# Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation
###### Abstract  
Micoseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent processing. It is challenging for traditional methods to realize efficient and accurate microseismic velocity inversion due to the low signal-to-noise ratio of field data. Deep learning can efficiently invert the velocity model by constructing a mapping relationship from the waveform data domain to the velocity model domain. The predicted and reference values are fitted with mean square error as the loss function. To reduce the feature mismatch between the synthetic and real microseismic data, data augmentation is also performed using correlation and convolution operations. Moreover, a hybrid training strategy is proposed by combining synthetic and augmented data. By testing real microseismic data, the results show that the Unet is capable of high-resolution and robust velocity prediction. The data augmentation method complements more high-frequency components, while the hybrid training strategy fully combines the low-frequency and high-frequency components in the data to improve the inversion accuracy. microseismic velocity inversion; deep learning; data augmentation; hybrid training; Unet 2020 20  
Recently, deep learning (DL) has shown excellent capabilities for nonlinear mapping function approximation in computer vision, especially in the tasks of reconstructing models and high-resolution images [15; 16]. The development of DL has also brought new opportunities to seismic and microseismic data processing and inversion [17], such as signal denoising [18], signal identification and classification [19; 20], first-arrival picking [21; 22; 23], source location [24], and velocity model building and calibration [25]. Using seismic waveforms as the feature input and velocity models as the labels, the trained models with the nonlinear mapping capability of neural networks can effectively predict velocity models from seismic waveforms. There are already several studies on using DL algorithms to invert velocity models. [PERSON] et al. [26] extracted features from the acquired seismic data and proposed using deep convolutional neural networks (DCNNs), instead of seismic tomography, to reconstruct velocity models. [PERSON] et al. [27] proposed a supervised deep fully convolutional neural network (FCN) approach to build velocity models directly from raw seismic data. However, there are only a few studies on DL-based downhole microseismic velocity inversion to take advantage of the nonlinear mapping ability of deep neural networks (DNNs) to carry out velocity inversion tasks [28; 29]. Unlike velocity model inversion in active seismology, there is generally only one velocity model corresponding to hundreds, possibly even thousands, of microseismic events. The combination of abundant microseismic events within restricted regions and limited velocity model information hinders dataset construction and network performance. Additionally, microseismic processing and interpretation is dependent on activities and geology in the region of interest, which may limit the availability of past microseismic events for DL algorithms. In this sense, the training data play a vital role to ensure the learning performance of the network. FWI in active seismology relies heavily on low-frequency components [30], while field microseismic data generally contain higher frequency contents than active seismic data, and the high-frequency information might be missing in synthetic data considering the computational expense. [PERSON] et al. [31] found that integrating physical information with synthetic data can improve the effectiveness of the training data and network performance. [PERSON] et al. [32] employed the domain adaptation approach to introduce real signal features into the synthetic data by correlation and convolution operations.