# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
###### Abstract  
With the continuous depletion of conventional oil and gas reservoir resources, the beginning of exploration and development of unconventional oil and gas reservoir resources has led to the rapid development of microseismic monitoring technology. Addressing the challenges of low signal-to-noise ratio and inaccurate localization in microseismic data, we propose a new neural network MA_W-Net based on the U-Net network with the following improvements: (1) The foundational U-Net model was refined by evolving the single-channel decoder into a two-channel decoder, aimed at enhancing microseismic event localization and noise suppression capabilities. (2) The integration of attention mechanisms such as the convolutional block attention module (CBAM), coordinate attention (CA), and squeeze-and-excitation (SE) into the encoder to bolster feature extraction. We use synthetic data for evaluating the proposed method. Comparing with the normal U-net network, our accuracy in seismic recordings with a signal-to-noise ratio of \(-15\) is improved from 78 percent to 93.5 percent, and the average error is improved from 2.60 m to 0.76 m. The results show that our method can accurately localize microseismic events and denoising processes from microseismic records with a low signal-to-noise ratio. microseismic; deep learning; event location; noise reduction; attention mechanism +
Footnote †: journal: _Applied_  
with accuracy, as they can only provide the location of one microseismic event, which may be compromised by the noise from concurrent events. [PERSON] et al. [17] introduced ConvNetQuake, the inaugural convolutional neural network designed for earthquake detection and localization, aiming to tackle seismic monitoring challenges through supervised classification. [PERSON] et al. [18] crafted a deep neural network tailored to seismic source offset imaging, capable of identifying two distinct events within diffraction stacked images from continuous seismic records. [PERSON] et al. [19] developed two convolutional neural networks for identifying multiple earthquake sources: one to categorize event counts and another to predict several epicenter locations. [PERSON] et al. [20] leveraged synthetic data to train a U-Net model, subsequently applying it to field data for microseismic event localization, demonstrating that real data can be effectively augmented with synthetic data for network training purposes. [PERSON] et al. [21] unveiled a technique for microseismic source localization utilizing deep reinforcement learning. In this study, we propose a novel neural network based on U-Net, named MA_W-Net, which combines low-resolution semantic information with high-resolution localized spatial information using skip connections. However, the existing U-Net architectural system has some limitations, such as the relatively small number of backbone layers and the feature extraction capability not being as good as that of other networks, which may lead to lower accuracy in localizing the earthquake source point. To solve these problems, we have made the following improvements: (1) We built a deep neural network with one encoder and two decoder structures based on U-Net, which is a multi-task learning based on the parameter hard-sharing mechanism to realize microseismic localization and denoising in a strong noise environment. Among them, the main task is to localize the microseismic source and the auxiliary task is denoising. The auxiliary task can help the network perform better in localizing microseismic events with a low SNR. And we define the label of the localization task as a Gaussian mixture model which contains the source location information of different microseismic events. (2) We have integrated multiple attention mechanisms into MA_W-Net, including CBAM in the encoder and the CA and SE modules in the decoder.