CN111965696A - Dynamic disaster prediction method based on elastic wave multi-target analysis - Google Patents
Dynamic disaster prediction method based on elastic wave multi-target analysis Download PDFInfo
- Publication number
- CN111965696A CN111965696A CN202010828182.7A CN202010828182A CN111965696A CN 111965696 A CN111965696 A CN 111965696A CN 202010828182 A CN202010828182 A CN 202010828182A CN 111965696 A CN111965696 A CN 111965696A
- Authority
- CN
- China
- Prior art keywords
- elastic wave
- acoustic emission
- dynamic
- wave signals
- inversion imaging
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000004458 analytical method Methods 0.000 title claims abstract description 17
- 238000003384 imaging method Methods 0.000 claims abstract description 42
- 230000008859 change Effects 0.000 claims abstract description 35
- 230000002159 abnormal effect Effects 0.000 claims abstract description 24
- 238000012806 monitoring device Methods 0.000 claims abstract description 13
- 208000037516 chromosome inversion disease Diseases 0.000 claims description 42
- 238000012544 monitoring process Methods 0.000 claims description 7
- 239000003245 coal Substances 0.000 claims description 6
- 230000000644 propagated effect Effects 0.000 claims description 4
- 238000005422 blasting Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 2
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000013277 forecasting method Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The invention discloses a dynamic disaster prediction method based on elastic wave multi-target analysis, which comprises the following steps: s1, during operation, an acoustic emission monitoring device is used for collecting and sending elastic wave signals transmitted in a measured target body; s2, the ground comprehensive signal processing device receives and analyzes the elastic wave signals to obtain abnormal geologic bodies in the measured target body and dynamic inversion imaging of stress changes, and acoustic emission characteristic parameters of the elastic wave signals are extracted at the same time; and S3, the ground comprehensive signal processing device carries out disaster judgment and intelligent early warning according to the change condition of the abnormal geologic body dynamic inversion imaging or the change condition of the stress change dynamic inversion imaging or the change condition of the acoustic emission characteristic parameter in the measured target body. The dynamic disaster prediction method based on elastic wave multi-target analysis can comprehensively and accurately predict and early warn dynamic disasters, greatly improve prediction accuracy, greatly improve prediction work efficiency and reduce prediction cost.
Description
Technical Field
The invention relates to the field of disaster prediction, in particular to a dynamic disaster prediction method based on elastic wave multi-target analysis.
Background
Nowadays, mines gradually enter a deep mining stage, the mining environment of the mines is severe day by day, stress-dominated coal and rock dynamic disasters are increased day by day, the safe and efficient production of the coal mines is seriously affected, and the accurate prediction and early warning of the dynamic disasters are the key for preventing and controlling the dynamic disasters. The dynamic disaster occurs as a result of the combined action of multiple influence factors such as stress, geological conditions, coal body structure and the like, the currently widely used prediction technology and equipment mainly aim at processing single influence factors and obtain a better effect, but the multiple factors are mastered, multiple technologies and equipment are needed to be jointly detected and monitored for multiple times, so that the working efficiency and the reliability of prediction are seriously influenced, the comprehensive dynamic monitoring on multiple disaster-causing factors in the actual operation process is difficult, and the prediction investment cost is very high.
Disclosure of Invention
In view of the above, the invention aims to overcome the defects in the prior art, provide a dynamic disaster prediction method based on elastic wave multi-target analysis, and perform comprehensive and accurate prediction and early warning on the dynamic disaster, thereby greatly improving the prediction accuracy, greatly improving the prediction work efficiency and reducing the prediction cost.
The dynamic disaster prediction method based on elastic wave multi-target analysis comprises the following steps:
s1, during operation, an acoustic emission monitoring device is used for collecting and sending elastic wave signals transmitted in a measured target body;
s2, the ground comprehensive signal processing device receives and analyzes the elastic wave signals to obtain abnormal geologic bodies in the measured target body and dynamic inversion imaging of stress changes, and acoustic emission characteristic parameters of the elastic wave signals are extracted at the same time; the acoustic emission characteristic parameters include acoustic emission event number, ringing count, energy, amplitude, arrival time, and duration;
and S3, the ground comprehensive signal processing device carries out disaster judgment and intelligent early warning according to the change condition of the abnormal geologic body dynamic inversion imaging or the change condition of the stress change dynamic inversion imaging or the change condition of the acoustic emission characteristic parameter in the measured target body.
