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Abstract A catastrophic Mw7.8 earthquake hit southeast Turkey and northwest Syria on February 6th, 2023, leading to more than 44 k deaths and 160 k building collapses. The interpretation of earthquake-triggered building damage is usually subjective, labor intensive, and limited by accessibility to the sites and the availability of instant, high-resolution images. Here we propose a multi-class damage detection (MCDD) model enlightened by artificial intelligence to synergize four variables, i.e., amplitude dispersion index (ADI) and damage proxy (DP) map derived from Synthetic Aperture Radar (SAR) images, the change of the normalized difference built-up index (NDBI) derived from optical remote sensing images, as well as peak ground acceleration (PGA). This approach allows us to characterize damage on a large, tectonic scale and a small, individual-building scale. The integration of multiple variables in classifying damage levels into no damage, slight damage, and serious damage (including partial or complete collapses) excels the traditional practice of solely use of DP by 11.25% in performance. Our proposed approach can quantitatively and automatically sort out different building damage levels from publicly available satellite observations, which helps prioritize the rescue mission in response to emergent disasters. 

SUMMARY Due to the partly diffuse character of ambient noise, the retrieval of amplitude information and attenuation from noise cross-correlations has been difficult. Here, we apply the temporal reweighting method proposed by Weaver & Yoritomo to seismic data from the USArray in the central-midwest US. The results show considerable improvements in retrieved Green's functions in both symmetry and causality. The reweighting is able to make the effective incident noise field more isotropic (though not yet truly isotropic). It produces more robust amplitude measurements and also makes both the causal and anticausal parts usable. This suggests that it could be widely applicable for retrieval of Green's functions from ambient noise for attenuation study. The results also suggest an alternative measure of signal-to-noise ratio that complements the conventional one.