Microseismic source localization methods with deep learning can directly predict the source location from recorded microseismic data, showing remarkably high accuracy and efficiency. Two main categories of deep learning-based localization methods are coordinate prediction methods and heatmap prediction methods. Coordinate prediction methods provide only a source coordinate and generally do not provide a measure of confidence in the source location. Heatmap prediction methods require the assumption that the microseismic source is located on a grid point. Thus, they tend to provide lower resolution information and localization results may lose precision. This study reviews and compares previous methods for locating the source based on deep learning. To address the limitations of existing methods, we devise a network fusing a convolutional neural network and a Transformer to locate microseismic sources. We first introduce the multi-modal heatmap combining the Gaussian heatmap and the offset coefficient map to represent the source location. The offset coefficients are utilized to correct the source locations predicted by the Gaussian heatmap so that the source is no longer confined to the grid point. We then propose a fusion network to accurately estimate the source location. A gated multi-scale feature fusion module is developed to efficiently fuse features from different branches. Experiments on synthetic and field data demonstrate that the proposed method yields highly accurate localization results. A comprehensive comparison of coordinate prediction method and heatmap prediction methods with our proposed method demonstrates that the proposed method outperforms the other methods.

深度学习的微震震源定位方法可以直接根据记录的微震数据预测震源位置，具有极高的准确性和效率。基于深度学习的定位方法的两大类是坐标预测方法和热图预测方法。坐标预测方法仅提供源坐标，并且通常不提供源位置的置信度测量。热图预测方法需要假设微震源位于网格点上。因此，它们往往提供较低分辨率的信息，并且定位结果可能会失去精度。本研究回顾并比较了以往基于深度学习的溯源方法。为了解决现有方法的局限性，我们设计了一种融合卷积神经网络和 Transformer 的网络来定位微震源。我们首先引入结合高斯热图和偏移系数图的多模态热图来表示源位置。利用偏移系数来校正高斯热图预测的源位置，使源不再局限于网格点。然后，我们提出一个融合网络来准确估计源位置。开发了门控多尺度特征融合模块，以有效地融合来自不同分支的特征。合成数据和现场数据的实验表明，所提出的方法可以产生高度准确的定位结果。坐标预测方法和热图预测方法与我们提出的方法的全面比较表明，我们提出的方法优于其他方法。