An efficient intelligent sensing acquisition method for microseismic signal detection

Abstract

The invention discloses an efficient and intelligent acquisition method for microseismic signals, including compressed sensing sampling of microseismic data, extraction of effective microseismic events in compressed data, and reconstruction of compressed data of microseismic events. Specifically, all distributed acquisition nodes first perform compressive sensing on the microseismic data to obtain the measured value of the microseismic signal. Compressed sensing removes the redundant data of the microseismic signal in the time domain and avoids unnecessary subsequent computation overhead. Then, the measured values ​​of the microseismic signals are input into the pre-trained deep neural network to complete the identification of valid microseismic events. After identifying a valid event, the acquisition node transmits compressed sampling data containing microseismic events to the data center. Finally, the data center performs approximation optimization through singular value decomposition and clustering methods to continuously update the sparse base of seismic data, and uses l 1 The Norm Spectral Projected Gradient (SPGL1) algorithm reconstructs the original microseismic event data. To sum up, the new data collection method of the present invention can improve the data collection efficiency of the entire system from the aspects of data recording and data transmission.

Description

technical field [0001] The invention relates to the field of microseismic signal detection method design, in particular to an efficient intelligent perception acquisition method for microseismic signal detection. Background technique [0002] With the rapid development of artificial intelligence algorithms, the academic community has paid more and more attention to its application in the field of resource exploration. For example, in the "Global Engineering Frontiers" released by the Chinese Academy of Engineering in 2018, the intelligent and efficient exploration and exploitation of oil and gas resources is also listed as the frontier of engineering research, and it is pointed out that the intelligent collection, efficient transmission and intelligent analysis of data are future development. the trend of. Therefore, combining the relevant theories of artificial intelligence, it is of great significance to carry out research on acquisition methods to improve the utilization...

AI Technical Summary

Problems solved by technology

However, due to the diversity of seismic data features, most of the methods for applying compressive sensing to efficiently acquire seismic data in acquisition nodes use fixed transformation domains as the sparse domains of seismic data (such as Curvelet, Seislet, etc.) The intersection conditions are used as constraints for dictionary learning (such as principal component analysis, K-SVD, etc.), so it is difficult to achieve high-quality representations for different types of seismic data

Moreover, these methods do not fully consider the high-order combination of sparse domain features when reconstructing seismic data, and it is difficult to restore details in seismic data with high precision under the premise of using fewer feature components. Therefore, for high compression ratio seismic data The ability to refactor is not strong

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