US 12,422,580 B2
Physics-based and data-driven integrated method for rock burst hazard assessment
Wu Cai, Xuzhou (CN); Qiang Lu, Xuzhou (CN); Siyuan Gong, Xuzhou (CN); Anye Cao, Xuzhou (CN); Xuming Zhu, Xuzhou (CN); Xiang Ma, Xuzhou (CN); and Linming Dou, Xuzhou (CN)
Assigned to CHINA UNIVERSITY OF MINING AND TECHNOLOGY, Xuzhou (CN)
Filed by CHINA UNIVERSITY OF MINING AND TECHNOLOGY, Xuzhou (CN)
Filed on Sep. 6, 2022, as Appl. No. 17/903,683.
Claims priority of application No. 202111337335.9 (CN), filed on Nov. 12, 2021.
Prior Publication US 2023/0152479 A1, May 18, 2023
Int. Cl. G01V 1/30 (2006.01); G01V 1/28 (2006.01); G06F 30/20 (2020.01)
CPC G01V 1/30 (2013.01) [G01V 1/282 (2013.01); G01V 2210/60 (2013.01); G06F 30/20 (2020.01)] 10 Claims
OG exemplary drawing
 
1. A physics-based and data-driven integrated method for rock burst hazard assessment, comprising the following steps:
establishing a three-dimensional model of an assessment region as mining district at one or more processors;
determining an initial stress concentration coefficient by conducting grid discretization on the three-dimensional model of the assessment region according to a certain spacing, and assigning a value to each of grid nodes using a Weibull distribution function, wherein the grid nodes are in a completely homogeneous state by default (m=0), with the value of 1;
assessing a geological structure, layout of a roadway, and advancing of a working face for the assessment region, selecting, based on the assessing, physics-based models for the grid nodes to calculate stress concentration factor values of each grid node for selected physics-based models, and conducting superposition calculation on the stress concentration factor values of each grid node for the selected physics-based models to obtain a stress concentration coefficient of each grid node under the physics-based models;
preliminarily correcting, based on seismic wave CT detection data, the stress concentration coefficient of each grid node by using a seismic wave CT detection and its derived characterization stress model;
further correcting, based on microseismic data, the stress concentration coefficient preliminarily corrected of each grid node by using a microseismic damage reconstruction stress model, and finally assessing a degree of rock burst hazard according to a size of the stress concentration coefficient value further corrected; and
receiving, at the one or more processor, seismic wave CT detection data and microseismic data detected in real time regarding the assessment region for the preliminarily correcting and the further correcting, outputting corresponding size of stress concentration coefficient value further corrected and corresponding degree of rock burst hazard in real time, and transmitting a warning signal to a monitoring and early warning cloud platform for early warning in responding to the corresponding size of stress concentration coefficient value exceeding a predetermined threshold.