AU2020275806A1 - Mining-induced stress assessment method based on microseismic damage reconstruction - Google Patents
Mining-induced stress assessment method based on microseismic damage reconstruction Download PDFInfo
- Publication number
- AU2020275806A1 AU2020275806A1 AU2020275806A AU2020275806A AU2020275806A1 AU 2020275806 A1 AU2020275806 A1 AU 2020275806A1 AU 2020275806 A AU2020275806 A AU 2020275806A AU 2020275806 A AU2020275806 A AU 2020275806A AU 2020275806 A1 AU2020275806 A1 AU 2020275806A1
- Authority
- AU
- Australia
- Prior art keywords
- micro
- mining
- statistical
- seismic
- grid node
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005065 mining Methods 0.000 title claims abstract description 76
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000001186 cumulative effect Effects 0.000 claims abstract description 26
- 238000009826 distribution Methods 0.000 claims description 40
- 238000011156 evaluation Methods 0.000 claims description 26
- 239000003245 coal Substances 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 12
- 239000011435 rock Substances 0.000 claims description 12
- 238000009825 accumulation Methods 0.000 claims description 6
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 abstract description 10
- 238000005520 cutting process Methods 0.000 description 3
- 238000003325 tomography Methods 0.000 description 3
- 208000010392 Bone Fractures Diseases 0.000 description 2
- 206010017076 Fracture Diseases 0.000 description 2
- 238000005553 drilling Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000005670 electromagnetic radiation Effects 0.000 description 2
- PCTMTFRHKVHKIS-BMFZQQSSSA-N (1s,3r,4e,6e,8e,10e,12e,14e,16e,18s,19r,20r,21s,25r,27r,30r,31r,33s,35r,37s,38r)-3-[(2r,3s,4s,5s,6r)-4-amino-3,5-dihydroxy-6-methyloxan-2-yl]oxy-19,25,27,30,31,33,35,37-octahydroxy-18,20,21-trimethyl-23-oxo-22,39-dioxabicyclo[33.3.1]nonatriaconta-4,6,8,10 Chemical compound C1C=C2C[C@@H](OS(O)(=O)=O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2.O[C@H]1[C@@H](N)[C@H](O)[C@@H](C)O[C@H]1O[C@H]1/C=C/C=C/C=C/C=C/C=C/C=C/C=C/[C@H](C)[C@@H](O)[C@@H](C)[C@H](C)OC(=O)C[C@H](O)C[C@H](O)CC[C@@H](O)[C@H](O)C[C@H](O)C[C@](O)(C[C@H](O)[C@H]2C(O)=O)O[C@H]2C1 PCTMTFRHKVHKIS-BMFZQQSSSA-N 0.000 description 1
- 208000013201 Stress fracture Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
- G01L1/25—Measuring force or stress, in general using wave or particle radiation, e.g. X-rays, microwaves, neutrons
- G01L1/255—Measuring force or stress, in general using wave or particle radiation, e.g. X-rays, microwaves, neutrons using acoustic waves, or acoustic emission
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/288—Event detection in seismic signals, e.g. microseismics
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- General Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geophysics (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Health & Medical Sciences (AREA)
- Toxicology (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
- Geophysics And Detection Of Objects (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a mining-induced stress assessment method based on microseismic damage reconstruction, and is suitable for use in the field of microseismic monitoring for mine safety. The method specifically comprises: performing grid division on an assessment area, and calculating cumulative deformation energy ε
Description
MINING-INDUCED STRESS ASSESSMENT METHOD BASED ON MICROSEISMIC DAMAGE RECONSTRUCTION Description
Technical Field
[0001] The present invention relates to a mining stress evaluation method for mine, in particular to a mining stress evaluation method based on micro-seismic damage reconstruction applicable to the field of micro-seismic monitoring for mine safety.
Background Art
[0002] Mining stress is a secondary stress after the primary stress in the surrounding rock of a downhole mining space is redistributed under the impact of mining disturbance. Learning about the stress distribution characteristics is one of the indispensable bases and tasks in the mining production process, such as mining face design, stopping mining line design, coal pillar design, safety protection design and support design.
