CN108051853A - A kind of epicentral distance Method of fast estimating based on separate unit station first arrival P ripples - Google Patents
A kind of epicentral distance Method of fast estimating based on separate unit station first arrival P ripples Download PDFInfo
- Publication number
- CN108051853A CN108051853A CN201711255117.4A CN201711255117A CN108051853A CN 108051853 A CN108051853 A CN 108051853A CN 201711255117 A CN201711255117 A CN 201711255117A CN 108051853 A CN108051853 A CN 108051853A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- mtr
- mtd
- log
- 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
- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000001133 acceleration Effects 0.000 claims description 16
- 238000006073 displacement reaction Methods 0.000 claims description 7
- 238000009825 accumulation Methods 0.000 claims description 3
- 230000033001 locomotion Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 abstract description 5
- 238000004458 analytical method Methods 0.000 abstract description 2
- 230000015572 biosynthetic process Effects 0.000 abstract description 2
- 238000003786 synthesis reaction Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 7
- 230000007812 deficiency Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/24—Recording seismic data
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)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a kind of epicentral distance Method of fast estimating based on separate unit station first arrival P ripples, by carrying out time and frequency domain analysis to the initial 1.5 seconds P ripples signal detected, obtain the epicentral distance estimation formula for the synthesis many kinds of parameters being made of Parameters in Time Domain Amplitude and frequency domain cycle parameter, record is moved using Historical Strong and differential evolution algorithm returns the coefficient of each parameter in calculating epicentral distance estimation formula, provides the final estimation formula of epicentral distance.The method of the present invention only needs the P wave numbers of separate unit station first arrival 1.5 seconds it is estimated that epicentral distance, has good accuracy, timeliness and ease for use, suitable for seismic monitoring and earthquake pre-warning.
Description
Technical field
The present invention relates to a kind of epicentral distance Method of fast estimating based on separate unit station first arrival P ripples, are mainly used for using initial
P-wave data estimation epicentral distance.
Background technology
Earthquake disaster can cause casualties, damage artificial structure's object and can trigger fire, flood, mud-rock flow, tsunami,
The secondary disasters such as nuclear leakage seriously endanger socio-economic development.Although earthquake prediction is still global problem so far, earthquake
New technology of the early warning as earthquake hazard prevention draws attention and is applied in many earthquake-prone countries and area.Earthquake pre-warning
Technology refers to after the earthquake, before destructive seismic wave reaches fortified defence area, quickly determines seismologic parameter (earthquake magnitude, shake
Middle-range, depth of focus etc.), a kind of technology of alarm is sent to fortified defence area.Earthquake pre-warning technology is divided into more station earthquake pre-warnings
With separate unit station earthquake pre-warning, more station earthquake pre-warnings are definitely to shake parameter using multiple monitoring stations, and data available is more, accurately
Property is higher, but poor in timeliness；Separate unit station earthquake pre-warning is definitely to shake parameter using the single monitoring station, and data available is few, accurate
True property is poor, but timeliness is good.Single seismic station early warning technology can provide longer pre-warning time for fortified defence area, therefore how
The accuracy of separate unit station earthquake pre-warning is improved, is an important research content of earthquake pre-warning technology.In separate unit station earthquake pre-warning
In, the accuracy of epicentral distance determines the precision of earthquakes location, the estimation of epicentral distance generally use B- Δs method (B for fitting parameter,
Δ is epicentral distance), but the deficiency of this method is mainly shown as：1) the epicentral distance experience established using single parameter log (B) is public
Formula, excessively unilateral, accuracy is low；2) the P wave numbers using first arrival 3 seconds or more is needed still to be difficult in timeliness according to being calculated
Meet real-time earthquake early-warning system demand；3) need to be fitted P wave envelopes calculating B parameter, algorithm complexity is high, meter
It is low to calculate efficiency, and is difficult to be realized with program.Therefore, a kind of accurate, efficient, easy-to-use separate unit station epicentral distance quickly side of estimation is developed
Method becomes the problem of a urgent need to resolve of separate unit station earthquake pre-warning.
