# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 1 Introduction  
Microseismic technology refers to the research and application of faint seismic signals generated by underground geological deformations or rock fractures. Real-time monitoring and analysis of these microseismic signals provide valuable information about underground structures and material distribution. This technology finds wide applications in various fields, including mining monitoring [1], geotechnical engineering [2], geological hazard prevention [3], and tectonic studies [4]. In recent years, significant advancements in microseismic technology research have primarily focused on seismic source localization techniques [5] and microseismic data processing [6; 7]. However, to further enhance the utility of microseismic monitoring for the precise assessment of underground target area structures and timely decision-making, efficient and reliable velocity modeling is essential [8]. Seismic velocity modeling plays a crucial role in geophysical exploration as it involves inferring the underground velocity structure based on observed seismic records. This technique provides valuable insights into the geological structures [9]. There are two main categories of seismic velocity modeling: active source velocity modeling and passive source velocity modeling. Common methods include time-domain Normal Moveout (NMO) velocity analysis [10; 11], travel time tomography [12], and full-waveform inversion (FWI) [13]. Currently, these methods are primarily used for active source seismic data velocity modeling. However, when dealing with passive source records such as microseismic data or background noise, a common approach involves reconstructing them into a format similarto active source records before using conventional methods to obtain velocity information [14; 15]. This reconstruction process introduces various optimization parameters and uncontrollable factors, resulting in increased computational costs. Moreover, it may not meet the high-efficiency requirements for velocity model updates in microseismic monitoring. Therefore, in the current phase of research, there is a growing emphasis on the direct inversion of underground velocity structures using microseismic records in the passive source domain [16; 17; 18]. In the process of microseismic monitoring, the ability to conduct real-time analysis and make real-time decisions is crucial for ensuring the success of engineering projects and the prevention of geological disasters. This critical capability hinges upon precise seismic source localization, which, in turn, relies on the accuracy of the subsurface velocity model. Therefore, the updating of velocity models should be both efficient and reliable. At present, FWI stands as the most accurate method for constructing velocity models using prestack seismic data. However, FWI heavily relies on low-frequency information, and incorporating high-frequency microseismic data into FWI increases the likelihood of encountering cycle-skipping issues. Traditional algorithms face constraints related to source positions, unknown source wavelets, and high computational costs. To overcome the challenges posed by traditional methods and meet the computational efficiency requirements of microseismic monitoring, this paper has chosen to introduce the method of DL. By using the neural network to directly invert velocity models from microseismic data, dynamically capturing changes in reservoir parameters, which would enhance the accuracy of seismic source localization and help facilitate real-time decision-making. This enables better disaster prediction and safety evaluation [19]. DL, a significant branch of machine learning (ML), has experienced rapid development and extensive application in various fields, with computer vision being particularly prominent.