Further, the acoustic emission monitoring device comprises an acoustic emission monitoring device and an acoustic emission sensor; the acoustic emission sensor is used for collecting and transmitting elastic wave signals, and the acoustic emission monitoring equipment is used for receiving and sending the elastic wave signals collected by the acoustic emission sensor.
Further, the ground comprehensive signal processing device comprises a receiving unit, a calculating unit, a processing unit and a display unit; the receiving unit is used for receiving elastic wave signals; the computing unit carries out inversion imaging computation on the elastic wave signals and extracts acoustic emission characteristic parameters of the elastic wave signals; the processing unit analyzes the change condition of the inversion imaging and the change condition of the acoustic emission characteristic parameters to obtain a disaster early warning result; the display unit is used for displaying the disaster early warning result.
Further, before operation, acquiring and analyzing an elastic wave signal propagated in a measured target body under the condition of an active seismic source to obtain a primary inversion imaging of the abnormal geologic body; the active seismic sources comprise a blasting seismic source, a heading machine operation seismic source, a coal cutter operation seismic source and a bolting machine operation seismic source.
Further, in step S1, acquiring an elastic wave signal propagating in the measured object under the condition of an active seismic source and/or a passive seismic source; the passive seismic source is a seismic source generated in the measured target body.
Further, in step S2, analyzing the elastic wave signal to obtain a dynamic inversion image of the abnormal geologic body and the stress variation in the measured target body, specifically including:
s21, inverting the elastic wave signals by using an inversion imaging algorithm to obtain a once inversion imaging of the geologic body and the stress variation;
and S22, obtaining a new inversion imaging of the abnormal geologic body and the stress change in the measured target body by analogy according to the step S21 by taking the set dynamic update interval time T as a period.
The invention has the beneficial effects that: according to the dynamic disaster prediction method based on elastic wave multi-target analysis, comprehensive analysis is performed by comprehensively acquiring the evolution of the abnormal geologic body, the evolution of the stress state and the acoustic emission advanced appearance characteristic of the dynamic disaster in the operation process, so that comprehensive and accurate prediction and early warning of the dynamic disaster are realized, the prediction accuracy is greatly improved, the prediction work efficiency is greatly improved, and the prediction cost is reduced.
Drawings
The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings, in which:
the dynamic disaster prediction method based on elastic wave multi-target analysis comprises the following steps:
s1, during operation, an acoustic emission monitoring device is used for collecting and sending elastic wave signals transmitted in a measured target body;
s2, the ground comprehensive signal processing device receives and analyzes the elastic wave signals to obtain abnormal geologic bodies in the measured target body and dynamic inversion imaging of stress changes, and acoustic emission characteristic parameters of the elastic wave signals are extracted at the same time; the acoustic emission characteristic parameters comprise acoustic emission event number, ringing count, energy, amplitude, arrival time, duration and the like;
and S3, the ground comprehensive signal processing device carries out disaster judgment and intelligent early warning according to the change condition of the abnormal geologic body dynamic inversion imaging or the change condition of the stress change dynamic inversion imaging or the change condition of the acoustic emission characteristic parameter in the measured target body.
The disaster forecasting method can select only one of the three change conditions to carry out disaster early warning according to actual conditions and requirements, can also be combined and applied, has rich functions and flexible application selection, meets the requirements of automation, informatization and intellectualization, can obviously improve the disaster forecasting accuracy, makes up the defect of a single forecasting means, and greatly reduces the forecasting cost.
According to the dynamic disaster comprehensive identification and intelligent prediction method, elastic wave signals transmitted in a measured target body are collected and transmitted through the acoustic emission monitoring device, dynamic inversion imaging of abnormal geologic bodies and stress changes in the measured target body and extraction of acoustic emission characteristic parameters are carried out after synchronization of the ground comprehensive signal processing device, and then the change rules of the dynamic inversion imaging and the acoustic emission characteristic parameters are analyzed, so that dynamic disaster comprehensive identification and intelligent prediction based on elastic wave inversion imaging and real-time monitoring multi-target fusion are realized.