The content of the invention
For the foregoing present situation and deficiency of epicentral distance evaluation method, the present invention provides one kind to be based on separate unit station first arrival P ripples
Epicentral distance Method of fast estimating, by carrying out time and frequency domain analysis to initial 1.5 seconds P ripples signal for detecting, obtain by
The epicentral distance regression formula of the synthesis many kinds of parameters of Parameters in Time Domain Amplitude and frequency domain cycle parameter composition, note is moved using Historical Strong
Record and differential evolution algorithm calculate the coefficient of each parameter in epicentral distance regression formula, provide the final estimation formula of epicentral distance.This
Inventive method only needs the P wave numbers of separate unit station first arrival 1.5 seconds it is estimated that epicentral distance, has good accuracy, timeliness and easy-to-use
Property, suitable for seismic monitoring and earthquake pre-warning.
Present invention technical solution used for the above purpose is：
1) time domain parameter of initial 1.5 seconds P wave number evidences is calculated：The resultant acceleration of three directional acceleration data is obtained
The maximum PGA of (quadratic sum opens radical sign)；The maximum ZPGA of vertical acceleration is obtained；Vertical acceleration information is integrated
Speed data and displacement data are obtained, speed maximum ZPGV and displacement maximum ZPGD is obtained；To vertical acceleration information
Absolute value is integrated to obtain accumulation absolute velocity data, and maximum CAV is obtained.
2) frequency domain parameter of initial 1.5 seconds P wave number evidences is calculated：Maximum predominant period TPmax is calculated by formula (1)；By public affairs
Formula (2) calculates average predominant period Tc.
In formula (1) and formula (2), v is speed data, and u is displacement data, and the t data time-histories times, i is discrete data
Point sequence number, N discrete data points sum, T data total times are long.
1) and 2) 3) parameter is sought by all, establishes epicentral distance estimation formula (3).
In formula (3), Epd is epicentral distance to be evaluated, and a1, a2, a3, a4, a5, a6, a7, a8 are to treat regression coefficient.
4) Japan NIED strong motions record (K-NET, Kik-net are utilized：Epicentral distance 20km~200km, earthquake magnitude are more than 3.5
Grade, the depth of focus are less than 100km, and P ripples then explicitly record) initial 1.5 seconds P wave numbers evidences and differential evolution algorithm to formula
(3) Coefficient Fitting is carried out, obtains final available epicentral distance estimation formula (4)
The advantages of the method for the present invention：
1) the method for the present invention integrates a variety of time domains, frequency domain parameter establishes epicentral distance estimation empirical equation, has taken into account various aspects
Information, and using the high quality strong motion data and differential evolution algorithm regression coefficient of Japan NIED, greatly improve epicentral distance and estimate
The accuracy of calculation；
2) the method for the present invention makes epicentral distance evaluation time be reduced to 1.5 seconds by 3 seconds, meets the seismic data of grade of real-time or second
The requirement of processing system, timeliness are good；
3) the method for the present invention principle is simple, is easily programmed realization, computational efficiency is high, can widely apply seismic monitoring and ground
In the various software and hardware systems for shaking early warning, ease for use is good.
Description of the drawings
Fig. 1 is a kind of epicentral distance Method of fast estimating based on separate unit station first arrival P ripples.
Specific embodiment
Embodiment：
With reference to embodiment, the present invention will be further described, but the present invention is not limited to following embodiments.
The specific implementation flow of epicentral distance Method of fast estimating provided by the present invention based on separate unit station first arrival P ripples is such as
Under：
1) three-component (east-west direction, North and South direction and vertical direction) acceleration information acquisition instrument detects P ripple signals
Afterwards, 1.5 seconds P wave number evidences are buffered, Baseline Survey is carried out to 1.5 second datas.(Baseline Survey method is：P wave numbers evidence subtracts P within 1.5 seconds
Ripple then in former seconds noise data average value.)
2) time domain parameter is calculated：Calculate the maximum of the resultant acceleration (quadratic sum opens radical sign) of three directional acceleration data
Value PGA；Calculate the maximum ZPGA of vertical acceleration；Integration is carried out to vertical acceleration information, speed data is obtained, and be obtained
Maximum ZPGV；Speed data is integrated again, displacement data is obtained, and maximum ZPGD is obtained；To vertical acceleration information
Absolute value is integrated to obtain accumulation absolute velocity data, and maximum CAV is obtained.
3) frequency domain parameter is calculated：Maximum predominant period TPmax is calculated by formula (1)；Average remarkable week is calculated by formula (2)
Phase Tc.
4) epicentral distance is calculated：Epicentral distance is calculated by formula (4).
