An Anisotropic Velocity Model for Surface Microseismic Monitoring

Abstract

The invention provides an anisotropic velocity model for ground microseismic monitoring and its automatic construction method, including: S1: construction and calculation of a nine-parameter velocity model; S2: automatic adjustment of the initial velocity; S3: correction of the velocity model by cross-validation parameter. Compared with the traditional horizontal layered velocity model, the present invention can better reflect the trend that the velocity changes with the direction of seismic wave propagation, and can better simulate the propagation of actual seismic waves, so the established velocity model is more accurate. Using such The positioning accuracy of the speed model is higher; by automatically adjusting the initial speed, the trouble of manually adjusting the initial speed is eliminated, and the determination of the initial speed is completed more efficiently; The speed model parameters of all groups are linearly superimposed as weights, and the corrected speed model parameters can effectively improve the positioning accuracy.

Description

technical field [0001] The invention belongs to the field of microseismic monitoring, in particular to an anisotropic velocity model for ground microseismic monitoring. Background technique [0002] When some production activities occur, the stress around the original or newly created cracks in the rock will concentrate, and the strain energy will increase. When the external force increases to a certain extent, microscopic deformation will occur in the crack area, and part of the strain energy will be expressed as elastic waves. Forms are released, producing smaller earthquakes known as "microseismic". [0003] Earthquakes generally appear as clear pulses in seismic records. The stronger the microseismic event, the more obvious the pulse, and vice versa, the weaker the pulse. The occurrence of microseismic is complex in space and time, and its signal is easily affected by surrounding noise, and various media in the formation will absorb seismic waves and reduce their energy...

AI Technical Summary

Problems solved by technology

The Inglada algorithm is simple to implement and uses a single-layer velocity model, but because it is contrary to the horizontal layered velocity model between formations, the positioning accuracy is not high; the Geiger method uses a horizontal layered velocity model and iterative technology, so the positioning accuracy is better than that of Inglada. The algorithm has been improved; the grid search method is a basic global optimization algorithm. This method first needs to limit the range of solutions, set the size of the grid, divide the solution space into grids, and then use a grid as a unit Traverse the solution space to find the optimal solution in the solution space. This method depends on the determination of the solution space and the size of the grid that divides the solution space. The larger the grid, the lower the positioning accuracy, and the smaller the grid, the higher the positioning accuracy , but the calculation amount is also larger

[0006] Through the research, it is found that the propagation of P- and S-wave velocity in the formation medium is characterized by horizontal layers, that is, the velocity of P- and S-wave propagation is different in different layers. The existing microseismic positioning methods all use the horizontal layered velocity model positioning, but the positioning accuracy is not ideal, and there is still anisotropy in the propagation of formation velocity, that is, the propagation velocity of seismic waves from the same point will vary with the propagation direction.

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