Liquefaction hazard assessment and ground failure probability analysis in the Kathmandu Valley of Nepal

During the 2015 Gorkha Earthquake (Mw7.8), extensive soil liquefaction was observed across the Kathmandu Valley. As a densely populated urban settlement, the assessment of liquefaction potential of the valley is crucial especially for ensuring the safety of engineering structures. In this study, we use borehole data including SPT-N values of 410 locations in the valley to assess the susceptibility, hazard, and risk of liquefaction of the valley soil considering three likely-to-recur scenario earthquakes. Some of the existing and frequently used analysis and computation methods are employed for the assessments, and the obtained results are presented in the form of liquefaction hazard maps indicating factor of safety, liquefaction potential index, and probability of ground failure (PG). The assessment results reveal that most of the areas have medium to very high liquefaction susceptibility, and that the central and southern parts of the valley are more susceptible to liquefaction and are at greater risk of liquefaction damage than the northern parts. The assessment outcomes are validated with the field manifestations during the 2015 Gorkha Earthquake. The target SPT-N values (Nimproved) at potentially liquefiable areas are determined using back analysis to ascertain no liquefaction during the aforesaid three scenario earthquakes.

Source reconstruction of the 1969 Majene, Sulawesi earthquake and tsunami: A preliminary study

We studied the February 23rd, 1969 M7.0 Majene, Sulawesi earthquake and tsunami. It was followed by tsunami reported at five locations. At least 64 people were killed and severe damage on infrastructures were reported in Majene region. Based on damage data, we estimated that the maximum intensity of the earthquake was MMI VIII. Focal mechanisms, derived using first motion polarity analysis, indicated that the earthquake had a thrust mechanism. Furthermore, we built hypothetical earthquake scenarios based on a rectangular fault plane of 40 km × 20 km with a homogeneous slip model of 1.5 m. We run the Open Quake and the JAGURS code to validate the macroseismic and tsunami observation data, respectively. Our best-fitted earthquake model generates maximum intensity of 8+ which is in line with the reported macroseismic data. However, the maximum simulated tsunami height from all scenario earthquakes is 2.25 m which is smaller than the 4 m tsunami height observed at Pelattoang. The possibility of contribution of another mechanism to tsunami generation requires further investigation.

Probabilistic Liquefaction Potential of Kathmandu Soil Based On Standard Penetration Tests

Despite being a liquefaction susceptible zone, Kathmandu Valley soil in Nepal has limited studies on liquefaction potential and most of them are based on the deterministic approach. Although this method is widely used, it ignores the uncertainties of seismic parameters such as peak ground acceleration, amax, and earthquake magnitude, Mw as well as the inherent variabilities of soil layers, in-situ testing procedures, and geotechnical properties. On the other hand, the probabilistic approach helps assess the liquefaction potential by considering all these uncertainties. In this study, we assess the liquefaction hazard in the Kathmandu Valley using the first-order second-moment (FOSM) method as a probabilistic approach for liquefaction hazard assessment. The assessment is done for three likely-to-recur scenario earthquakes utilizing the geotechnical data of 1510 boreholes. The soils are characterized geotechnically to further assess susceptibility criteria of liquefaction in the valley. The assessment reveals that the central part of the valley is more vulnerable to liquefaction than other parts and the liquefaction probability increases with increasing depth up to 9 m, after which has geared down the value. Moreover, a relationship between the probability of liquefaction (PL) and the factor of safety (FS) against liquefaction is established. The hazard maps prepared for different earthquake scenarios can be useful for future infrastructure planning in Kathmandu Valley.

Robust, an Earthquake Early Warning System in the Lower Rhine Embayment, Germany

<p>The Lower Rhine Embayment in western Germany is one of the most important areas of earthquake recurrence north of the Alps, facing a moderate level of seismic hazard in the European context but a significant level of risk due to a large number of important industrial infrastructures. In this context, the project ROBUST aims at designing a user-oriented hybrid earthquake early warning and rapid response system where regional seismic monitoring is combined with smart, on-site sensors, resulting in the implementation of decentralized early warning procedures.<br><br>One of the research areas of this project deals with finding an optimal regional seismic network arrangement. With the optimally compacted network, strong ground movements can be detected quickly and reliably. In this work simulated scenario earthquakes in the area are used with an optimization approach in order to densify the existing sparse network through the installation of additional decentralized measuring stations. Genetic algorithms are used to design efficient EEW networks, computing optimal station locations and trigger thresholds in recorded ground acceleration. By minimizing the cost function, a comparison of the best earthquake early warning system designs is performed and the potential usefulness of existing stations in the region is considered as will be presented in the meeting.</p>

Ranking and calibration of ground-motion models using the stochastic area metric.

<p>The selection and ranking of  Ground Motion Models (GMMs) for scenario earthquakes is a crucial element in seismic hazard analysis. Typically model testing and ranking do not appropriately account for uncertainties, thus leading to improper ranking. We introduce the stochastic area metric (AM) as a scoring metric for GMMs, which not only informs the analyst of the degree to which observed or test data fit the model but also considers the uncertainties without the assumption of how data are distributed. The AM can be used as a scoring metric or cost function, whose minimum value identifies the model that best fits a given dataset. We apply this metric along with existing testing methods to recent and commonly used European ground motion prediction equations: Bindi et al. (2014, B014), Akkar et al. (2014, A014) and Cauzzi et al. (2015, C015). The GMMs are ranked and their performance analysed against the European Engineering Strong Motion (ESM) dataset. We focus on the ranking of models for ranges of magnitude and distance with sparse data, which pose a specific problem with other statistical testing methods. The performance of models over different ranges of magnitude and distance were analysed using AM, revealing the importance of considering different models for specific applications (e.g., tectonic, induced). We find the A014 model displays good performance with complete dataset while B014 appears to be best for small magnitudes and distances. In addition, we calibrated GMMs derived from a compendium of data and generated a suite of models for the given region through an optimisation technique utilising the concept of AM and ground motion variability. This novel framework for ranking and calibration guides the informed selection of models and helps develop regionally adjusted and application-specific GMMs for better prediction. </p><p> </p>