Claims (1)
1. a kind of seismic events method for quickly identifying based on initial vibration signal, it is characterised in that concretely comprise the following steps：
1) time domain parameter of initial 1.5 seconds P wave number evidences is calculated：Be obtained three directional acceleration data resultant acceleration (square
With open radical sign) maximum PGA；The maximum ZPGA of vertical acceleration is obtained；Vertical acceleration information is integrated to obtain
Speed maximum ZPGV and displacement maximum ZPGD is obtained in speed data and displacement data；To the absolute of vertical acceleration information
Value is integrated to obtain accumulation absolute velocity data, and maximum CAV is obtained.
2) frequency domain parameter of initial 1.5 seconds P wave number evidences is calculated：Maximum predominant period TPmax is calculated by formula (1)；By formula (2)
Calculate average predominant period Tc.
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>T</mi>
<mi>P</mi>
<mi> </mi>
<mi>max</mi>
<mo>=</mo>
<mi>max</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>TP</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>TP</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mn>2</mn>
<mi>&pi;</mi>
<msqrt>
<mfrac>
<msub>
<mi>X</mi>
<mi>i</mi>
</msub>
<msub>
<mi>D</mi>
<mi>i</mi>
</msub>
</mfrac>
</msqrt>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>X</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mn>0.98</mn>
<msub>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>+</mo>
<msubsup>
<mi>v</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>D</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mn>0.98</mn>
<msub>
<mi>D</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>+</mo>
<msubsup>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<mi>d</mi>
<mi>v</mi>
</mrow>
<mrow>
<mi>d</mi>
<mi>t</mi>
</mrow>
</mfrac>
<mo>)</mo>
</mrow>
<mi>i</mi>
<mn>2</mn>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>,</mo>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1...</mn>
<mi>N</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mi>T</mi>
<mi>c</mi>
<mo>=</mo>
<mn>2</mn>
<mi>&pi;</mi>
<msqrt>
<mfrac>
<mrow>
<msubsup>
<mo>&Integral;</mo>
<mn>0</mn>
<mi>T</mi>
</msubsup>
<msup>
<mi>u</mi>
<mn>2</mn>
</msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>t</mi>
</mrow>
<mrow>
<msubsup>
<mo>&Integral;</mo>
<mn>0</mn>
<mi>T</mi>
</msubsup>
<msup>
<mi>v</mi>
<mn>2</mn>
</msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>t</mi>
</mrow>
</mfrac>
</msqrt>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (1) and formula (2), v is speed data, and u is displacement data, and the t data time-histories times, i is discrete data point sequence
Number, N discrete data points are total, and T data total times are long.
1) and 2) 3) parameter is sought by all, establishes epicentral distance estimation formula (3).
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>E</mi>
<mi>p</mi>
<mi>d</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>a</mi>
<mn>1</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>P</mi>
<mi>G</mi>
<mi>A</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>a</mi>
<mn>2</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>Z</mi>
<mi>P</mi>
<mi>G</mi>
<mi>A</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>a</mi>
<mn>3</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>Z</mi>
<mi>P</mi>
<mi>G</mi>
<mi>V</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<mi>a</mi>
<mn>4</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>Z</mi>
<mi>P</mi>
<mi>G</mi>
<mi>D</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>a</mi>
<mn>5</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>C</mi>
<mi>A</mi>
<mi>V</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<mi>a</mi>
<mn>6</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>T</mi>
<mi>P</mi>
<mi> </mi>
<mi>max</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>a</mi>
<mn>7</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>T</mi>
<mi>c</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<mi>a</mi>
<mn>8</mn>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (3), Epd is epicentral distance to be evaluated, and a1, a2, a3, a4, a5, a6, a7, a8 are band regression coefficients.
