论文标题
控制不稳定性并煽动广义毛龙 - 诺波夫模型中的慢滑滑
Control instabilities and incite slow-slip in generalized Burridge-Knopoff models
论文作者
论文摘要
广义的Burridge-Knopoff(GBK)模型显示出丰富的动力学,其特征是不稳定性和多个分叉。 GBK模型由相互连接的质量组成,这些质量可以在摩擦下的粗糙表面上滑动。所有质量都连接到板,该板缓慢地为系统提供了能量。该系统显示长时间的静止,并被能量放松的快速动态事件(雪崩)中断。在这些事件中,块的簇突然滑动,模拟地震滑移和地震破裂。在这里,我们提出了一种预防GBK雪崩,控制其动力学并煽动慢滑的理论。我们利用摩擦对压力的依赖性,并将其用作改变系统动力学的后门。我们使用控制的数学理论,并首次成功地(a)稳定和限制了GBK模型中的混乱,(b)保证缓慢的摩擦耗散,(c)将GBK系统调整为较低能量的理想全球渐近平衡。我们的控制方法是强大的,不需要确切了解系统的摩擦行为。最后,已知GBK模型会呈现自组织的批判性(SOC)行为。因此,提出的方法论显示了SOC控制(SOCC)的另一个例子。鉴于GBK模型的动力学表现出许多与地震的类比,我们希望激发有关人为和/或自然地震的缓解地震策略。从更广泛的角度来看,我们的控制方法可用于改善对地球物理中复杂系统级联故障的理解,访问隐藏的特征并通过实时控制其时空行为来提高其可预测性。
Generalized Burridge-Knopoff (GBK) models display rich dynamics, characterized by instabilities and multiple bifurcations. GBK models consist of interconnected masses that can slide on a rough surface under friction. All masses are connected to a plate, which slowly provides energy to the system. The system displays long periods of quiescence, interrupted by fast, dynamic events (avalanches) of energy relaxation. During these events, clusters of blocks slide abruptly, simulating seismic slip and earthquake rupture. Here we propose a theory for preventing GBK avalanches, control its dynamics and incite slow-slip. We exploit the dependence of friction on pressure and use it as a backdoor for altering the dynamics of the system. We use the mathematical Theory of Control and, for the first time, we succeed in (a) stabilizing and restricting chaos in GBK models, (b) guaranteeing slow frictional dissipation and (c) tuning the GBK system toward desirable global asymptotic equilibria of lower energy. Our control approach is robust and does not require exact knowledge of the frictional behavior of the system. Finally, GBK models are known to present Self-Organized Critical (SOC) behavior. Therefore, the presented methodology shows an additional example of SOC Control (SOCC). Given that the dynamics of GBK models show many analogies with earthquakes, we expect to inspire earthquake mitigation strategies regarding anthropogenic and/or natural seismicity. In a wider perspective, our control approach could be used for improving understanding of cascade failures in complex systems in geophysics, access hidden characteristics and improve their predictability by controlling their spatio-temporal behavior in real-time.