[0003] Owing to the fact that the disturbance of coal mining is a dynamic process, the mining stress also varies dynamically at all times. Consequently, it is difficult to extensively observe and quantitatively describe such a "black box" process. At present, the main methods used for mining stress observation include: borehole stress monitoring method, electromagnetic radiation method, drilling cuttings method, micro-seismic method, and seismic wave velocity tomography method. Among them, the borehole stress monitoring method is a direct observation method, but it is only limited to the observation of a small area in the shallow part of the roadway coal walls; both the electromagnetic radiation method and the drilling cuttings method are indirect evaluation methods, which are methods based on the indirect relationship between electromagnetic parameters, cuttings quantity and mining stress, and are unable to realize large-scale observation and are obviously affected by ambient noise; as a powerful and effective tool for monitoring the micro-fracture events of the coal and rock induced by mining disturbance, the micro-seismic method has been widely applied in the field of mine safety monitoring, and especially, the seismic wave velocity tomography technique extended from the micro-seismic method can realize large-scale detection, and the micro-seismic method can indirectly and qualitatively evaluate the scope of effect of mining stress on the basis of the spatial distribution of micro-seismic events and the frequency density distribution and energy density distribution of micro-seismic events. However, the seismic wave velocity tomography technique indirectly reckons the mining stress distribution according to the P-wave (longitudinal wave) velocity distribution, and inevitably leads to some errors in the calculation result owing to an unreasonable assumption that the P-wave velocity must be set as a constant during inversion because a certain number of micro-seismic events are required as the raw data for inversion in the calculation process; besides, it is difficult to realize real-time inversion with this technique because the technique usually involves high computation workload; although the evaluation method based on spatial distribution of micro-seismic events, frequency density distribution of micro-seismic events and energy density distribution can infer mining stress distribution to a certain extent by reflecting mining fracture distribution, it lacks obvious physical and mechanical correlation. Therefore, it is of great practical value and practical significance to reconstruct a method that has physical and mechanical significance and can realize approximately real-time and large-scale mining stress evaluation on the basis of micro seismic monitoring data.
Contents of the Invention
[0004] Object of the Invention: in view of the shortcomings of the above-mentioned techniques, the present invention provides a mining stress evaluation method based on micro seismic damage reconstruction, which calculates the distribution of mining stress synchronously according to the micro-seismic monitoring data obtained in real time, and realizes approximately real-time inversion of the mining stress in the coal mining process.
[0005] Technical Solution: in order to attain the above object, the mining stress evaluation method based on micro-seismic damage reconstruction in the present invention reconstructs a damage parameter of the stope according to micro-seismic parameters firstly; then obtains the stress distribution of the stope by associating the damage parameter based on damage mechanics, thereby obtaining the distribution of the mining stress field.
[0006] The method comprises the following steps: a. dividing the evaluation area into grids to form a grid map, wherein a statistical circle corresponding to each grid node is utilized as a local statistical window, and the cumulative deformation energy EEi and the number Ni of micro-seismic events or the loading duration Ati of the coal and rock in each statistical area are calculated as the values at each grid node by using an accumulation method; b. traversing a series of cumulative deformation energy EEi in the grid map to find the maximum value max{EEi}, and calculating the average cumulative deformation energy EF in the evaluation area;
c. calculating the damage parameter Di corresponding to each grid node in the grid map by using a formula Di = 1- exp(-E), wherein Di is the damage parameter value EF corresponding to the grid node i; d. calculating the mining stress value at each grid node: using a strain-time mode preferentially, when the mining speed at the mining face is approximately uniform and steady: S= E -a-Ati- (1- Di)
using a strain-micro-seismic frequency mode approximately, when the mining speed at the mining face is unsteady: S=E -aN -Ni-(1- Di)
wherein ag is the mining stress value corresponding the grid node i; E is the elasticity modulus; at is astrain-time coefficient; aN is a strain-micro-seismic frequency coefficient; next, performing interpolation of the mining stress values at the grid nodes to obtain the spatial distribution information of mining stress in the evaluation area; finally, obtaining a stress distribution map of an evaluated area by using the distribution information, so as to provide a guiding basis for mine safety design.
