Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulation and evolution. Conventional approaches for event detection and location often suffer from manual intervention and/or heavy computation, while current machine learning-assisted approaches typically address detection and location separately; such limitations hinder the potential for real-time microseismic monitoring. We propose an approach to unify event detection and source location into a single framework by adapting a Convolutional Neural Network backbone and an encoder-decoder Transformer with a set-based Hungarian loss, which is applied directly to recorded waveforms. The proposed network is trained on synthetic data simulating multiple microseismic events corresponding to random source locations in the area of suspected microseismic activities. A synthetic test on a 2D profile of the SEAM Time Lapse model illustrates the capability of the proposed method in detecting the events properly and locating them in the subsurface accurately; while, a field test using the Arkoma Basin data further proves its practicability, efficiency, and its potential in paving the way for real-time monitoring of microseismic events.

翻译：微震事件检测与定位是微震监测中的两个核心环节，能够为储层压裂改造与演化过程提供宝贵的地下信息。传统的事件检测与定位方法常受限于人工干预和/或繁重的计算负担，而当前机器学习辅助的方法通常将检测与定位分开处理；这些局限性阻碍了实时微震监测的实现。我们提出一种方法，通过采用卷积神经网络骨干架构和编码器-解码器Transformer结构，结合基于集合的匈牙利损失函数，将事件检测与震源定位统一到单一框架中，并直接应用于记录的波形数据。所提出的网络在使用合成数据上进行训练，这些数据模拟了疑似微震活动区域内随机震源位置对应的多个微震事件。在SEAM时延模型的二维剖面上进行的合成测试表明，该方法能够正确检测事件并准确确定其地下位置；同时，利用Arkoma盆地数据进行的现场测试进一步验证了其实用性、高效性，及其在为微震事件实时监测铺平道路方面的潜力。