Automatic Microseismic Event Location Using Deep Neural Networks

  • Yuanyuan Yang (King Abdullah University of Science and Technology (KAUST) (Creator)



In contrast to large-scale earthquakes which are caused when energy is released as a result of rock failure along a fault, microseismic events are caused when human activities, such as mining or oil and gas production, change the stress distribution or the volume of a rockmass. During such processes, microseismic event location, which aims at estimating source locations accurately, is a vital component of observing, diagnosing and acting upon the dynamic indications in reservoir performance by tracking the fracturing properly. Conventional methods for microseismic event location face considerable drawbacks. For example, traveltime based methods require manual labor in traveltime picking and thus suffer from the heavy workload of human interactions and manmade errors. Migration based and waveform inversion based location methods demand large computational memory and time for simulating the wavefields, especially in face of tens of thousands of microseismic events recorded. In this thesis research, we developed an approach based on a deep CNN for the purpose of microseismic event location, which is completely automatic with no human interactions like traveltime picking and also computationally friendly due to no requirement of wavefield simulations. An example in which the network is well-trained on the synthetic data from the smooth SEAM model and tested on the true SEAM model has shown its accuracy and efficiency. Moreover, we have proved that this approach is not only feasible for the cases with a uniform receiver distribution, but also applicable to cases where the passive seismic data are acquired with an irregular spacing geometry of sensors, which makes this approach more practical in reality.
Date made available2021
PublisherKAUST Research Repository

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