Failure evolution and disaster prediction of rock under uniaxial compression based on non-extensive statistical analysis of electric potential

Rock failure can cause serious geological disasters,and the non-extensive statistical features of electric potential(EP)are expected to provide valuable information for disaster prediction.In this paper,the uniaxial compression experiments with EP monitoring were carried out on fine sandstone,marble and granite samples under four displacement rates.The Tsallis entropy q value of EPs is used to analyze the selforganization evolution of rock failure.Then the influence of displacement rate and rock type on q value are explored by mineral structure and fracture modes.A self-organized critical prediction method with q value is proposed.The results show that the probability density function(PDF)of EPs follows the q-Gaussian distribution.The displacement rate is positively correlated with q value.With the displacement rate increasing,the fracture mode changes,the damage degree intensifies,and the microcrack network becomes denser.The influence of rock type on q value is related to the burst intensity of energy release and the crack fracture mode.The q value of EPs can be used as an effective prediction index for rock failure like b value of acoustic emission(AE).The results provide useful reference and method for the monitoring and early warning of geological disasters.

Predicting microseismic,acoustic emission and electromagnetic radiation data using neural networks

Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.

Pressure stimulated current in progressive failure process of combined coal-rock under uniaxial compression:Response and mechanism

Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in underground engineering.To reveal the effect of this way,the uniaxial compression experiments with PSC monitoring were conducted on three types of coal-rock combination samples with different strength combinations.The mechanism explanation of PSCs are investigated by resistivity test,atomic force microscopy(AFM)and computed tomography(CT)methods,and a PSC flow model based on progressive failure process is proposed.The influence of strength combinations on PSCs in the progressive failure process are emphasized.The results show the PSC responses between rock part,coal part and the two components are different,which are affected by multi-scale fracture characteristics and electrical properties.As the rock strength decreases,the progressive failure process changes obviously with the influence range of interface constraint effect decreasing,resulting in the different responses of PSC strength and direction in different parts to fracture behaviors.The PSC flow model is initially validated by the relationship between the accumulated charges of different parts.The results are expected to provide a new reference and method for mining design and roadway quality assessment.

Building a Large-Scale and Wide-Area Quantum Internet Based on an OSI-Alike Model

The theory and experiment of quantum information have been studied extensively in recent years,and the feasibility of quantum communication has been proved.Although the fundamental technology is not yet mature,research on quantum internet should be conducted.To implement quantum internet,an architecture that describes how quantum nodes are linked to form networks and how protocol functions are vertically composed need to be developed urgently.In this paper,we present a novel design of a clusterbased structure to describe how quantum nodes are interconnected,and how the structure can improve the performance of qubit transmission and reduce the network complexity.The idea of the quantum local area network(QLAN)is proposed as an essential component of the quantum internet.Besides,each quantum repeater links to neighboring repeaters to form a core network,and multiple QLANs are connected through the core network.The core network can be grouped into different hierarchical quantum repeater networks according to needed service requirements.For the sake of interoperability and fast prototyping,we adopt the idea of OSI layering model of the current Internet in the design of quantum internet.Finally,we elaborate on the composition of quantum nodes and the realization of end-to-end communication.

Experimental study on the infrared precursor characteristics of gas-bearing coal failure under loading

The stress and gas pressure in deep coal seams are very high,and instability and failure rapidly and intensely occur.It is important to study the infrared precursor characteristics of gas-bearing coal instability and failure.In this paper,a self-developed stress-gas coupling failure infrared experimental system was used to analyse the infrared radiation temperature(IRT)and infrared thermal image precursor characteristics of gas-free coal and gas-bearing coal.The changes in the areas of the infrared temperature anomalous precursor regions and the effect of the gas on the infrared precursors were examined.The results show that high-temperature anomalous precursors arise mainly when the gas-free coal fails under loading,whereas the gas-bearing coal has high-temperature and low-temperature anomalous precursors.The area of the high-temperature anomalous precursor is approximately 30%–40%under gasbearing coal unstable failure,which is lower than the 60%–70%of the gas-free coal.The area of the low-temperature abnormal precursor is approximately 3%–6%,which is higher than the 1%–2%of the gas-free coal.With increasing gas pressure,the area of the high-temperature anomalous precursor gradually decreases,and the area of the low-temperature anomalous precursor gradually increases.The highand low-temperature anomalous precursors of gas-bearing coal are mainly caused by gas desorption,volume expansion,and thermal friction.The presence of gas inhibits the increase in IRT on the coal surface and increases the difficulty of infrared radiation(IR)monitoring and early warning for gas-bearing coal.

Investigation of multispectral SF6 stimulated Raman scattering laser

In this work, SF6 as a Raman-active medium is investigated to generate a multispectral Raman laser by the combination of cascade stimulated Raman scattering(SRS) and four wave mixing. The Raman frequency comb from the 10 th-order anti-Stokes to the 9 th-order Stokes was generated, and its spectral range covered377–846 nm. The photon conversion efficiency of 16.4% for the first Stokes was achieved, and the Raman gain coefficient at 1.5 MPa of SF6 under the 532 nm pump laser was calculated to be 0.83 cm/GW by the SRS threshold comparison with H2. Using helium as the carrier gas, the thermal effect of the SF6 Raman laser was improved dramatically under a repetition rate of 10 Hz.