In this embodiment, the acoustic emission monitoring device includes an acoustic emission monitoring device and a matched acoustic emission sensor; the acoustic emission sensor is used for collecting and transmitting elastic wave signals, and the acoustic emission monitoring equipment is used for receiving and sending the elastic wave signals collected by the acoustic emission sensor. The acoustic emission sensor is fixedly arranged in a measured target body in a space hierarchical symmetry mode, and the measured target body is a monitored object in a monitoring range; the acoustic emission monitoring device is connected with the acoustic emission sensor through a shielding signal cable, and the acoustic emission monitoring device is arranged according to the actual environment of the measured target body.
In this embodiment, the ground integrated signal processing apparatus includes a receiving unit, a calculating unit, a processing unit, and a display unit. The receiving unit is used for receiving elastic wave signals sent by the acoustic emission monitoring equipment; the computing unit carries out inversion imaging computation based on the elastic wave signals to obtain dynamic inversion imaging of abnormal geologic bodies and stress changes in the measured target body, and acoustic emission characteristic parameters in the elastic wave signals are extracted; the processing unit analyzes the change condition of the dynamic inversion imaging of the abnormal geologic body, the change condition of the dynamic inversion imaging of the stress change and the change condition of the acoustic emission characteristic parameter to obtain a disaster early warning result; the display unit is used for displaying the disaster early warning result.
In this embodiment, before operation, elastic wave signals propagated in the measured target body may be collected and analyzed under the condition of an active seismic source to obtain a single inversion imaging of an abnormal geologic body in the measured target body, so as to preliminarily grasp a geologic occurrence condition in the measured target body. The inversion imaging adopts the existing high-precision imaging algorithm to carry out inversion. The active seismic sources comprise a blasting seismic source, a heading machine operation seismic source, a coal cutter operation seismic source and a bolting machine operation seismic source; the active seismic sources also include sources generated by other standard seismic source generators.
In this embodiment, in step S1, the acquisition of the elastic wave signal propagating in the measured object is realized under the condition of at least one of the active seismic source and the passive seismic source; the passive seismic source is a seismic source generated in the measured target body.
In this embodiment, in step S2, analyzing the elastic wave signal to obtain a dynamic inversion image of an abnormal geologic body and a stress change in the measured target body, specifically including:
s21, a computing unit of the acoustic emission processing equipment uses a built-in high-precision inversion imaging algorithm to invert the elastic wave digital signals to obtain abnormal geologic bodies in the measured target body and one-time inversion imaging of stress variation; the inversion imaging algorithm adopts the prior art, and is not described herein again;
s23, taking the set dynamic update interval time T as a period, obtaining new inversion imaging of the abnormal geologic body and the stress change in the measured target body by analogy according to the step S21, and further continuously updating the inversion imaging of the abnormal geologic body and the stress change, so that dynamic inversion imaging is realized; in this embodiment, the interval time T is set according to an actual working condition environment.
In this embodiment, in step S3, a multi-information coupling cooperative analysis is performed according to the change condition of the abnormal geologic body dynamic inversion imaging, the change condition of the stress change dynamic inversion imaging, and the change condition of the acoustic emission characteristic parameter in the measured target body, and an identification early warning result is issued in combination with a field actual given comprehensive identification criterion. The early warning result can be divided according to the severity to obtain different early warning levels, and the early warning levels can be divided into different levels according to actual field use, such as three levels of safety, general danger and severe danger.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (6)
1. A dynamic disaster prediction method based on elastic wave multi-target analysis is characterized by comprising the following steps: the method comprises the following steps:
s1, during operation, an acoustic emission monitoring device is used for collecting and sending elastic wave signals transmitted in a measured target body;
s2, the ground comprehensive signal processing device receives and analyzes the elastic wave signals to obtain abnormal geologic bodies in the measured target body and dynamic inversion imaging of stress changes, and acoustic emission characteristic parameters of the elastic wave signals are extracted at the same time; the acoustic emission characteristic parameters include acoustic emission event number, ringing count, energy, amplitude, arrival time, and duration;
and S3, the ground comprehensive signal processing device carries out disaster judgment and intelligent early warning according to the change condition of the abnormal geologic body dynamic inversion imaging or the change condition of the stress change dynamic inversion imaging or the change condition of the acoustic emission characteristic parameter in the measured target body.
