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901.
Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time.  相似文献   
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International Journal of Earth Sciences - The Saxonian–Bohemian Cretaceous Basin (Elbsandsteingebirge, E Germany and Czech Republic, Elbtal Group) comprises Upper Cretaceous sedimentary rocks...  相似文献   
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International Journal of Earth Sciences - During the late Palaeozoic, lithospheric thinning in part of the Alpine realm caused high-temperature low-to-medium pressure metamorphism and partial...  相似文献   
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International Journal of Earth Sciences - In the Cadomian orogen of the NE Bohemian Massif and of SW Iberia, a post-Gaskiers glacial event dated at c. 565 Ma has been detected. Such...  相似文献   
905.
This report summarizes the seismicity in Switzerland and surrounding regions in the years 2015 and 2016. In 2015, the Swiss Seismological Service detected and located 735 earthquakes in the region under consideration. With a total of 20 earthquakes of magnitude ML ≥ 2.5, the seismic activity of potentially felt events in 2015 was close to the average of 23 earthquakes over the previous 40 years. Seismic activity was above average in 2016 with 872 located earthquakes of which 31 events had ML ≥ 2.5. The strongest event in the analyzed period was the ML 4.1 Salgesch earthquake, which occurred northeast of Sierre (VS) in October 2016. The event was felt in large parts of Switzerland and had a maximum intensity of V. Derived focal mechanisms and relative hypocenter relocations of aftershocks image a SSE dipping reverse fault, which likely also hosted an ML 3.9 earthquake in 2003. Another remarkable earthquake sequence in the Valais occurred close to Sion with four felt events (ML 2.7–3.2) in 2015/16. We associate this sequence with a system of WNW-ESE striking fault segments north of the Rhône valley. Similarities with a sequence in 2011, which was located about 10 km to the NE, suggest the existence of an en-echelon system of basement faults accommodating dextral slip along the Rhône-Simplon line in this area. Another exceptional earthquake sequence occurred close to Singen (Germany) in November 2016. Relocated hypocenters and focal mechanisms image a SW dipping transtensional fault segment, which is likely associated with a branch of the Hegau-Bodensee Graben. On the western boundary of this graben, micro-earthquakes close to Schlattingen (TG) in 2015/16 are possibly related to a NE dipping branch of the Neuhausen Fault. Other cases of earthquakes felt by the public during 2015/16 include earthquakes in the region of Biel, Vallorcine, Solothurn, and Savognin.  相似文献   
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