基于深度学习的模型（例如卷积神经网络）已经将计算机视觉的各个部分进行了升级。但是，该技术很少用于地震散粒噪声的定位问题。..

Seismic Shot Gather Noise Localization Using a Multi-Scale Feature-Fusion-Based Neural Network

Deep learning-based models, such as convolutional neural networks, have advanced various segments of computer vision. However, this technology is rarely applied to seismic shot gather noise localization problem.This letter presents an investigation on the effectiveness of a multi-scale feature-fusion-based network for seismic shot-gather noise localization. Herein, we describe the following: (1) the construction of a real-world dataset of seismic noise localization based on 6,500 seismograms; (2) a multi-scale feature-fusion-based detector that uses the MobileNet combined with the Feature Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box classification/regression. Additionally, we propose the use of the Focal Loss function that improves the detector's prediction accuracy. The proposed detector achieves an [email protected] of 78.67\% in our empirical evaluation.