2. The dynamic disaster prediction method based on elastic wave multi-target analysis according to claim 1, characterized in that: the acoustic emission monitoring device comprises an acoustic emission monitoring device and an acoustic emission sensor; the acoustic emission sensor is used for collecting and transmitting elastic wave signals, and the acoustic emission monitoring equipment is used for receiving and sending the elastic wave signals collected by the acoustic emission sensor.
3. The dynamic disaster prediction method based on elastic wave multi-target analysis according to claim 1, characterized in that: the ground comprehensive signal processing device comprises a receiving unit, a calculating unit, a processing unit and a display unit; the receiving unit is used for receiving elastic wave signals; the computing unit carries out inversion imaging computation on the elastic wave signals and extracts acoustic emission characteristic parameters of the elastic wave signals; the processing unit analyzes the change condition of the inversion imaging and the change condition of the acoustic emission characteristic parameters to obtain a disaster early warning result; the display unit is used for displaying the disaster early warning result.
4. The dynamic disaster prediction method based on elastic wave multi-target analysis according to claim 1, characterized in that: before operation, acquiring and analyzing an elastic wave signal propagated in a measured target body under the condition of an active seismic source to obtain a primary inversion imaging of an abnormal geologic body; the active seismic sources comprise a blasting seismic source, a heading machine operation seismic source, a coal cutter operation seismic source and a bolting machine operation seismic source.
5. The dynamic disaster prediction method based on elastic wave multi-target analysis according to claim 1, characterized in that: in step S1, acquiring an elastic wave signal propagated in the measured object under the condition of an active seismic source and/or a passive seismic source; the passive seismic source is a seismic source generated in the measured target body.
6. The dynamic disaster prediction method based on elastic wave multi-target analysis according to claim 1, characterized in that: in step S2, analyzing the elastic wave signal to obtain a dynamic inversion image of the abnormal geologic body and the stress variation in the target body, specifically including:
s21, inverting the elastic wave signals by using an inversion imaging algorithm to obtain a once inversion imaging of the geologic body and the stress variation;
and S22, obtaining a new inversion imaging of the abnormal geologic body and the stress change in the measured target body by analogy according to the step S21 by taking the set dynamic update interval time T as a period.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010828182.7A CN111965696A (en) | 2020-08-17 | 2020-08-17 | Dynamic disaster prediction method based on elastic wave multi-target analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010828182.7A CN111965696A (en) | 2020-08-17 | 2020-08-17 | Dynamic disaster prediction method based on elastic wave multi-target analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111965696A true CN111965696A (en) | 2020-11-20 |
Family
ID=73388273
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010828182.7A Pending CN111965696A (en) | 2020-08-17 | 2020-08-17 | Dynamic disaster prediction method based on elastic wave multi-target analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111965696A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120318500A1 (en) * | 2011-06-15 | 2012-12-20 | Esg Solutions Inc. | Methods and systems for monitoring and modeling hydraulic fracturing of a reservoir field |
| CN103883352A (en) * | 2014-04-08 | 2014-06-25 | 中煤科工集团重庆研究院有限公司 | Acoustic emission early-warning method for underground coal unstability dynamic disasters |
| CN104454008A (en) * | 2014-10-24 | 2015-03-25 | 苏州德鲁森自动化系统有限公司 | Mine disaster early warning method |
| CN104500139A (en) * | 2014-11-13 | 2015-04-08 | 四川大学 | Mine disaster prevention and control system based on acoustic emission technique and implementation method thereof |
| CN106194263A (en) * | 2016-08-29 | 2016-12-07 | 中煤科工集团重庆研究院有限公司 | Coal mine gas disaster monitoring early-warning system and method for early warning |
| CN109978413A (en) * | 2019-04-10 | 2019-07-05 | 中煤科工集团重庆研究院有限公司 | The appraisal procedure of derivative stress in coal bed state is migrated based on Gas feature |
