Composite borehole profiling combined with trenching is an effective way to acquire evidence of past ruptures of buried active faults. In this study, three composite borehole profiles and a large-scale trench excavation were carried out across the surface rupture zone of the 1976 M s 7.8 Tangshan earthquake. The following three major conclusions have been reached. (1) The surface rupture zone of the 1976 earthquake extends more than 47 km long to the south of Tangshan city, passing to the west of Sunjialou, to Daodi town in Fengnan County, to Xihe in Fengnan County. (2) The surface rupture zone is divided into south and north branches. The north branch has mainly right-lateral strike-slip motion, and the vertical displacement of the surface is up on the west and down on the east. On the other hand, the vertical displacement of the south branch is up on the east and down on the west, accompanied by some right-lateral slip. Such a faulting style cannot be explained by the movement of a single normal or reverse fault, but is consistent with the vertical displacement field induced by the right-lateral strike-slip of the fault belt. The drilling and trenching data from this study verify that such activity continued through the Late Quaternary on the Tangshan Fault. (3) The fault planes exhumed by trenching and the dislocations of strata revealed by the boreholes indicate that multiple faulting events occurred on the Tangshan Fault in the Late Quaternary. The timing of three ruptures prior to the 1976 earthquake was 7.61-8.13, >14.57, and 24.21-26.57 ka BP. Counting the earthquake of 1976, the recurrence interval of the four strong events is about 6.7 to 10.8 ka. On one of the three borehole profiles, the Niumaku profile, nine faulting events were detected since 75.18 ka BP with an average interval of 8.4 ka. In addition, this paper also discusses the difference between the Late Quaternary sedimentary environments to the north and south of Tangshan city based on stratum dating.Tangshan earthquake, surface rupture zone, multi-stage activity, earthquake recurrence interval, paleoseismic trenching, borehole profile Citation:Guo H, Jiang W L, Xie X S. Late-Quaternary strong earthquakes on the seismogenic fault of the 1976 M s 7.8 Tangshan earthquake, Hebei, as revealed by drilling and trenching.

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Tourism ecological security is an important basis for measuring the realization of the “double carbon” goal of regional tourism. Based on the drivers, pressures, state, impact and response model of intervention (DPSIR), an evaluation index system of tourism ecological security in the old revolutionary region of the Dabie Mountains is constructed. The entropy technique for order of preference by similarity to ideal solution (TOPSIS) method, spatial variation model, standard deviation ellipse model and gray dynamic model are used to explore the spatial and temporal evolution characteristics of the tourism ecological security level in the old revolutionary region of the Dabie Mountains from 2001 to 2020, and to forecast its future spatial development pattern. The study shows that (1) the average value in tourism ecological security in that region is 0.3153. Moreover, the comprehensive index increased from 0.2296 in 2001 to 0.4302 in 2020, which shows a steady improvement. The security status has improved from insecure to critically secure; (2) the number of municipalities that are insecure or relatively insecure in the region is gradually decreasing, while the number of municipalities that are located within critically secure and relatively secure cities and towns in the region is gradually decreasing. Moreover, an increasing number of cities and towns are critically secure and safe, and the whole region is now in the critical transition period between an average to low level to an average to high level of tourism ecological security; (3) the degree of spatial variation in tourism ecological security is increasing, the features of spatial differentiation are more obvious, and the overall spatial pattern of “Hubei > Henan > Anhui” is presented. (4) The spatial distribution pattern for tourism ecological security is “southeast-northwest”, and the spatial distribution range has undergone the process of “convergence to diffusion”. (5) The spatial distribution pattern in tourism ecological security is “southeast-northwest”, and the spatial distribution range has undergone a process of “convergence to diffusion”. This shows expansion toward the southeast that reflects a certain spatial spillover effect and “convergence” toward the northwest, with no obvious spatial spillover effect.

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Short term load forecasting (STLF) is one of the basic techniques for economic operation of the power grid. Electrical load consumption can be affected by both internal and external factors so that it is hard to forecast accurately due to the random influencing factors such as weather. Besides complicated and numerous internal patterns, electrical load shows obvious yearly, seasonal, and weekly quasi-periodicity. Traditional regression-based models and shallow neural network models cannot accurately learn the complicated inner patterns of the electrical load. Long short-term memory (LSTM) model features a strong learning capacity to capture the time dependence of the time series and presents the state-of-the-art performance. However, as the time span increases, LSTM becomes much harder to train because it cannot completely avoid the vanishing gradient problem in recurrent neural networks. Then, LSTM models cannot capture the dependence over large time span which is of potency to enhance STLF. Moreover, electrical loads feature data imbalance where some load patterns in high/low temperature zones are more complicated but occur much less often than those in mild temperature zones, which severely degrades the LSTM-based STLF algorithms. To fully exploit the information beneath the high correlation of load segments over large time spans and combat the data imbalance, a deep ensemble learning model within active learning framework is proposed, which consists of a selector and a predictor. The selector actively selects several key load segments with the most similar pattern as the current one to train the predictor, and the predictor is an ensemble learning-based deep learning machine integrating LSTM and multi-layer preceptor (MLP). The LSTM is capable of capturing the short-term dependence of the electrical load, and the MLP integrates both the key history load segments and the outcome of LSTM for better forecasting. The proposed model was evaluated over an open dataset, and the results verify its advantage over the existing STLF models.