Assessing the strength of volcanic eruptions using acoustic infrasound measurements

在火山爆发时，岩石碎片从火山中高速喷出，并被热气带入大气层。当膨胀的气体推动岩浆通过火山管道并向地表移动时，就会产生这种被称为火山灰的细小颗粒的研磨混合物。当充满气体的岩浆接近地球表面时，压力突然下降，导致猛烈的爆炸，使岩浆破碎。小的液体碎片被迫进入大气层，在那里它们迅速凝固，形成了在火山爆发上方经常观察到的引人注目的羽流。这些气体和岩石碎片的混合物可以上升到几公里的高度，大气风将火山灰带到很大的水平距离。众所周知，空气中的火山灰对航空业构成直接威胁。人们认识到飞机遭遇火山灰云的问题日益严重已经有一段时间了。喷发物质的体积、从火山口喷出的速度以及喷发羽流的最大高度是大气灰扩散数值模型的关键输入。最近的研究强调了在次声带进行声学测量以评估喷发源参数的潜力。火山爆发释放出大量物质扰乱大气。这些辐射产生次声带的声波，低于人类听力的阈值。因此，所产生的次声的强度可以与大气的体积加速度以及在喷口喷出的物质的速率和数量相关联。然而，使用过于简化的火山声源和次声传播模型，在一定程度上阻碍了更广泛地应用基于使用声学数据评估喷发强度的方法。该项目将通过实施第一个声学次声信号建模和反演的理论和数字框架，以及实时评估喷发源参数，克服过去的局限性。我们将建立复杂的声波传播的数值模型，考虑到大气的变化和地形的影响。将比较观测信号和理论信号，以评估火山声源的强度和机制等喷发参数。此外，我们将展示如何将这些参数作为数值模型的输入，以显着提高火山灰羽流的大气传播的预测。我们将使用多学科的数据收集在一个现场活动在山。埃特纳，意大利，以确认我们的预测和校准我们的模型。该项目解决了火山学中的重要问题，并将有助于我们对次声信号，火山排放和喷发动力学的理解。这反过来将改善对火山灾害的监测和探测。将认真评估使用次声作为一种连续的、远距离的工具来探测和消除火山喷发物的可行性。我们预计，我们的研究将影响新战略的发展，以实时监测和预测火山灰的危害。

During volcanic eruptions, fragments of rock are ejected from volcanoes at high speed, and driven into the atmosphere by hot gases. This abrasive mixture of fine particles, called volcanic ash, is created when expanding gases push magma through the volcanic conduit and towards the surface. The sudden drop in pressure as gas-charged magma approaches the Earth's surface results in violent explosions that shatter magma. Small fragments of liquid are forced into the atmosphere where they rapidly solidify, forming the dramatic plumes frequently observed above erupting volcanoes. These mixtures of gases and fine rock fragments can rise to heights of several kilometers where atmospheric winds transport volcanic ash over large horizontal distances. It is common knowledge that airborne volcanic ash represents a direct threat to aviation. The growing problem of aircraft encounters with ash clouds has been recognized for some time. The volume of erupted material, the rate at which it is ejected from volcanic vents, and the maximum height of eruption plumes are key inputs into numerical models of atmospheric ash dispersal. Recent studies have highlighted the potential of acoustic measurements in the infrasonic band for assessment of eruption source parameters. Erupting volcanoes perturb the atmosphere by emission of large amounts of material. These emissions produce sound waves in the infrasonic band, below the threshold of human hearing. The intensity of the produced infrasound can, thus, be linked to the volumetric acceleration of the atmosphere, and the rates and amount of material ejected at the vent. The use of oversimplified models of volcano acoustic sources and infrasound propagation has, however, partly hindered more extensive application of methods based on the use of acoustic data to assess the strength of eruptions. This project will overcome past limitations by implementing the first theoretical and numerical framework for modelling and inversion of acoustic infrasound signals, and assessment of eruption source parameters in real-time. We will build complex numerical models of acoustic wave propagation that take into account atmospheric variability and the effects of topography. Observed and theoretical signals will be compared in order to assess eruption parameters such as the strength and mechanisms of volcano acoustic sources. Further, we will show how these parameters can be used as input into numerical models to dramatically improve predictions of atmospheric propagation of volcanic ash plumes. We will use multi-disciplinary data collected during a field campaign at Mt. Etna, Italy, to confirm our predictions and calibrate our models. This project addresses important questions in volcanology and will contribute to our understanding of infrasound signals, volcanic emissions, and eruption dynamics. This will, in turn, improve monitoring and detection of volcanic hazards. The feasibility of using infrasound as a continuous, remote, tool to detect and characterise volcanic emissions will be scrupulously evaluated. We anticipate that our research will influence the development of new strategies to monitor and forecast volcanic ash hazards in real-time.