns are extracted using the density connectivity mechanism. The theory can be used to any type of point process data. It can be considered as the“Fourier transform”of point process da-ta.