Joint Microseismic Event Detection and Location with a Detection Transformer

During the processes of reservoir stimulation, fluids are injected into a specific area underground. The high-pressure condition created by the fluid injection causes rocks to crack to release the built-up stress, resulting in small earthquakes called microseismic events. Detecting these events in seismic recordings and locating them back to their subsurface locations are important for understanding the subsurface conditions such as fracture networks and fluid flow pathways. This knowledge is critical for applications like carbon storage, geothermal energy extraction, and oil/gas production. Traditional approaches for microseismic 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. These limitations prevent the potential for real-time microseismic monitoring, which is crucial for scientists and engineers to make instant, informed decisions, like optimization of injection strategies. Here, we proposed a machine learning-based procedure for simultaneously detecting and locating microseismic events within a single framework, using a conventional Convolutional Neural Network and an encoder-decoder Transformer. Tests on synthetically-generated and field-collected passive seismic data illustrate the accuracy, efficiency, and potential of the proposed method, which could pave the way for real-time monitoring of microseismic events in the future.