Wang Xiuhua, Feng Guangcai, He Lijia, An Qi, Xiong Zhiqiang, Lu Hao, Wang Wenxin, Li Ning, Zhao Yinggang, Wang Yuedong, Wang Yuexin
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
Sensors (Basel). 2023 Jul 12;23(14):6342. doi: 10.3390/s23146342.
On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage.
当地时间2023年2月6日，土耳其中部和南部发生两次地震（震级分别为Mw7.8和Mw7.7），造成多个城市大面积破坏，导致4万人丧生。在本研究中，我们提出了一种结合合成孔径雷达（SAR）幅度和相位相干变化检测的地震建筑物损伤评估与分析方法。我们确定了五个受严重影响城市地区的建筑物损伤情况，并通过测量城市区域和受损区域来计算损伤率。努尔达吉的损伤率最高，为18.93%，伊斯拉希耶的损伤率最低，为7.59%。我们将结果与高分辨率光学图像以及微软团队的人工智能识别结果进行比较，对结果进行了验证。我们还使用像素偏移跟踪（POT）技术和差分干涉合成孔径雷达（D-InSAR）技术，利用哨兵-1A图像获取地表形变，并分析了地表形变与震后城市建筑物损伤之间的关系。结果表明，努尔达吉的城市平均地表形变为0.48米，是最大的，安塔基亚的形变为0.09米，是最小的。我们发现，坡度较陡或离地震断层较近区域的建筑物倒塌风险更高。我们还讨论了SAR图像参数对建筑物变化识别的影响。图像分辨率和观测几何对变化检测结果有很大影响，可以通过多种方式提高分辨率以提升识别精度。我们的研究结果可指导地震灾害评估与分析，并识别地震破坏的影响因素。
