Volcanic earthquake catalog enhancement using integrated detection, matched-filtering, and relocation tools
Volcanic earthquake catalog enhancement using integrated detection, matched-filtering, and relocation tools
复制标题
DOI：
10.3389/feart.2023.1158442
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Darren Tan;D. Fee;A. Hotovec-Ellis;J. Pesicek;M. Haney;J. Power;T. Girona
中科院分区：
文献类型：
--
作者：
Darren Tan;D. Fee;A. Hotovec-Ellis;J. Pesicek;M. Haney;J. Power;T. Girona
火山地震目录是解释地下火山活动和预测火山爆发的重要数据产品。检测技术的进展（例如，匹配过滤、机器学习）和相对重定位工具已经改进了目录完整性并细化了事件位置。然而，大多数火山观测站尚未将这些技术纳入其编目工作流程。这部分是由于操作，自动化和校准这些技术的复杂性，以令人满意的方式为不同的火山网络和它们不同的地震活动。为了简化阿拉斯加火山天文台（AVO）的目录增强工具的集成，我们集成了四个流行的开源工具：REDPy，EQcorrscan，HypoDD和GrowClust。这些工具的组合提供了在单个工作流中添加地震事件检测和重新定位事件的能力。该工作流程依赖于标准触发和互相关聚类（REDPy）的组合来整合匹配过滤（EQcorrscan）中使用的代表性模板。然后使用由HypoDD和/或GrowClust提供的差分时间方法重新定位模板及其检测。我们的工作流程还提供了代码，以在适当的节点合并活动数据，并计算有效事件的震级和频率指数。我们将此工作流程应用于三个数据集：2012-2013年猛犸山（加州）的地震群序列，2009年的里道特火山（阿拉斯加）喷发，以及2006年的奥古斯丁火山（阿拉斯加）喷发;并将我们的结果与以前的研究在每个火山。在一般情况下，我们的工作流程提供了一个显着增加的事件和改进的位置的数量，我们将事件集群和时间进展相关的火山活动。我们还讨论了工作流实现的最佳实践，特别是在应用这些工具稀疏火山地震网络。我们设想，我们的工作流程和这里提出的数据集将有助于详细的火山分析监测和研究工作。
Volcanic earthquake catalogs are an essential data product used to interpret subsurface volcanic activity and forecast eruptions. Advances in detection techniques (e.g., matched-filtering, machine learning) and relative relocation tools have improved catalog completeness and refined event locations. However, most volcano observatories have yet to incorporate these techniques into their catalog-building workflows. This is due in part to complexities in operationalizing, automating, and calibrating these techniques in a satisfactory way for disparate volcano networks and their varied seismicity. In an effort to streamline the integration of catalog-enhancing tools at the Alaska Volcano Observatory (AVO), we have integrated four popular open-source tools: REDPy, EQcorrscan, HypoDD, and GrowClust. The combination of these tools offers the capability of adding seismic event detections and relocating events in a single workflow. The workflow relies on a combination of standard triggering and cross-correlation clustering (REDPy) to consolidate representative templates used in matched-filtering (EQcorrscan). The templates and their detections are then relocated using the differential time methods provided by HypoDD and/or GrowClust. Our workflow also provides codes to incorporate campaign data at appropriate junctures, and calculate magnitude and frequency index for valid events. We apply this workflow to three datasets: the 2012–2013 seismic swarm sequence at Mammoth Mountain (California), the 2009 eruption of Redoubt Volcano (Alaska), and the 2006 eruption of Augustine Volcano (Alaska); and compare our results with previous studies at each volcano. In general, our workflow provides a significant increase in the number of events and improved locations, and we relate the event clusters and temporal progressions to relevant volcanic activity. We also discuss workflow implementation best practices, particularly in applying these tools to sparse volcano seismic networks. We envision that our workflow and the datasets presented here will be useful for detailed volcano analyses in monitoring and research efforts.