[0007] In the grid map, s represents the grid pitch and r represents the statistical radius of slip, and in order to avoid distortion of the result caused by missing some micro-seismic events in the statistical process of slip, the required relationship between the grid pitch and the statistical radius of slip is as follows: sl ir; the specific calculation process is as follows: using a statistical circle corresponding to each grid node as a local statistical window, calculating the cumulative deformation energy EEi and the number Ni of micro-seismic events or the loadingduration Ati of the coal and rock in each statistical area as the values at each grid node by using an accumulation method with the following calculation formula: j=Ni
EEi-ZE 1
j=1
At =tiN- til
wherein EEi represents the cumulative deformation energy in the statistical circle area corresponding to the grid node i; Ni represents the number of micro-seismic events in the statistical circle area corresponding to the grid node i; Eij represents the energy of the micro seismic events in the statistical circle area corresponding to the grid node i; Ati represents the loading duration in the statistical circle area corresponding to the grid node i; tiN represents the time when the last micro-seismic event occurs in the statistical circle area corresponding to the grid node i; and tii represents the time when the first micro-seismic event occurs in the statistical circle area corresponding to the grid node i.
[0008] The average cumulative deformation energy is calculated with a formula EF EF
(- , wherein max{EEi is the maximum cumulative deformation energy value in the evaluation area; and Dc is the damage parameter value corresponding to a completely damaged state, and is determined as 0.95 here.
[0009] Beneficial effects: the formulas for calculating mining stress involved in the present invention have obvious physical and mechanical significance, and the parameters involved in the formulas are calculated clearly and with high universality and operability, and the method is suitable for implementation by programming and has high application feasibility; the measured data involved in the calculation process adopts the micro-seismic data monitored in real time in large mining areas, which has high time-effectiveness, can realize approximately real-time inversion of the mining stress distribution in the coal mining process of in a large area, supports daily monitoring and early warning, and has high practical value and great practical significance.
Description of Drawings
[00010] Fig. 1 is a schematic view of mining stress distribution according to the mining stress evaluation method based on micro-seismic damage reconstruction in the present invention;
[00011] Fig. 2 is a schematic view of grid division according to the mining stress evaluation method based on micro-seismic damage reconstruction in the present invention;
[00012] Fig. 3 is a spatial distribution view of micro-seismic events;
[00013] Fig. 4 is a spatial distribution view of the cumulative deformation energy calculated on the basis of micro-seismic parameters;
[00014] Fig. 5 is a spatial distribution view of the loading duration of the coal and rock calculated on the basis of micro-seismic parameters;
[00015] Fig. 6 is a distribution view of the damage parameter calculated on the basis of micro seismic parameters;
[00016] Fig. 7 is a distribution view of mining stress based on micro-seismic damage reconstruction.
Embodiments
[00017] Hereunder the present invention will be further described with reference to the drawings.
[00018] With the mining of the underground coal seam, the mining stress distribution (ABCD) shown in Fig. 1 is formed in the coal and rock mass ahead of the mining face, including an elastic zone (AB), a plastic zone (BC) and a post-peak strain softened zone (CD), which respectively correspond to a curved subsidence zone, a fracture zone and a caving zone in the space of overlying strata in the vertical direction. Analyzed from the viewpoint of damage mechanics, all of the coal and rock materials in the area from the point D to the goaf zone each a completely damaged state, and the corresponding distribution density of the cumulative micro-seismic events reaches peak in this area;
[00019] The mining stress evaluation method based on micro-seismic damage reconstruction in the present invention comprises the following steps: reconstructs a damage parameter of the stope according to micro-seismic parameters firstly; then obtains the stress distribution of the stope by associating the damage parameter based on damage mechanics, thereby obtaining the distribution of the mining stress field;
[00020] Specifically, the steps are as follows: a. dividing the evaluation area into grids to form a grid map as shown in Fig. 2, in which s represents the grid pitch and r represents the statistical radius of slip, and in order to avoid distortion of the result caused by missing some micro-seismic events in the statistical process of slip, the required relationship between the grid pitch s and the statistical radius of slip r is as follows: s.] r ; the specific calculation process is as follows: using a statistical circle corresponding to each grid node as a local statistical window, calculating the cumulative deformation energy EEi and the number Ni of micro-seismic events or the loading duration Ati of the coal and rock in each statistical area as the values at each grid node by using an accumulation method, wherein EEi is used to calculate the value of the damage parameter (see step c), and Ni and Ati are respectively used to calculate the value of mining stress in a strain-micro-seismic frequency mode and a strain-time mode (see step d) with the following calculation formulas: j=Ni
EEi ZE j j=1
At =tiN- til
wherein EEi represents the cumulative deformation energy in the statistical circle area corresponding to the grid node i; Ni represents the number of micro-seismic events in the statistical circle area corresponding to the grid node i; Eij represents the energy of the micro-seismic events in the statistical circle area corresponding to the grid node i; Ati represents the loading duration in the statistical circle area corresponding to the grid node i; tiN represents the time when the last micro-seismic event occurs in the statistical circle area corresponding to the grid node i; and tii represents the time when the first micro seismic event occurs in the statistical circle area corresponding to the grid node i. b. traversing a series of cumulative deformation energy EEi in the grid map to find the maximum value max{EEi}, and calculating the average cumulative deformation energy EF in the evaluation area; wherein max{EEi} is the maximum cumulative deformation energy value in the evaluation area; and Dc is the damage parameter value corresponding to a completely damaged state, and is determined as 0.95 here.
