Onset time automatic picking method of microseismic signal on the basis of time-recursive neural network

Abstract

The invention discloses an onset time automatic picking method of a microseismic signal on the basis of a time-recursive neural network. Each microseismic record is sampled according to a uniform and fixed dimension; then, the onset time behaviors of parts of record are manually picked to serve as the label information of a corresponding record; the record of picked information and the label of the record are used as a total dataset during network construction, wherein the total dataset is divided into three parts: a training dataset, a verification dataset and a test dataset; the data is input into a deep belief neural network to be trained and tested, and the time-recursive neural network is constructed; and the data which is not subjected to onset time picking is input into a trained network model, and the network outputs the data as a sequence corresponding to input data, wherein a first point which is not zero in the sequence is the onset time point of microseismic data.

Description

technical field [0001] The invention belongs to the technical field of geophysical detection, and relates to a time-recursive neural network-based method for automatically picking up microseismic signals when they arrive. Background technique [0002] With the continuous development of electronic technology and computer technology, automatic real-time detection and positioning of microseismic events have been successfully applied in various engineering applications, such as hydraulic fracturing to exploit oil and shale gas, mine dynamic disaster monitoring, and deep rock mass excavation Unloading disturbance warning forecast, etc. [0003] The timely automatic picking of microseismic signals generated by rock fracture events is a prerequisite to ensure the accuracy and efficiency of positioning results. Whether the picking results are accurate or not directly affects the accuracy of the final results, and its processing speed directly affects the overall work. efficiency. ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that when the microseismic signal is picked up, the traditional short-long time window energy ratio method needs certain human intervention during the picking process, which leads to the unstable performance of the picking method, and the traditional neural network can identify in a large amount of monitoring data. Difficult training of network models during microseismic events

Images