论文标题
地震射击使用基于特征融合的神经网络收集噪声定位
Seismic Shot Gather Noise Localization Using a Multi-Scale Feature-Fusion-Based Neural Network
论文作者
论文摘要
基于深度学习的模型，例如卷积神经网络，已经提高了计算机视觉的各个部分。但是，这项技术很少应用于地震射击收集噪声定位问题。这封信对基于多尺度融合的网络进行地震射击场噪声定位的多尺度网络的有效性进行了调查。本文中，我们描述以下内容：（1）基于6,500个地震图的地震噪声定位的现实世界数据集的构建； （2）一种多尺度基于特征融合的检测器，该检测器使用Mobilenet与特征金字塔网作为骨架； （3）用于盒子分类/回归的单个Shot多框检测器。此外，我们建议使用焦点损耗函数，以提高检测器的预测准确性。拟议的检测器在我们的经验评估中实现了78.67 \％的[email protected]。
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.