c. calculating the damage parameter Di corresponding to each grid node in the grid map with the following formula: EEi Di = 1 - exp(- -) EF
wherein Di is the damage parameter value corresponding to the grid node i; d. calculating the mining stress value at each grid node: using a strain-time mode preferentially, when the mining speed at the mining face is approximately uniform and steady: -= E -a -At -(1- Dj)
using a strain-micro-seismic frequency mode approximately, when the mining speed at the mining face is unsteady: = E -aN -Ni-(1- Dj)
wherein og is the mining stress value corresponding the grid node i; E is the elasticity modulus; at is astrain-time coefficient; aN is a strain-micro-seismic frequency coefficient; finally, performing interpolation of the mining stress values at the grid nodes to obtain the spatial distribution of mining stress in the evaluation area.
[00021] Example 1: In this example, the micro-seismic monitoring data of the mining face in the stoping stage in a coal mine is analyzed. In view that the mining speed at the mining face is approximately uniform and steady, the average daily advance is 1.2m, the calculation in the present invention is explained here exemplarily in the strain-time mode. The present invention is implemented according to the idea of the present invention: (1) According to the spatial distribution of the micro-seismic events as shown in Fig. 3, the evaluation area is divided into three-dimensional grids, with the grid pitch s of 10m and the statistic radius r of slip of 30m; the cumulative deformation energy EEi and the loading duration At of the coal and rock at each grid node are calculated by using an accumulation method; then the spatial distribution maps of cumulative deformation energy as shown in Figs. 4 and 5 are obtained by using an interpolation calculation method. Fig. 5 is a spatial distribution map of the loading duration of the coal and rock calculated on the basis of micro-seismic parameters. (2) By traversing the grid node sequence EEi, the maximum cumulative deformation energy max{EEi} is determined to be 9,141.3678, and accordingly the average cumulative deformation energy EF is calculated to be 3,051.4635.
(3) EEi and EF are substituted into the formula Di = 1 - exp(- )to obtain the damage EF parameter value at each grid node, and then the damage parameter distribution is calculated by interpolation, as shown in Fig. 6.
(4) The actual elastic modulus E=9 GPa and the coefficient at=0.000026 are substituted into the formula og = E - at - Ati - (1 - Dj) to obtain the mining stress values at each grid node, and then the mining stress distribution is calculated by interpolation, as shown in Fig. 7.
[00022] The example proves that the method provided in the present invention calculates the related parameters clearly, with high universality and operability, provides a reasonable result of distribution of the mining stress through inversion calculation and attains a good effect, and can realize approximately real-time inversion of the mining stress in the coal seam mining process.
