Ensemble data assimilation for earthquake sequences: probabilistic estimation and forecasting of fault stresses
Ensemble data assimilation for earthquake sequences: probabilistic estimation and forecasting of fault stresses
复制标题
地震序列集合数据同化：断层应力的概率估计和预测
DOI：
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发表时间：
2019
影响因子：
2.8
通讯作者：
A. Fichtner
中科院分区：
文献类型：
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作者：
Y. van Dinther;H. Künsch;A. Fichtner
我们对地震的物理理解，沿着我们预测地震的能力，都受到断层当前和未来应力状态的有限指示的阻碍。将间接观测、实验室实验和基于物理学的数值模拟相结合，以定量估计这种演变至关重要。然而，鉴于观测的稀缺性和不确定性以及对控制地震的物理学建模的困难，定量积分是脆弱的。我们表明，观测和先验物理知识，沿着他们的错误，可以有效地整合通过集合数据同化（EDA），这是从天气预报的统计框架。为了评估断层应力估计和预测是否可能，我们在俯冲带设置中进行了一个完美的模型试验，该试验模仿了一个缩放的实验室实验。合成噪声数据的速度和应力从一个点附近的表面同化使用Enhancement卡尔曼滤波器。这些数据更新的速度和应力状态在整个150个合奏成员，其动态是由地震周期模型。该粘弹塑性正演模型通过求解具有强烈速率相关摩擦系数的Navier-Stokes方程来预测系统的演化。集合同化的数据从一个单一的位置提供概率估计的断层应力和动态强度演化，捕捉真正的解决方案非常好。这是可能的，因为采样的误差协方差矩阵包含来自物理学的先验信息，该物理学将地表处的速度、应力和压力与断层处的速度、应力和压力相关联。在分析步骤中，该协方差允许重构应力和强度分布。在随后的预测步骤中，求解物理方程以在时间上向前传播更新的状态。这提供了在该合成实验室环境中下一次地震发生可能性的概率信息。在整个集合模拟中，对大型准周期事件的预测能力明显优于周期性递归模型。例如，它只需要警报响起17%，而不是68%的时间来预测21个事件中的70%。我们表明，通过贝叶斯框架将我们的物理定律的先验知识与观测相结合，提供了独立使用观测或数值模型的独特附加值。因此，这个教育测试显示了巨大的潜力，包括基于物理的信息到概率地震危险性评估使用EDA。为了分析它的真实的世界，关于二维简化艾德系统中物理学的精确表示的潜在假设仍有待探索。
Our physical understanding of earthquakes, along with our ability to forecast them, is hampered by limited indications on the current and future state of stress on faults. Integrating indirect observations, laboratory experiments and physics-based numerical modelling to quantitatively estimate this evolution is crucial. However, quantitative integrations are tenuous in light of the scarcity and uncertainty of observations and the difficulty of modelling the physics governing earthquakes. We show that observations and prior physical knowledge, along with their errors, can be efficiently integrated through the statistical framework of ensemble data assimilation (EDA), which is adopted from weather forecasting. To evaluate whether fault stress estimation and forecasting is possible, we perform a perfect model test in a subduction zone setup that mimicks a scaled laboratory experiment. Synthetic noised data on velocities and stresses from one point near the surface are assimilated using an Ensemble Kalman Filter. These data update the velocity and stress states throughout 150 ensemble members, whose dynamics is governed by a seismic cycle model. This visco-elasto-plastic forward model forecasts the system’s evolution through solving Navier–Stokes equations with a strongly rate-dependent friction coeffi-cient. The ensemble assimilation of data from a single location provides probabilistic estimates of fault stress and dynamic strength evolution, which capture the true solution exceptionally well. This is possible, because the sampled error covariance matrix contains prior information from the physics that relates velocities, stresses and pressure at the surface to those at the fault. In the analysis step, this covariance allows stress and strength distributions to be reconstructed. In the subsequent forecast step the physical equations are solved to propagate the updated states forward in time. This provides probabilistic information on the likelihood of occurrence of the next earthquake in this synthetic laboratory setting. Throughout the ensemble simulations the forecasting ability for large, quasi-periodic events turns out to be significantly better than that of a periodic recurrence model. For example, it only requires an alarm to sound for 17 per cent instead of 68 per cent of the time to forecast 70 per cent of 21 events. We show that combining our prior knowledge of physical laws with observations through a Bayesian framework provides distinct added value with respect to using observations or numerical models indepen-dently. This educational test thus shows vast potential for including physics-based information into probabilistic seismic hazard assessment using EDA. To analyze its real world potential assumptions on an exact representation of the physics in a 2-D simplified system remain to be explored.
影响因子：
13.6
作者：
Shaw BE;Milner KR;Field EH;Richards-Dinger K;Gilchrist JJ;Dieterich JH;Jordan TH
通讯作者：
Jordan TH
影响因子：
16.6
作者：
Dal Zilio, Luca;van Dinther, Ylona;Avouac, Jean-Philippe
通讯作者：
Avouac, Jean-Philippe