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We have determined Lg Coda Q (Q c Lg ) from ground motion recorded at seven broadband stations in Australia, using a stacked spectral ratio method. In spite of the relatively small number of events and less than optimum station coverage, we were able to use those data to obtain a tomographic map Q c Lg and its frequency dependence, at 1 Hz for almost the entire island continent.

Recently, the convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still remains challenging. First, the extreme large input size makes the existing object detection solutions too slow for practical use. Second, the massive and complex backgrounds cause serious false alarms. Moreover, the ultratiny objects increase the difficulty of accurate detection. To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network (R 2 -CNN), composing of backbone Tiny-Net, intermediate global attention block, and final classifier and detector. Tiny-Net is a lightweight residual structure, which enables fast and powerful features extraction from inputs. Global attention block is built upon Tiny-Net to inhibit false positives. Classifier is then used to predict the existence of targets in each patch, and detector is followed to locate them accurately if available. The classifier and detector are mutually reinforced with end-to-end training, which further speed up the process and avoid false alarms. Effectiveness of R 2 -CNN is validated on hundreds of GF-1 images and GF-2 images that are 18 000 × 18 192 pixels, 2.0-m resolution, and 27 620 × 29 200 pixels, 0.8m resolution, respectively. Specifically, we can process a GF-1 image in 29.4 s on Titian X just with single thread. According to our knowledge, no previous solution can detect the tiny object on such huge remote sensing images gracefully. We believe that it is a significant step toward practical real-time remote sensing systems.Index Terms-Object detection, remote sensing images, remote sensing region-based convolutional neural network(R 2 -CNN).

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In an effort to mitigate ecological environments and improve human well-being, the Chinese government's largest-ever relocation and settlement programme is underway. Measuring livelihood resilience and further assessing its impact hold the key to strengthening adaptive capacity and well-being in poverty resettlements. Using a household survey of contiguous poor areas in Southern Shaanxi, China, this research proposes a framework to examine livelihood resilience and its impact on livelihood strategies in the context of poverty alleviation resettlement. To provide more comprehensive empirical evidence, we drew on three dimensions of the previously proposed livelihood resilience framework: buffer capacity, self-organizing capacity, and learning capacity. The results show that capital endowments, social cooperation networks, transportation convenience, and skills acquired from education and rural-urban migration can significantly affect the construction of livelihood resilience. The resilience of households that were relocated because of ecological restoration is the highest, followed by households relocated because of disasters; households relocated because of poverty reduction attempts have the lowest resilience. As for indicators of livelihood resilience, physical capital assets and previous work experience play a major role in household livelihood strategies for pursuing non-farming activities, while household size, stable income, social capital, and information sharing result in diversified livelihood strategies. These findings provide policy implications for enhancing livelihood resilience capacities and improving the scope of available livelihood strategies to emerge from the poverty trap and to adapt to the new environment.

[1] We present new maps of Lg coda Q (Q o and its frequency dependence) at 1-Hz that cover virtually all of Eurasia. Q o is relatively high, up to 650 or more, in the east European, Siberian, Indian, and Kazakh platforms but is surprisingly low (300-500) in the Arabian platform and western portion of the Siberian platform as well as in Great Britain and the northwestern portion of mainland Europe. It is generally low throughout the Tethysides orogenic belt, but there too it displays substantial regional variations (150-400). Most Q o anomalies appear to be related to the tectonic history of the Eurasian crust. The four regions with lowest values coincide with four of Eurasia's most active concentrations of earthquake activity. Observed Eurasian Q o values are consistent with a previously determined empirical plot of global values in which Q o in any region is proportional to the time elapsed since the most recent episode of tectonic or orogenic activity there. Comparisons of the new Q o map with maps of Rayleigh wave velocity, temperature, subducted lithosphere, and seismicity lead us to infer that Lg coda wave attenuation is most easily explained by energy lost in moving fluids through permeable rock or in being scattered from fluid-enhanced zones. The fluids are likely to have originated by hydrothermal release from subducting lithosphere or other upper mantle heat sources. Anomalies in Q o and other lithospheric properties in some cases extend well outside the Tethysides belt and other regions where upper mantle heat sources are known to occur. Possible explanations include earlier episodes of subduction activity, crustal deformation due to collision of the Afro-Arabian and Indian plates, or broader than expected back-arc activity beneath Eurasia.Citation: Mitchell, B. J., L. Cong, and G. Ekström (2008), A continent-wide map of 1-Hz Lg coda Q variation across Eurasia and its relation to lithospheric evolution,