Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture

Abstract

In microseismic monitoring, the reconstruction of a reliable velocity model is essential for precise seismic source localization and subsurface imaging. However, traditional methods for microseismic velocity inversion face challenges in terms of precision and computational efficiency. In this paper, we use deep learning (DL) algorithms to achieve precise and efficient real-time microseismic velocity modeling, which holds significant importance for ensuring engineering safety and preventing geological disasters in microseismic monitoring. Given that this task was approached as a non-linear regression problem, we adopted and modified the Attention U-Net network for inversion. Depending on the degree of coupling among microseismic events, we trained the network using both single-event and multi-event simulation records as feature datasets. This approach can achieve velocity modeling when dealing with inseparable microseismic records. Numerical tests demonstrate that the Attention U-Net can automatically uncover latent features and patterns between microseismic records and velocity models. It performs effectively in real time and achieves high precision in velocity modeling for Tilted Transverse Isotropy (TTI) velocity structures such as anticlines, synclines, and anomalous velocity models. Furthermore, it can provide reliable initial models for traditional methods.

Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation

“…Since the predicted velocity models are almost the same as the real ones and thus do not contain much information, we do not show those simple results in this manuscript. Zhou et al [29] demonstrated the effectiveness of a modified Attention Unet in predicting complex synthetic velocity models with microseismic records. They did not consider field microseismic data and adopted Gaussian noise to evaluate the robustness of the model, while we used field data to enhance the synthetic data by data augmentation operations.…”

“…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.…”