A common practice of seismology is to analyze earthquake occurrence in terms of events catalogues, with the aim to either find useful correlations between internal mechanisms under study and their outcome in the spatial/temporal series of the events or, more directly, to assess some statistical rules from observations. With this approach, catalogues are often searched for some recognizable patterns or behaviors: in this work we present a software tool created to reveal a particular kind of events sequences.

The idea follows from the concept of multiplets, a well known events pattern often found in seismic series. A multiplet is defined as a sequence of events, all near in space and time and exhibiting similar magnitudes. The amount of multiplets in seismic series is related, as it is for other clustering mechanisms, to underlying correlations in the physics of the events.

The software, built from scratch, scans seismic catalogues in search of events clustered as “multiplets”: this is done through the thorough application of comparison tests whose parameters thresholds are both user defined and semi-automated. The tool is however more “general” in the sense that by varying values of the filtering parameters it can reveal other kind of patterns too.

While we think that this tool can be thought as a general purpose space–time series analyzer, we have found it particularly useful when applied to the results of a seismic simulator with the purpose of assessing their adherence with the observed seismicity. It can be used as a sort of metric to quantify the simulation predictions effectiveness in terms of presence of similar multiplets distributions in simulated vs. real catalogues.

The software has been entirely developed in the Wolfram Language (Mathematica), a commercial powerful environment for scientific calculus and results report, but the main computational routine has been also ported to python for open-source, copyleft usage.

地震学的常见做法是根据事件目录来分析地震发生，目的是找到所研究的内部机制与其在事件的空间/时间序列中的结果之间的有用相关性，或者更直接地评估一些统计数据观察中的规则。通过这种方法，经常会在目录中搜索一些可识别的模式或行为：在这项工作中，我们提出了一个软件工具，旨在揭示特定类型的事件序列。

这个想法源于多重态的概念，多重态是地震序列中常见的一种众所周知的事件模式。多重态被定义为一系列事件，所有事件在空间和时间上都很接近，并且表现出相似的幅度。与其他聚类机制一样，地震系列中多次波的数量与事件物理中的潜在相关性有关。

该软件从头开始构建，扫描地震目录以搜索聚集为“多重组”的事件：这是通过彻底应用比较测试来完成的，其参数阈值都是用户定义的并且是半自动化的。然而，该工具更“通用”，因为通过改变过滤参数的值，它也可以揭示其他类型的模式。

虽然我们认为该工具可以被视为通用时空序列分析仪，但我们发现当应用于地震模拟器的结果以评估其与观测到的地震活动的一致性时，它特别有用。它可以用作一种度量，根据模拟与真实目录中是否存在相似的多重态分布来量化模拟预测的有效性。

该软件完全采用 Wolfram 语言 (Mathematica) 开发，这是一种用于科学计算和结果报告的强大商业环境，但主要计算例程也已移植到 python 中以供开源、copyleft 使用。