论文标题

使用深度学习的全球地磁扰动预测

Global geomagnetic perturbation forecasting using Deep Learning

论文作者

Upendran, Vishal, Tigas, Panagiotis, Ferdousi, Banafsheh, Bloch, Teo, Cheung, Mark C. M., Ganju, Siddha, Bhatt, Asti, McGranaghan, Ryan M., Gal, Yarin

论文摘要

地球诱导的电流(GIC)源于太阳风与地球磁层的相互作用而引起的地球磁场的时空变化,并将灾难性破坏驱动到我们的技术依赖社会。因此,在全球范围内预测GIC的计算模型,高空间分辨率和时间节奏对于执行迅速必要的缓解至关重要。由于GIC数据是专有的,因此使用磁场扰动(DB/DT)水平分量的时间变异性用作GIC的代理。在这项工作中,我们开发了一个快速,全局的DB/DT预测模型,该模型仅使用太阳能测量结果作为输入来预测未来30分钟。该模型使用门控复发单元总结了2小时的太阳风测量,并生成系数的预测,这些系数以球形谐波折叠以启用全局预测。部署时,我们的模型将在第二秒钟以下产生结果,并在1分钟的节奏下生成全局预测。我们在2011年8月5日和2015年3月17日的两次特定风暴中评估了我们在文献中的模型,同时具有自洽的基准模型集。我们的模型优于表现,或者在最先进的高级节奏本地和低时间节奏全球模型中具有一致的性能,同时在基准模型上表现出色/表现优于/具有可比性的性能。高度节奏和任意空间分辨率的这种快速推断最终可以使地球上任何地方的db/dt进行准确警告,从而导致以知情方式采取预防措施。

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 minutes into the future using only solar wind measurements as input. The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1-minute cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self-consistent benchmark model set. Our model outperforms, or has consistent performance with state-of-the-practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.

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