Abstract. An intelligent method is presented for locating
a microseismic source based on the particle swarm optimization (PSO) concept. It
eliminates microseismic source locating errors caused by the inaccurate velocity
model of the earth medium. The method uses, as the target of PSO, a global
minimum of the sum of squared discrepancies between differences of modeled
arrival times and differences of measured arrival times. The discrepancies
are calculated for all pairs of detectors of a seismic monitoring system.
Then, the adaptive PSO algorithm is applied to locate the microseismic
source and obtain optimal value of the P-wave velocity. The PSO algorithm
adjusts inertia weight, accelerating constants, the maximum flight velocity
of particles, and other parameters to avoid the PSO algorithm trapping by
local optima during the solution process. The origin time of the
microseismic event is estimated by minimizing the sum of squared
discrepancies between the modeled arrival times and the measured arrival
times. This sum is calculated using the obtained estimates of the
microseismic source coordinates and P-wave velocity. The effectiveness of
the PSO algorithm was verified through inversion of a theoretical model and
two analyses of actual data from mine blasts in different locations.
Compared with the classic least squares method (LSM), the PSO algorithm displays
faster convergence and higher accuracy of microseismic source location.
Moreover, there is no need to measure the microseismic wave velocity in
advance: the PSO algorithm eliminates the adverse effects caused by error in
the P-wave velocity when locating a microseismic source using traditional
methods.
Prediction of shear wave velocity in underground layers using Particle Swarm Optimization