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Earthquake detection capability of the Swiss Seismic Network

机译:瑞士地震台网的地震探测能力

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摘要

A reliable estimate of completeness magnitudes is vital for many seismicity- and hazard-related studies. Here we adopted and further developed the Probability-based Magnitude of Completeness (PMC) method. This method determines network detection completeness (MP) using only empirical data: earthquake catalogue, phase picks and station information. To evaluate the applicability to low- or moderate-seismicity regions, we performed a case study in Switzerland. The Swiss Seismic Network (SSN) at present is recording seismicity with one of the densest networks of broad-band sensors in Europe. Based on data from 1983 January 1 to 2008 March 31, we found strong spatio-temporal variability of network completeness: the highest value of MP in Switzerland at present is 2.5 in the far southwest, close to the national boundary, whereas MP is lower than 1.6 in high-seismicity areas. Thus, events of magnitude 2.5 can be detected in all of Switzerland. We evaluated the temporal evolution of MP for the last 20 yr, showing the successful improvement of the SSN. We next introduced the calculation of uncertainties to the probabilistic method using a bootstrap approach. The results show that the uncertainties in completeness magnitudes are generally less than 0.1 magnitude units, implying that the method generates stable estimates of completeness magnitudes. We explored the possible use of PMC: (1) as a tool to estimate the number of missing earthquakes in moderate-seismicity regions and (2) as a network planning tool with simulation computations of installations of one or more virtual stations to assess the completeness and identify appropriate locations for new station installations. We compared our results with an existing study of the completeness based on detecting the point of deviation from a power law in the earthquake-size distribution. In general, the new approach provides higher estimates of the completeness magnitude than the traditional one. We associate this observation with the difference in the sensitivity of the two approaches in periods where the event detectability of the seismic networks is low. Our results allow us to move towards a full description of completeness as a function of space and time, which can be used for hazard-model development and forecast-model testing, showing an illustrative example of the applicability of the PMC method to regions with low to moderate seismicity
机译:对于许多与地震和灾害有关的研究,对完整性大小的可靠估计至关重要。在这里,我们采用并进一步开发了基于概率的完整性量级(PMC)方法。该方法仅使用经验数据来确定网络检测的完整性(MP):地震目录,相位选择和站点信息。为了评估在低地震或中地震地区的适用性,我们在瑞士进行了案例研究。目前,瑞士地震台网(SSN)正在使用欧洲最密集的宽带传感器网络之一来记录地震活动。根据1983年1月1日至2008年3月31日的数据,我们发现网络完整性具有强烈的时空变异性:瑞士目前的MP最高值位于西南偏远地区,接近国界,而MP低于1.6在高地震地区。因此,在整个瑞士都可以检测到2.5级的事件。我们评估了最近20年MP的时间演变,显示了SSN的成功改进。接下来,我们使用引导方法将不确定性的计算引入概率方法。结果表明,完整性量级的不确定度通常小于0.1个量级单位,这表明该方法可以生成稳定度的完整性量级估计。我们探讨了PMC的可能用途:(1)作为估计中地震度地区失踪地震数量的工具,(2)作为网络规划工具,通过模拟计算一个或多个虚拟站的安装来评估完整性并确定新站安装的适当位置。我们将结果与现有的完整性研究进行了比较,该研究基于检测地震大小分布中与幂定律的偏离点。通常,与传统方法相比,新方法可提供更高的完整性估计值。我们将此观察结果与两种方法在地震网络的事件可检测性较低的时期的灵敏度差异相关联。我们的结果使我们能够对作为时间和空间函数的完整性进行完整描述,可将其用于危险模型开发和预测模型测试,从而展示了PMC方法在低污染地区的适用性的示例。中度地震

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