Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic Events

One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the large subsurface uncertainty and complex governing physics. Traditional leakage detection and monitoring techniques rely on geophysical observations including seismic. However, the resulting accuracy of these methods is limited because of indirect information they provide requiring expert interpretation, therefore yielding in-accurate estimates of leakage rates and locations. In this work, we develop a novel machine-learning detection package, named“SeismicNet”, which is based on the deep densely connected neural network. To validate the performance of our proposed leakage detection method, we employ our method to a natural analog site at Chimayó, New Mexico. The seismic events in the data sets are generated because of the eruptions of geysers, which is due to the leakage of CO2. In particular, we demonstrate the efficacy of our Seismic-Net by formulating our detection problem as an event detection problem with time series data. A fixed-length window is slid throughout the time series data and we build a deep densely connected network to classify each window to determine if a geyser event is included. Through our numerical tests, we show that our model achieves precision/recall as high as 0.889/0.923. Therefore, our Seismic-Net has a great potential for detection of CO2 leakage.