Summary
We present a microseismic location method using genetic algorithm full waveform inversion (GAFWI). This method not only considers the travel time of microseismic wavefield, but also uses full waveform information, such as amplitude, frequency, primaries, multiples, etc. With GAFWI, we do not need to pick up the first arrival time or give a good initial position. We should only provide an approximate velocity model and a time window that contains the microseismic event. We discuss the effects of velocity perturbation and noises on location results. When the source wavelet is uncertain, we try to match the observed wavefields using Ricker wavelet wavefields. We also discuss the calculation efficiency of this method, and find that parallel and FDFD are two potential methods. In a complex numerical example, we obtain an approximate right position using GAFWI, but the starting time is with some error. Overall, our method can give good results for microseismic location.
Introduction
Until now, most of the methods for microseismic location only use the travel time of microseismic wavefields to calculate the location, such as double difference method (Tian, 2014). We consider the full waveform information of microseismic wavefield, and solve the location and time by Genetic Algorithm Full Waveform Inversion (GAFWI). Full Waveform Inversion (FWI) uses calculated wavefields to match observed wavefields, and updates the model parameters gradually during the matching process (Virieux, 2009; Zhang, 2014). FWI matches not only travel time, amplitude and frequency, but also full waveform information, such as direct waves, primaries and multiples. With GAFWI, we do not need to pick up the first arrival time. What we need are an approximate velocity model and a time window that contains the microseismic event. When considering real microseismic data, we should deal with many problems, such as the inaccuracy of velocity model, noises in original recordings, the unknown of wavelet and the calculation efficiency. After presenting the fundamentals of GAFWI, we discuss the above problems respectively and show the location results using our method. Finally, we test our method in a complex example.
