Accurately and quickly identifying the types of natural and unnatural earthquake events is the basic premise of monitoring, prediction, early warning, and other study in the field of seismology, which is of great significance to the prevention, evaluation, emergency rescue, and other work of earthquake disasters. Convolutional neural network model is a representative artificial intelligence deep learning algorithm, which has been widely used in computer vision, natural language processing, object type identification, and other fields in recent years. In this study, AlexNet convolutional neural network model is selected to study the type identification of 1539 earthquake event waveform records in and around Ningxia Hui Autonomous Region, China. Earthquake event waveform records contain three types: natural earthquake, explosion, and collapse, in which both explosion and collapse are unnatural earthquakes. MATLAB software is used to build the training module and test module for AlexNet convolutional neural network model, and the earthquake event waveform record is transformed into an image format file of 224 times 224 pixels as input parameters. Finally, AlexNet convolutional neural network model has the ability of automatic identification of earthquake event types. The results of this study show that the identification accuracy of earthquake event type in training module is 99.97%, the average value of loss function is 0.001, the identification accuracy of earthquake event type in test set is 98.51%, and the average value of loss function is 0.059. After training and testing, 60 different types of earthquake event waveform records were randomly selected, and AlexNet convolutional neural network model was used to identify them automatically. The automatic identification accuracy of natural earthquakes, explosions, and collapses was 90%, 80%, and 85%, respectively. After training AlexNet convolutional neural network model with earthquake event waveform records, it can have accurate and fast automatic identification ability. The accuracy of automatic identification is comparable to that of professional seismic workers, and the time of automatic identification is greatly reduced compared with that of professional seismic workers. This study can provide an implementation idea of deep learning based on artificial intelligence for the identification of earthquake event types and make contributions to the cause of earthquake prevention and disaster reduction.

In seismic analysis, the undesirable mode of oscillation is determined using analytical and numerical approaches. El Centro earthquake response spectrum plot and El Centro accelerogram are used to compute the response to the seismic event and to compare the results of time-history analysis. The ANSYS software is used for all of the analyses. FE software ANSYS used spectrum and time-dependent analysis to calculate seismic response to earthquake loading. The method of response spectrum is preferable when merely considering the overall reaction of a linearly behaving structure because it requires less processing effort. Time-history analysis has the advantage of being universal. Compared to other approaches, this one has a disadvantageous computational time and convergence issues. The results of the dynamic analyses were in close agreement with one another. Regardless of the shape of the vessel, this method can be used. Additionally, slenderness characteristics can be applied to various tank shapes, such as the intze (truncated conical) or truncated intze (truncated conical). The mass, height and stiffness values of the equivalent circular tank shall be utilized. Analysis of rectangular storage tanks subjected to earthquake excitations is the focus of this study. The tank can be used both above and below ground. Predicting the natural frequencies has been done using a linear three-dimensional finite element method. The tank's height-to-length ratio, soil type, water level, and tank wall thickness are some of the variables that can be used to analyze the tank's performance. Full tanks have higher top displacement and axial force components than half-full (31%), and empty tanks (75 percent). According to the underground tank, empty tank top displacement and axial force components are higher than those in half-full (19%) and full tank circumstances (40 percent). Tank cases that are located above ground have higher base shear values than those that are located below ground (19 percent to 37 percent). Soil type 2 has a stronger shear foundation than soil type 1. (17 percent to 28 percent).