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首页> 外文期刊>Pure and Applied Geophysics >Study of volcanic sources at Long Valley Caldera, California, using gravity data and a genetic algorithm inversion technique
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Study of volcanic sources at Long Valley Caldera, California, using gravity data and a genetic algorithm inversion technique

机译：利用重力数据和遗传算法反演技术研究加利福尼亚州长谷火山口的火山源

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We model the source inflation of the Long Valley Caldera, California, using a genetic algorithm technique and micro-gravity data. While there have been numerous attempts to model the magma injection at Long Valley Caldera from deformation data, this has proven difficult given the complicated spatial and temporal nature of the volcanic source. Recent work illustrates the effectiveness of considering micro-gravity measurements in volcanic areas. A genetic algorithm is a problem-solving technique which combines genetic and prescribed random information exchange. We perform two inversions, one for a single spherical point source and another for two-sources that might represent a more spatially distributed source. The forward model we use to interpret the results is the elastic-gravitational Earth model which takes into account the source mass and its interaction with the gravity field. The results demonstrate the need to incorporate more variations in the model, including another source geometry and the faulting mechanism. In order to provide better constraints on intrusion volumes, future work should include the joint inversion of gravity and deformation data during the same epoch.
机译：我们使用遗传算法技术和微重力数据对加利福尼亚长谷火山口的源膨胀进行建模。尽管已经进行了许多尝试根据变形数据对长谷火山口的岩浆注入进行建模，但是鉴于火山源的时空复杂性，事实证明这很困难。最近的工作说明了考虑在火山区进行微重力测量的有效性。遗传算法是一种解决问题的技术，它结合了遗传和规定的随机信息交换。我们执行两个反演，一个反演一个球面点源，另一个反演两个源头，它们可能代表一个空间分布更大的源头。我们用来解释结果的正向模型是弹性重力地球模型，该模型考虑了源质量及其与重力场的相互作用。结果表明，需要在模型中包含更多的变化，包括另一个源几何形状和断层机制。为了更好地限制入侵量，未来的工作应包括同一时期重力和变形数据的联合反演。

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