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Short term forecasting of explosions at Ubinas volcano, Peru

机译:秘鲁乌比纳斯火山爆炸的短期预测

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Most seismic eruption forerunners are described using Volcano‐Tectonic earthquakes, seismic energy release, deformation rates or seismic noise analyses. Using the seismic data recorded at Ubinas volcano (Peru) between 2006 and 2008, we explore the time evolution of the Long Period (LP) seismicity rate prior to 143 explosions. We resolve an average acceleration of the LP rate above the background level during the 2–3 hours preceding the explosion onset. Such an average pattern, which emerges when stacking over LP time series, is robust and stable over all the 2006–2008 period, for which data is available. This accelerating pattern is also recovered when conditioning the LP rate on the occurrence of an other LP event, rather than on the explosion time. It supports a common mechanism for the generation of explosions and LP events, the magma conduit pressure increase being the most probable candidate. The average LP rate acceleration toward an explosion is highly significant prior to the higher energy explosions, supposedly the ones associated with the larger pressure increases. The dramatic decay of the LP activity following explosions, still reinforce the strong relationship between these two processes. We test and we quantify the retrospective forecasting power of these LP rate patterns to predict Ubinas explosions. The prediction quality of the forecasts (e.g. for 17% of alarm time, we predict 63% of Ubinas explosions, with 58% of false alarms) is evaluated using error diagrams. The prediction results are stable and the prediction algorithm validated, i.e. its performance is better than the random guess.
机译:大多数火山爆发的先兆都是通过火山-构造地震,地震能量释放,变形率或地震噪声分析来描述的。利用2006年至2008年在Ubinas火山(秘鲁)记录的地震数据,我们探究了143次爆炸之前长期(LP)地震活动率的时间演变。我们解决了爆炸开始前2-3小时内LP速率高于背景水平的平均加速度。这样的平均模式出现在LP时间序列上,在整个2006-2008年期间都是稳健且稳定的(有数据可用)。当根据其他LP事件的发生而不是根据爆炸时间来调节LP速率时,也可以恢复这种加速模式。它支持产生爆炸和LP事件的通用机制,岩浆管道压力增加是最可能的选择。在较高能量的爆炸之前,朝向爆炸的平均LP速率加速度非常重要,据推测与较高的压力增加相关。爆炸后液化石油气活动急剧下降,仍然加强了这两个过程之间的牢固关系。我们测试并量化了这些LP速率模式对Ubinas爆炸的回顾性预测能力。使用错误图评估了预测的预测质量(例如,对于17%的警报时间,我们预测63%的Ubinas爆炸,其中58%的错误警报)。预测结果稳定且预测算法得到验证,即其性能优于随机猜测。

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