# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 1 Introduction
With advancing technology, an increasing number of geophysicists have started applying DL methods to address diverse problems in the field, including lithofacies classification [20], reservoir identification [21], and seismic data processing and interpretation [22], leading to notable progress. Nevertheless, the utilization of DL in seismic velocity modeling is still in its early stages. Pioneering work by [PERSON] et al. involved using neural networks (NNs) to map synthetic common shot gather data to one-dimensional (1-D) velocity models [23]. [PERSON] and colleagues used artificial neural networks (ANNs) for seismic inversion to estimate 1D velocity models [24]. [PERSON] et al. used deep neural networks (DNNs) to reconstruct 2D velocity models from inputs of velocity spectra [25]. [PERSON] et al. used generative adversarial networks (GANs) for mapping seismic records to velocity models [26]. [PERSON] et al. modified a DNN and proposed end-to-end seismic inversion networks (SeisInvNets) to establish spatial correspondences between each seismic trace and the entire velocity model [27]. [PERSON] et al. developed a DNN to estimate root mean square velocity and layer velocities using co-centered shot gather data [28]. [PERSON] et al. introduced Newton machine learning (NML) inversion, which mitigates cycle-skipping issues in conventional FWI by combining forward modeling using Newton wave propagation and the dimensionality reduction capability of ML [29]. [PERSON] et al. modified the U-net architecture to directly reconstruct salt dome velocity models from raw seismic images [9]. [PERSON] et al. proposed a combination of a U-Net and three fully connected networks (UCNN) for multi-parameter pre-stack seismic inversion with constraints on sparse reflection coefficients [30]. [PERSON] et al. developed a DL-based pre-stack seismic inversion architecture (IHGNN) that improved network accuracy and robustness by introducing a priori loss function and achieved more reasonable inversion results on real data through transfer learning [31]. While DL has made significant progress in active source velocity modeling, the exploration of microseismic velocity inversion based on DL is still in its early stages. [PERSON] et al. used ResNet50, a 50-layer deep residual network, for the inversion of microseismic data recorded by Distributed Acoustic Sensing (DAS) [32]. In the subsequent year, they further modified the AlexNet architecture, enabling real-time localization and velocity model updates for inter-well microseismic data under horizontally layered (Vertical Transverse Isotropy, VTI) media conditions [19]. In summary, DL has revolutionized the field of geophysics by introducing new tools and methods, leading to significant advancements in active source velocity modeling. Researchers have predominantly employed and adapted various neural networks, or even proposed new ones, to make them more suitable for seismic inversion applications. This progress has seen the evolution from initial 1D models to more complex 2D and even 3D velocity modeling. However, velocity inversion for microseismic events is still in its early stages and encounters several challenges, which highlights the need for further research and exploration in this area. This paper focuses on velocity modeling in the microseismic field. We adopted and modified the Attention U-Net architecture [33] to automatically extract hidden features and patterns from microseismic records and velocity models. The paper also discusses the degree of coupling of microseismic events individually. Simulate records for single-event and multi-event microseisms with randomly distributed source locations, along with corresponding velocity models, are used for training. Ultimately, real-time reconstruction of microseismic velocity models has been achieved. The effectiveness of this approach is demonstrated through numerical tests using simulated data generated from various complex velocity structures, including anticlines, synclines, and anomalous velocity models.