A Compact Program for 3D Passive Seismic Source-Location Imaging

Microseismic source-location imaging is important for inferring the dynamic status of reservoirs during hydraulic fracturing. The accuracy and resolution of the located microseismic sources are closely related to the imaging technique. We present an open-source program for high-fidelity and high-resolution 3D microseismic source-location imaging. The presented code is compact in the sense that all required subroutines are included in one single C program, based on which seismic wavefields can be propagated either forward during a synthetic test or backward during a real time-reversal imaging process. The compact C program is accompanied by a Python script known as the SConstruct file in the Madagascar open-source platform to compile and run the C program. The velocity model and recorded microseismic data can be input using the Python script. This compact program is useful for educational purposes and for future algorithm development. We introduce the basics of the imaging method used in the presented package and present one representative synthetic example and a field data example. The results show that the presented program can be reliably used to locate source locations using a passive seismic dataset.

The adaptive particle swarm optimization technique for solving microseismic source location parameters

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.