Microearthquake focus positioning combined inversion method based on grid search and Newton iteration

Abstract

The invention discloses a microearthquake inversion algorithm which is a combined inversion positioning method based on grid search and Newton iteration. The algorithm is roughly divided into two parts, according to the first part, grid division is performed in the area where a microearthquake event happens, and the rough position where the microearthquake event happens is obtained through the grid search method; the second part is the Newton method, and the initial iteration value during Newton method inversion is the result of the grid search method in the first part. The defect that the Newton iteration method excessively depends on the initial iteration value is overcome, the number of iterations is reduced, and a target function can be well converged close to a theoretical true value finally.

Description

technical field [0001] The invention is a microseismic positioning and inversion method based on optimization theory. Background technique [0002] Microseisms are earthquakes whose magnitude is greater than one and less than three. Such small magnitude earthquakes are difficult for people to detect and can only be monitored with instruments. But even a small underground vibration will generate corresponding excitation to the underground medium, and such excitation may change the mechanical state of the underground medium. For example, microseisms directly or indirectly increase the probability of mine roof and floor rupture or gas outburst to a large extent. Such microseisms bring great potential safety hazards to people's production and life. [0003] Nowadays, with the improvement of microseismic monitoring technology, it has been widely used in the engineering field. In the coal mine system, people use the microseismic monitoring technology to judge the rupture of t...

AI Technical Summary

Problems solved by technology

However, these algorithms have some limitations, that is, there is a great dependence on the given initial value. If the selection of the initial value is within the range that the algorithm can bear, then a series of iterations will be performed. The value will eventually converge near the real value, but if the deviation between the initial value and the real value is too large, this will have a certain impact on the convergence process, and sometimes even determine whether the final result is correct

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