Claims (3)
- Claims 1. A mining stress evaluation method based on micro-seismic damage reconstruction, which reconstructs a damage parameter of the stope according to micro-seismic parameters firstly; then obtains the stress distribution of the stope by associating the damage parameter based on damage mechanics, thereby obtaining the distribution of the mining stress field; specifically, the method comprises the following steps: a. dividing the evaluation area into grids to form a grid map, a statistical circle corresponding to each grid node is utilized as a local statistical window, and the cumulative deformation energy EEi and the number Ni of micro-seismic events or the loading duration Ati of the coal and rock in each statistical area are calculated as the values at each grid node by using an accumulation method;b. traversing a series of cumulative deformation energy EEi in the grid map to find the maximum value max{EEi}, and calculating the average cumulative deformation energy EF in the evaluation area;c. calculating the damage parameter Di corresponding to each grid node in the grid map by using a formula Di = 1- exp(- ), wherein Di is the damage parameter value EF corresponding to the grid node i; d. calculating the mining stress value at each grid node: using a strain-time mode preferentially, when the mining speed at the mining face is approximately uniform and steady: -= E -a -At -(1- Dj)using a strain-micro-seismic frequency mode approximately, when the mining speed at the mining face is unsteady: = E -aN -Ni-(1- Dj)wherein og is the mining stress value corresponding to the grid node i; E istheelasticity modulus; at is a strain-time coefficient; aN is a strain-micro-seismic frequency coefficient; next, performing interpolation of the mining stress values at each grid node to obtain the spatial distribution information of mining stress in the evaluation area; finally, obtaining a stress distribution map of an evaluated area by using the distribution information, so as to provide a guiding basis for mine safety design.
- 2. The mining stress evaluation method based on micro-seismic damage reconstruction according to claim 1, wherein in the grid map, s represents the grid pitch and r represents the statistical radius of slip, and in order to avoid distortion of the result caused by missing some micro-seismic events in the statistical process of slip, the required relationship between the grid pitch s and the statistical radius r of slip is as follows: s.-Ir ; the specific calculation process is as follows: using a statistical circle corresponding to each grid node as a local statistical window, calculating the cumulative deformation energy EEi and the number Ni of micro-seismic events or the loading duration Ati of the coal and rock in each statistical area as the values at each grid node by using an accumulation method with the following calculation formula: j=NiEEi-ZE 1j=1A tiN -ilwherein EEi represents the cumulative deformation energy in the statistical circle area corresponding to the grid node i; Ni represents the number of micro-seismic events in the statistical circle area corresponding to the grid node i; Eij represents the energy of the micro-seismic events in the statistical circle area corresponding to the grid node i; Ati represents the loading duration in the statistical circle area corresponding to the grid node i; tiN represents the time when the last micro-seismic event occurs in the statistical circle area corresponding to the grid node i; and tii represents the time when the first micro seismic event occurs in the statistical circle area corresponding to the grid node i.
- 3. The mining stress evaluation method based on micro-seismic damage reconstruction according to claim 1, wherein the average cumulative deformation energy EF is calculated with a formula EF - _ , where max{EEi} is the maximum cumulative deformation energy value in the evaluation area; and Dc is the damage parameter value corresponding to a completely damaged state, and is determined as 0.95 here.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910404955.6 | 2019-05-16 | ||
| CN201910404955.6A CN110118991B (en) | 2019-05-16 | 2019-05-16 | Mining induced stress assessment method based on microseismic damage reconstruction |
| PCT/CN2020/088156 WO2020228546A1 (en) | 2019-05-16 | 2020-04-30 | Mining-induced stress assessment method based on microseismic damage reconstruction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| AU2020275806A1 true AU2020275806A1 (en) | 2021-02-04 |
| AU2020275806B2 AU2020275806B2 (en) | 2022-11-24 |
Family
ID=67522497
