Microseismic first arrival acquisition method based on shear wave and Akaike information criterion

Abstract

The invention relates to a microseismic first arrival acquisition method based on shear wave and the Akaike information criterion, belonging to microseismic first arrival automatic extraction method research in an actual seismic exploration environment. For statistical characteristics showed by a micro seismic signal, background noise and the micro seismic signal are viewed as two stationary processes with different statistical properties, shear wave transform is used to decompose and reconstruct the micro seismic signal, at the same time, combined with an AIC model, each layer of reconstructed signal is subjected to first arrival pickup, and finally the pickup result of each layer is integrated to determine seismic two-dimensional first arrival pickup result. According to the method proposed in the invention, the preprocessing of complex background noise is not needed, time and calculation cost are saved, the energy loss of the micro seismic signal in the denoising process is avoided, the amplitude frequency characteristics of an effective signal in a micro seismic two-dimensional record is effectively retained, and an important basis is provided for oil and gas reservoir monitoring, geothermy dynamic monitoring, coal mining and dynamic engineering monitoring.

Description

technical field [0001] The invention relates to the research on the automatic extraction method of micro-seismic first arrivals under the actual seismic exploration environment. Background technique [0002] In the monitored microseismic records, in addition to weak microseismic signals, there is also a large amount of random background noise. Manually picking up effective microseismic events requires a lot of work. It is very difficult to pick up automatically, and the error is very large. At present, the microseismic first arrival picking methods mainly include long-short time-window ratio method, fractal-fractal-dimension method and autoregressive model method, etc., but these methods can handle records with high SNR, but it is extremely difficult to deal with records with low SNR Accurately pick up. [0003] In the actual microseismic records, the composition and properties of the background noise are very complex, which almost completely submerge the microseismic sign...

AI Technical Summary

Problems solved by technology

[0003] In actual microseismic records, the composition and properties of the background noise are very complex, which almost completely submerges the microseismic signals, making it extremely difficult to automatically pick up the first microseismic arrivals

A single preprocessing (such as: denoising) process cannot eliminate background noise with complex components, and may even cause loss of amplitude-frequency characteristics of microseismic records during processing

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