Remote Detection and Location of Explosive Volcanism in Alaska With the EarthScope Transportable Array
Remote Detection and Location of Explosive Volcanism in Alaska With the EarthScope Transportable Array
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DOI：
10.1029/2019jb018347
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发表时间：
2019-12
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影响因子：
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通讯作者：
R. Sanderson;R. Matoza;D. Fee;M. Haney;J. Lyons
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文献类型：
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作者：
R. Sanderson;R. Matoza;D. Fee;M. Haney;J. Lyons
目前在阿拉斯加部署的地球望远镜可移动阵列(TA)为利用相对密集的区域地震声学网络研究火山爆发提供了前所未有的机会。次声监测已证明可用于火山爆炸的远程(250公里范围)探测和定性，但以前的研究使用的是相对稀疏的区域或全球网络。来自当地无人监测的博戈斯洛夫火山的70次爆炸事件(2016-2017年)提供了一套独特的验证数据，以检查助理局和其他区域网络探测和定位阿拉斯加偏远爆发火山喷发的能力。通过一种简单的基于包络的反向时间偏移(RTM)技术，我们能够探测和定位阿拉斯加火山天文台探测到的61个Bogoslof次声事件中的72%以上。值得注意的是，仅使用稀疏区域次声阵列的RTM产生的结果类似于结合广泛的单传感器TA网络时的结果，这可能是由于良好的信噪比、季节性传播条件和源-接收器几何结构。我们的实现还可以检测和定位阿拉斯加克利夫兰火山和堪察加贝兹米亚尼火山的爆炸性喷发，以及地震等非火山事件的次声。我们使用接收器操作特征曲线、事件检测率和定位精度来表征RTM算法和相关参数选择的成功。我们的方法对于使用实时数据的火山和非火山爆炸事件的检测和定位以及扫描连续的波形数据档案是有用的。
The current deployment of the EarthScope Transportable Array (TA) in Alaska affords an unprecedented opportunity to study explosive volcanic eruptions using a relatively dense regional seismoacoustic network. Infrasound monitoring has demonstrated utility for the remote (>250 km range) detection and characterization of volcanic explosions, but previous studies have used relatively sparse regional or global networks. Seventy explosive events from the locally unmonitored Bogoslof volcano (2016–2017) provide a unique validation data set to examine the ability of the TA and other regional networks to detect and locate remote explosive volcanic eruptions in Alaska. With a simple envelope‐based reverse time migration (RTM) technique, we are able to detect and locate more than 72% of the 61 Bogoslof infrasound events detected by the Alaska Volcano Observatory. Notably, RTM using only sparse regional infrasound arrays produces results similar to when incorporating the extensive single‐sensor TA network, likely due to favorable signal‐to‐noise ratios, seasonal propagation conditions, and source‐receiver geometries. Our implementation also detects and locates explosive eruptions from Cleveland volcano, Alaska, and Bezymianny volcano, Kamchatka, as well as infrasound from nonvolcanic events such as earthquakes. We characterize the success of the RTM algorithm and associated parameter choices using receiver operating characteristic curves, event detection rates, and location accuracy. Our methods are useful for explosive volcanic and nonvolcanic event detection and localization using real‐time data and for scanning continuous waveform data archives.