| CN110925022A (en) * | 2019-12-12 | 2020-03-27 | 中煤科工集团重庆研究院有限公司 | Trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method |
| CN111206960A (en) * | 2020-01-15 | 2020-05-29 | 中煤科工集团重庆研究院有限公司 | Method for predicting coal rock dynamic disasters based on full time domain AE (acoustic emission) features |
-
2020
- 2020-08-17 CN CN202010828182.7A patent/CN111965696A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120318500A1 (en) * | 2011-06-15 | 2012-12-20 | Esg Solutions Inc. | Methods and systems for monitoring and modeling hydraulic fracturing of a reservoir field |
| CN103883352A (en) * | 2014-04-08 | 2014-06-25 | 中煤科工集团重庆研究院有限公司 | Acoustic emission early-warning method for underground coal unstability dynamic disasters |
| CN104454008A (en) * | 2014-10-24 | 2015-03-25 | 苏州德鲁森自动化系统有限公司 | Mine disaster early warning method |
| CN104500139A (en) * | 2014-11-13 | 2015-04-08 | 四川大学 | Mine disaster prevention and control system based on acoustic emission technique and implementation method thereof |
| CN106194263A (en) * | 2016-08-29 | 2016-12-07 | 中煤科工集团重庆研究院有限公司 | Coal mine gas disaster monitoring early-warning system and method for early warning |
| CN109978413A (en) * | 2019-04-10 | 2019-07-05 | 中煤科工集团重庆研究院有限公司 | The appraisal procedure of derivative stress in coal bed state is migrated based on Gas feature |
| CN110925022A (en) * | 2019-12-12 | 2020-03-27 | 中煤科工集团重庆研究院有限公司 | Trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method |
| CN111206960A (en) * | 2020-01-15 | 2020-05-29 | 中煤科工集团重庆研究院有限公司 | Method for predicting coal rock dynamic disasters based on full time domain AE (acoustic emission) features |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2017375855B2 (en) | Acousto-electric and gas real-time automatic monitoring system and method for coal-rock dynamic disaster | |
| CN110118991B (en) | Mining induced stress assessment method based on microseismic damage reconstruction | |
| CN106437854B (en) | Distributed coal rock dynamic disaster acoustic-electric synchronous monitoring system and method | |
| CN111879858B (en) | Rock damage prediction method based on acoustic emission seismic source dominant frequency uniqueness | |
| CN108802825B (en) | Method and system for positioning dynamic disasters of infrasonic wave monitoring coal rock | |
| CN204462405U (en) | A kind of rock burst omen early warning system based on acoustic emission | |
| US20200033496A1 (en) | Monitoring and forewarning method for coal-rock dynamic disasters based on electromagnetic radiation and earth sound | |
| CN111208555B (en) | Active and passive detection and positioning method for underground coal fire danger sound waves | |
| CN110568324B (en) | GIS partial discharge online monitoring system and method | |
| CN105158813B (en) | Rock blast hazard omen based on infrared radiation temperature knows method for distinguishing | |
| CN110824550B (en) | Tunnel bad geologic body microseismic advanced detection system and method | |
| CN110307038B (en) | Full-digital comprehensive monitoring and early warning system and method for mine power disasters | |
| CN106370306B (en) | Method for identifying rock burst disaster through infrared thermal image early warning | |
| CN103278843B (en) | Rock burst real-time prediction technique device in rock tunnel work progress | |
| CN105067101A (en) | Fundamental tone frequency characteristic extraction method based on vibration signal for vibration source identification | |
| CN112180429B (en) | Unfavorable geological structure detection system and method by utilizing tunnel blasting vibration inversion | |
| CN109239775B (en) | Method for tracking and positioning stolen mining of mineral resources | |
| CN113639849B (en) | Tunnel surrounding rock block collapse monitoring method and system based on natural vibration frequency | |
| CN111965696A (en) | Dynamic disaster prediction method based on elastic wave multi-target analysis | |
| CN104457851A (en) | Civil engineering structure healthy state remote monitoring system | |
| CN111520193A (en) | Non-contact tunnel engineering construction rock burst real-time forecasting method | |
| CN113703038B (en) | Automatic microseismic signal acquisition and identification method and system | |
| CN115950947A (en) | Real-time geological parameter prediction method of TBM tunnel based on vibration signal | |
| CN112857798B (en) | Multi-shafting mechanical transmission system quality evaluation method and device based on spectrum analysis | |
| CN111350545A (en) | Mine dynamic disaster system and method based on multi-dimensional monitoring |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |