In this study, we purposed to investigate the edge of geostructures and position of existing faults of the Shamakhy–Gobustan and Absheron hydrocarbon containing regions in Azerbaijan. For this purpose, the horizontal gradient, analytic signal, tilt angle, and hyperbolic of tilt angle methods were applied to the first vertical derivative of gravity data instead of Bouguer gravity data. We obtained the maps that show the previous lineaments which were designated by considering the maximum contours of horizontal gradient, analytic signal maps, and zero values of tilt angle, hyperbolic of tilt angle maps. The geometry of basement interface was also modeled utilizing the Parker–Oldenburg algorithm to understand the sediment thickness and coherency or incoherency between the gravity values and basement topography. The lineaments were held a candle to most current tectonic structure map of the study area. It was seen that the techniques used in this study are very effective to determine the old and new lineaments in the Shamakhy–Gobustan and Absheron regions. The epicenter distribution of earthquakes within the study area supports the new lineaments which are extracted by our interpretation. We concluded that better comprehension of Azerbaijan geostructures and its effect on the large scale works will be provided by means of this study. 相似文献
The Genç District is located on the Bingöl Seismic Gap (BSG) of the Eastern Anatolian Fault Zone (EAFZ) with its?~?34.000 residents. The Karl?ova Triple Junction, where the EAFZ, the North Anatolian Fault Zone, and the Varto Fault Zone meet, is only 80 km NE of the Genç District. To make an earthquake disaster damage prediction of the Genç District, carrying a high risk of disaster, we have (1) prepared a new geological map, and (2) conducted a single-station microtremor survey. We defined that three SW-NE trending active faults of the sinistral Genç Fault Zone are cutting through the District. We have obtained dominant period (T) as?<?0.2 s, the amplification factor (A) between 8 and 10, the average shear wave velocity for the first 30 m (Vs30) as?<?300 m/s, and the seismic vulnerability index (Kg) as?>?20, in the central part of the Genç District. We have also prepared damage prediction maps for three bedrock acceleration values (0.25, 0.50, 0.75 g). Our earthquake damage prediction scenarios evidenced that as the bedrock acceleration values increase, the area of soil plastic behavior expands linearly. Here we report that if the average expected peak ground acceleration value (0.55–0.625 g) is exceeded during an earthquake, significant damage would be inevitable for the central part of the Genç District where most of the schools, mosques, public buildings, and hospitals are settled-down.
On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km2. We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha).
A 6.8-magnitude earthquake that occurred on January 24, 2020, has been effective in Turkey’s eastern regions. The earthquake, with recorded peak ground acceleration (PGA) value of 0.292 g, caused the destruction or heavy damage of buildings, especially in the city center of Elaz?? province. The purpose of this paper was to share the results of detailed investigation in the earthquake-stricken area. Additionally, the causes of damages and failures observed in the buildings were compared to those that had occurred in previous earthquakes in Turkey. In this study, the damages observed in especially RC buildings as well as in masonry and rural buildings were summarized, the lessons learned were evaluated, and the results were interpreted with reference to Turkish earthquake codes. In the study, it was particularly emphasized why the building stock underwent such damage even though the buildings were exposed to earthquake acceleration well below the design acceleration values.
