In combination with other ground-based mapping efforts, the data will be analyzed in the following months to better understand the mechanics of earthquake rupture related to the fault zone rheology, rupture dynamics, and frictional properties along the fault interface. Such high-quality imagery also helps to document with a high fidelity the pattern related to how the faults ruptured to the ground surface and the distribution of off-fault damage. The dataset has proven valuable in documenting subtle and transient rupture features, such as the significant mole-tracks and opening fissures, which were ubiquitous coseismically but degraded during the subsequent summer storm season. This effort represents the first time that an earthquake rupture in the interior of the Tibetan Plateau has been fully and systematically captured by such high-resolution imagery, with an unprecedented level of detail, over its entire length. The complex surface rupture patterns associated with this event were covered comprehensively at 3-6 cm resolution. High-resolution aerial photographs were acquired in the days immediately following the mainshock. The Mw 7.4 Madoi, Qinghai, China earthquake presented a rare opportunity to apply modern unmanned aerial vehicle (UAV) photography method in extreme altitude and weather conditions to image surface ruptures and near-field effects of earthquake-related surface deformations in a remote Tibet. This study demonstrates that the proposed binary classification methods based on machine learning could efficiently and quickly provide the spatial distribution of liquefaction based on post-earthquake emergency satellite images. The spatial distribution of liquefaction pits is also consistent with the formation principle of liquefaction. The recognition accuracies of liquefaction were estimated by four evaluation indices, which all showed a score of about 90%. Finally, a morphological transformation method was used for the post-processing of the extracted liquefaction. The proposed methods trained the two machine learning methods with different numbers of typical samples, then used the trained binary classification methods to extract the spatial distribution of liquefaction. To overcome these shortcomings, this study proposed two binary classification methods (i.e., random forest and gradient boosting decision tree) based on typical samples. However, the current supervised classification methods depend on the precisely delineated polygons of liquefaction by manual and landcover maps. Supervised classification methods are potentially more accurate and do not need pre-earthquake images. Rapid extraction of liquefaction induced by strong earthquakes is helpful for earthquake intensity assessment and earthquake emergency response. Our results show that the massive liquefaction caused by the strong ground shaking during the Maduo (Madoi) earthquake was distributed as the specific local sedimentary environment and the groundwater level changed. The stronger coseismic liquefaction sites correspond to the Eling Lake section, the Yellow River floodplain, and the Heihe River floodplain, where the soil is mostly saturated with loose fine-grained sand and the groundwater level is high. Combined with the sedimentary distribution along-strike of the surface rupture, the mapped liquefaction sites indicate that the differences in the sedimentary environments could cause more intense liquefaction on the western side of the epicenter, where loose Quaternary deposits are widely spread. More than 90% of coseismic liquefaction occurs in the peak ground acceleration (PGA) > 0.50 g, and the liquefaction density is significantly higher in the region with seismic intensity > VIII. The amplification of the seismic waves in the vicinity of the rupture zone enhances the liquefaction effects near it. The coseismic liquefaction density remains on a higher level within 250 m from the surface rupture and decreases in a power law with the increasing distance. We then systematically analyze the coseismic liquefaction distribution characteristics and the possible influencing factors. By utilizing the unmanned aerial vehicle (UAV) photogrammetry technology, we accurately recognize and map 39,286 liquefaction sites within a 1.5 km wide zone along the coseismic surface rupture. The 2021 Mw 7.4 Maduo (Madoi) earthquake that struck the northern Tibetan Plateau resulted in widespread coseismic deformation features, such as surface ruptures and soil liquefaction.
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