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2020275806A Active AU2020275806B2 (en) | 2019-05-16 | 2020-04-30 | Mining-induced stress assessment method based on microseismic damage reconstruction |
Country Status (3)
| Country | Link |
|---|---|
| CN (1) | CN110118991B (en) |
| AU (1) | AU2020275806B2 (en) |
| WO (1) | WO2020228546A1 (en) |
Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110118991B (en) * | 2019-05-16 | 2020-06-23 | 中国矿业大学 | Mining induced stress assessment method based on microseismic damage reconstruction |
| CN110244354A (en) * | 2019-07-11 | 2019-09-17 | 东北大学 | A kind of metal mine mining disturbance stress field quantifies dynamic playback method |
| CN112377257B (en) * | 2020-10-26 | 2021-08-20 | 中国矿业大学 | Working face mining advance influence range determining method based on microseismic monitoring |
| CN112324506B (en) * | 2020-11-20 | 2024-05-14 | 上海大屯能源股份有限公司江苏分公司 | Dynamic early warning method for preventing rock burst of coal mine based on microseism |
| CN113312742B (en) * | 2021-03-15 | 2023-10-10 | 中国再保险(集团)股份有限公司 | Annular space grid data structure and construction and retrieval method and device thereof |
| CN113482720A (en) * | 2021-07-02 | 2021-10-08 | 中煤第三建设（集团）有限责任公司 | Mechanized operation line construction process under rock burst condition |
| CN114219211B (en) * | 2021-11-12 | 2024-09-20 | 中国矿业大学 | Rock burst risk assessment method based on theory and data driving fusion |
| CN114353745A (en) * | 2021-11-25 | 2022-04-15 | 煤炭科学技术研究院有限公司 | Mining area risk assessment method and device and electronic equipment |
| CN114810211B (en) * | 2021-11-26 | 2023-03-10 | 中国矿业大学 | Rock burst danger prediction method based on mine seismic group shock wave energy attenuation characteristics |
| CN115014613B (en) * | 2022-06-28 | 2023-05-23 | 中国科学院武汉岩土力学研究所 | Monitoring method for surrounding rock stress and deformation of coal mine tunnel |
| CN114935513A (en) * | 2022-07-26 | 2022-08-23 | 四川中水成勘院工程物探检测有限公司 | Method for predicting generation and expansion of concrete dam body crack based on microseismic signal characteristics |
| CN115826037B (en) * | 2022-11-02 | 2023-12-12 | 北京国信安科技术有限公司 | Evaluation method for monitoring and early warning capability of microseism monitoring and early warning system in mine goaf |
| CN116540299B (en) * | 2023-07-05 | 2023-09-26 | 煤炭科学研究总院有限公司 | Early warning method based on microseismic energy accumulation tendency for coal mine scene |
| CN117266842B (en) * | 2023-09-12 | 2024-06-18 | 东北大学 | Ground stress testing method based on microseismic monitoring |
Family Cites Families (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7460436B2 (en) * | 2005-12-05 | 2008-12-02 | The Board Of Trustees Of The Leland Stanford Junior University | Apparatus and method for hydraulic fracture imaging by joint inversion of deformation and seismicity |
| US8902707B2 (en) * | 2007-04-09 | 2014-12-02 | Baker Hughes Incorporated | Analysis of uncertainty of hypocenter location using the combination of a VSP and a subsurface array |
| US8743115B1 (en) * | 2009-10-23 | 2014-06-03 | Paradigm Sciences Ltd. | Systems and methods for coordinated editing of seismic data in dual model |
| US9176245B2 (en) * | 2009-11-25 | 2015-11-03 | Halliburton Energy Services, Inc. | Refining information on subterranean fractures |
| US9389326B2 (en) * | 2011-03-23 | 2016-07-12 | Global Ambient Seismic, Inc. | Methods, systems and devices for near-well fracture monitoring using tomographic fracture imaging techniques |
| CN103306722B (en) * | 2013-06-21 | 2015-08-05 | 中国矿业大学 | Micro-seismic multi-dimensional information comprehensive region detection and evaluation method for impact danger region |
| CA2945467C (en) * | 2014-05-23 | 2019-12-17 | Halliburton Energy Services, Inc. | Enhancing reservoir characterization using real-time srv and fracture evolution parameters |
| CN104100297B (en) * | 2014-07-31 | 2017-01-25 | 煤炭科学技术研究院有限公司 | Self-vibrating type micro-quake monitoring system and self-vibrating type micro-quake monitoring method |
| CN104331533A (en) * | 2014-09-18 | 2015-02-04 | 山东科技大学 | Poor-sealing drill hole mining response value simulation method |
| CN105422170B (en) * | 2015-11-02 | 2018-10-23 | 安徽理工大学 | Middle deep goaf grouting reinforcing and processing method under a kind of building foundation |
| CN105607127B (en) * | 2016-01-27 | 2018-01-02 | 中国矿业大学 | A kind of microseism many reference amounts method for early warning of high stress concentration zone bump |
| CN105891874B (en) * | 2016-06-30 | 2017-12-12 | 大连理工大学 | One kind adopts coal and rock gushing water micro seismic monitoring method |
| CN106285782B (en) * | 2016-08-30 | 2018-11-13 | 中国矿业大学(北京) | A kind of bump method for early warning under complicated geological environmental effect and system |
