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A Review of the Source Characteristics and Physical Mechanisms of Very Long Period (VLP) Seismic Signals at Active Volcanoes

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SURVEYS IN GEOPHYSICS
卷 -, 期 -, 页码 -

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SPRINGER
DOI: 10.1007/s10712-023-09800-0

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Very-long-period; Seismic signal; Active volcano; Moment tensor; Single force; Eruption

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VLP signals occur during both eruptive and quiet phases at various types of volcanoes, and they are connected to fluid movement within the plumbing system. Observations at several volcanoes indicate a correlation between gas emissions and VLPs, as well as deformation episodes and the occurrence of VLPs. The physical mechanisms that can generate VLPs include magma chamber and crack inflation/deflation, gas slug movement, and restoration of gravitational equilibrium in the plumbing system.
Very Long Period (VLP) signals with periods longer than 2 s may occur during eruptive or quiet phases at volcanoes of all types (shield and stratovolcanoes with calderas, as well as other stratovolcanoes) and are inherently connected to fluid movement within the plumbing system. This is supported by observations at several volcanoes that indicate a correlation between gas emissions and VLPs, as well as deformation episodes due to melt accumulation and migration that are followed by the occurrence of VLPs. Moment tensors of VLPs are usually characterized by large volumetric components of either positive or negative sign along with possibly the presence of single forces that may result from the exchange of linear momentum between the seismic source and the Earth. VLPs may occur during a variety of volcanological processes such as caldera collapse, phreatic eruptions, vulcanian eruptions, strombolian activity, and rockfalls at lava lakes. Physical mechanisms that can generate VLPs include the inflation and deflation of magma chambers and cracks, the movement of gas slugs through conduits, and the restoration of gravitational equilibrium in the plumbing system after explosive degassing or rockfalls in lava lakes. Our understanding of VLPs is expected to greatly improve in the future by the use of new instrumentation, such as Distributed Acoustic Sensing, that will provide a much denser temporal and spatial sampling of the seismic wavefield. This vast quantity of data will then require time efficient and objective processing that can be achieved through the use of machine learning algorithms.

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