This study describes the parametric uncertainty of artificial neural networks (ANNs) by employing the generalized likelihood uncertainty estimation (GLUE) method. The ANNs are used to forecast daily streamflow for three sub-basins of the Rhine Basin (East Alpine, Main, and Mosel) having different hydrological and climatological characteristics. We have obtained prior parameter distributions from 5000 ANNs in the training period to capture the parametric uncertainty and subsequently 125,000 correlated parameter sets were generated. These parameter sets were used to quantify the uncertainty in the forecasted streamflow in the testing period using three uncertainty measures: percentage of coverage, average relative length, and average asymmetry degree. The results indicated that the highest uncertainty was obtained for the Mosel sub-basin and the lowest for the East Alpine sub-basin mainly due to hydro-climatic differences between these basins. The prediction results and uncertainty estimates of the proposed methodology were compared to the direct ensemble and bootstrap methods. The GLUE method successfully captured the observed discharges with the generated prediction intervals, especially the peak flows. It was also illustrated that uncertainty bands are sensitive to the selection of the threshold value for the Nash–Sutcliffe efficiency measure used in the GLUE method by employing the Wilcoxon–Mann–Whitney test. 相似文献
The current earthquake forecast algorithms are not free of shortcomings due to inherent limitations. Especially, the requirement of stationarity in the evaluation of earthquake time series as a prerequisite, significantly limits the use of forecast algorithms to areas where stationary data is not available. Another shortcoming of forecast algorithms is the ergodicity assumption, which states that certain characteristics of seismicity are spatially invariant. In this study, a new earthquake forecast approach is introduced for the locations where stationary data are not available. For this purpose, the spatial activity rate density for each spatial unit is evaluated as a parameter of a Markov chain. The temporal pattern is identified by setting the states at certain spatial activity rate densities. By using the transition patterns between the states, 1- and 5-year forecasts were computed. The method is suggested as an alternative and complementary to the existing methods by proposing a solution to the issues of ergodicity and stationarity assumptions at the same time. 相似文献
In this study, it was aimed to characterize temporal variations of air pollutants for determining contribution to pollution episodes and to obtain correlations between these pollutants. With this aim we used data analysis for measured sulfur dioxide (SO2), particulate matter (PM, black fume and PM10), nitrogen oxides (NOx), ozone (O3), carbon monoxide (CO), methane (CH4), and non‐methane hydrocarbons (NMHC) recorded in Kocaeli, one of the most industrilizated cities of Turkey. Pollutant concentrations were the results of continuous and semi‐automatic measurements. Semi‐automatic measurements of SO2 and PM (black fume) were enclosing period from 1987 to 2008 whereas continuous monitoring of all pollutants included years of 2007–2009. In the first stage of the study daily, monthly, annual, and seasonal variations of pollution were researched. Annual average concentrations were compared with limits set by Air Quality Protection Regulation (AQPR), Air Quality Evaluation and Management Regulation (AQEMR), World Health Organization (WHO), European Union (EU), and National Ambient Air Quality Standards (USEPA). In the following stage relationships between pollutants such as NO2–O3, NOx–CO, NOx–NMHC, and NOx–SO2 were investigated and correlation coefficients were determined as 0.87, 0.56, 0.51, and 0.69, respectively. R2 values of regression models developed from these correlations were 0.78, 0.56, 0.34, and 0.72, respectively. Vehicle density of the traffic was evaluated with NOx–O3 emissions and decrease was seen in NOx emissions due to decreasing vehicle density at weekends whereas O3 concentrations increased. These correlations enable prediction of the parameters that cannot be measured which is important for providing improvement in early warning systems. 相似文献
Istanbul today is probably unique in the world not only in terms of the recognition of its earthquake risk by its inhabitants and administrators, but also in terms of significant steps taken in a such a short time toward the mitigation of its earthquake vulnerabilities. This paper, however, deals with the issues that still remain unattended. 相似文献
Lake water level forecasting is very important for an accurate and reliable management of local and regional water resources. In the present study two nonlinear approaches, namely phase-space reconstruction and self-exciting threshold autoregressive model (SETAR) were compared for lake water level forecasting. The modeling approaches were applied to high-quality lake water level time series of the three largest lakes in Sweden; Vänern, Vättern, and Mälaren. Phase-space reconstruction was applied by the k-nearest neighbor (k-NN) model. The k-NN model parameters were determined using autocorrelation, mutual information functions, and correlation integral. Jointly, these methods indicated chaotic behavior for all lake water levels. The correlation dimension found for the three lakes was 3.37, 3.97, and 4.44 for Vänern, Vättern, and Mälaren, respectively. As a comparison, the best SETAR models were selected using the Akaike Information Criterion. The best SETAR models in this respect were (10,4), (5,8), and (7,9) for Vänern, Vättern, and Mälaren, respectively. Both model approaches were evaluated with various performance criteria. Results showed that both modeling approaches are efficient in predicting lake water levels but the phase-space reconstruction (k-NN) is superior to the SETAR model. 相似文献
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