| CN106646607B (en) * | 2016-12-22 | 2018-11-27 | 中国矿业大学 | A kind of adaptive unequal spacing Meshing Method improving CT resolution of inversion and efficiency |
| CN106873029B (en) * | 2017-01-19 | 2020-02-07 | 秦福亮 | Method for determining coal and gas outburst indexes and critical states thereof |
| CN109581492A (en) * | 2017-09-29 | 2019-04-05 | 中国石油化工股份有限公司 | Petrophysical parameter calculation method and system based on Simulating Seismic Wave |
| CN109063257B (en) * | 2018-07-02 | 2019-04-26 | 山东科技大学 | A kind of coal and rock subregion water filling seepage flow-damage-stress coupling method for numerical simulation |
| CN109447837B (en) * | 2018-11-15 | 2022-05-20 | 国家能源投资集团有限责任公司 | Danger assessment method for rock burst in mining area |
| CN109597124B (en) * | 2018-12-29 | 2021-06-04 | 煤炭科学技术研究院有限公司 | Fine detection method for channel wave earthquake in stress concentration area of working surface |
| CN110118991B (en) * | 2019-05-16 | 2020-06-23 | 中国矿业大学 | Mining induced stress assessment method based on microseismic damage reconstruction |
-
2019
- 2019-05-16 CN CN201910404955.6A patent/CN110118991B/en active Active
-
2020
- 2020-04-30 WO PCT/CN2020/088156 patent/WO2020228546A1/en active Application Filing
- 2020-04-30 AU AU2020275806A patent/AU2020275806B2/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110118991A (en) | 2019-08-13 |
| CN110118991B (en) | 2020-06-23 |
| AU2020275806B2 (en) | 2022-11-24 |
| WO2020228546A1 (en) | 2020-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2020275806B2 (en) | Mining-induced stress assessment method based on microseismic damage reconstruction | |
| Dai et al. | Microseismic early warning of surrounding rock mass deformation in the underground powerhouse of the Houziyan hydropower station, China | |
| Cheng et al. | A zoning model for coal mining-induced strata movement based on microseismic monitoring | |
| US11899154B2 (en) | DAS same-well monitoring real-time microseismic effective event identification method based on deep learning | |
| Wang et al. | Application of a combined supporting technology with U-shaped steel support and anchor-grouting to surrounding soft rock reinforcement in roadway | |
| CN103913774B (en) | Reservoir geology mechanics parameter inversion method based on micro-seismic event | |
| CN114219211B (en) | Rock burst risk assessment method based on theory and data driving fusion | |
| CN112731525B (en) | Intelligent prediction method for stability of surrounding rock of roadway based on synchronous monitoring of microseismic and electromagnetic radiation | |
| Leake et al. | Microseismic monitoring and analysis of induced seismicity source mechanisms in a retreating room and pillar coal mine in the Eastern United States | |
| CN103726851B (en) | Excavation method capable of lowering rockburst risk of deep tunnel in process of passing through fault area | |
| Xue et al. | Rockburst prediction and analysis of activity characteristics within surrounding rock based on microseismic monitoring and numerical simulation | |
| CN114412573B (en) | Mine wall breakage monitoring and early warning system and method | |
| Jiang et al. | A novel method for automatic identification of rock fracture signals in microseismic monitoring | |
| CN106248672A (en) | Rock crack mode of extension recognition methods and system in a kind of on-the-spot hole based on DIC technology | |
| CN111983035B (en) | Rock sample damage prediction method based on acoustic emission technology | |
| CN113390458A (en) | Method for judging damage degree of surrounding rock in blasting area | |
| CN112483174A (en) | Arrangement method of tunneling working face impact dangerous vibration wave CT inversion system | |
| CN111077583B (en) | Structure activation double-parameter monitoring system and monitoring method | |
| CN109505654A (en) | The anti-risk topmast maintaining method in tunnel under the influence of a kind of repeated mining | |
| Morrison et al. | Chaotic behaviour and mining-induced seismicity | |
| CN110259443A (en) | A kind of coal seam borehole wall stability prediction method based on 3DEC discrete element | |
| CN103775073A (en) | Mining working face ground stress distribution characteristic detection method | |
| CN104832160A (en) | Downhole drilling trajectory microseism description method | |
| Mooney et al. | Influence of geological conditions on measured TBM vibration frequency | |
| CN115950947A (en) | Real-time geological parameter prediction method of TBM tunnel based on vibration signal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGA | Letters patent sealed or granted (standard patent) |