4) Japan NIED strong motions record (K-NET, Kik-net are utilized：Epicentral distance 20km~200km, earthquake magnitude are more than 3.5 grades, shake
Depth is less than 100km, and P ripples then explicitly record) initial 1.5 seconds P wave numbers evidences and differential evolution algorithm to formula (3) into
Row coefficient is fitted, and obtains final available epicentral distance estimation empirical equation (4)
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>E</mi>
<mi>p</mi>
<mi>d</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>0.1423</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>P</mi>
<mi>G</mi>
<mi>A</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mn>0.2126</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>Z</mi>
<mi>P</mi>
<mi>G</mi>
<mi>A</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>0.0355</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>Z</mi>
<mi>P</mi>
<mi>G</mi>
<mi>V</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<mn>0.0990</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>Z</mi>
<mi>P</mi>
<mi>G</mi>
<mi>D</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mn>0.0587</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>C</mi>
<mi>A</mi>
<mi>V</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<mn>0.1312</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>T</mi>
<mi>P</mi>
<mi> </mi>
<mi>max</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>0.0390</mn>
<mo>*</mo>
<msub>
<mi>log</mi>
<mn>10</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>T</mi>
<mi>c</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<mn>1.6494</mn>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711255117.4A CN108051853B (en) | 2017-12-02 | 2017-12-02 | A kind of epicentral distance Method of fast estimating based on separate unit station first arrival P wave |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711255117.4A CN108051853B (en) | 2017-12-02 | 2017-12-02 | A kind of epicentral distance Method of fast estimating based on separate unit station first arrival P wave |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108051853A true CN108051853A (en) | 2018-05-18 |
| CN108051853B CN108051853B (en) | 2019-08-09 |
Family
ID=62121315
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711255117.4A Active CN108051853B (en) | 2017-12-02 | 2017-12-02 | A kind of epicentral distance Method of fast estimating based on separate unit station first arrival P wave |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108051853B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114371504A (en) * | 2022-01-11 | 2022-04-19 | 西南交通大学 | Earthquake epicenter position determination method, device, equipment and readable storage medium |
| CN115047516A (en) * | 2022-05-09 | 2022-09-13 | 天津市地震局 | Python-based long-period spectral intensity calculation method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006038685A (en) * | 2004-07-28 | 2006-02-09 | Mitsutoyo Corp | Earthquake specification information output device, earthquake damage information annunciator, and earthquake damage evasion system |
| CN104914468A (en) * | 2015-06-09 | 2015-09-16 | 中南大学 | Mine micro-quake signal P wave first arrival moment joint pickup method |
| CN105372706A (en) * | 2015-12-08 | 2016-03-02 | 哈尔滨工业大学 | Seismic oscillation amplitude modulation index and amplitude modulation coefficient evaluation method |
| CN106291662A (en) * | 2016-09-09 | 2017-01-04 | 吉林大学 | Fracturing causes early warning and the Forecasting Methodology that microseism occurs in hot dry rock |
-
2017
- 2017-12-02 CN CN201711255117.4A patent/CN108051853B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006038685A (en) * | 2004-07-28 | 2006-02-09 | Mitsutoyo Corp | Earthquake specification information output device, earthquake damage information annunciator, and earthquake damage evasion system |
| CN104914468A (en) * | 2015-06-09 | 2015-09-16 | 中南大学 | Mine micro-quake signal P wave first arrival moment joint pickup method |
| CN105372706A (en) * | 2015-12-08 | 2016-03-02 | 哈尔滨工业大学 | Seismic oscillation amplitude modulation index and amplitude modulation coefficient evaluation method |
| CN106291662A (en) * | 2016-09-09 | 2017-01-04 | 吉林大学 | Fracturing causes early warning and the Forecasting Methodology that microseism occurs in hot dry rock |
Non-Patent Citations (3)
| Title |
|---|
| OKAMOTO,ET AL: "Investigation on relationship between epicentral distance and growth curve of initial P-wave propagating in local heterogeneous media for earthquake early warning system", 《EARTH, PLANETS AND SPACE》 * |
| 罗光财，等: "PGV/PGA和PGD/PGA随震级和震中距变化的研究", 《西北地震学报》 * |
| 邢帆，等: "近断层地震时－频谱特性小波变换分析", 《福州大学学报(自然科学版)》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114371504A (en) * | 2022-01-11 | 2022-04-19 | 西南交通大学 | Earthquake epicenter position determination method, device, equipment and readable storage medium |
| CN114371504B (en) * | 2022-01-11 | 2022-09-02 | 西南交通大学 | Earthquake epicenter position determination method, device, equipment and readable storage medium |
| CN115047516A (en) * | 2022-05-09 | 2022-09-13 | 天津市地震局 | Python-based long-period spectral intensity calculation method |
| CN115047516B (en) * | 2022-05-09 | 2024-02-02 | 天津市地震局 | Python-based long-period spectrum intensity calculation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108051853B (en) | 2019-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108089225B (en) | A kind of earthquake magnitude Method of fast estimating based on separate unit station first arrival P wave | |
| CN102508278B (en) | Adaptive filtering method based on observation noise covariance matrix estimation | |
| González et al. | Edge wave and non-trapped modes of the 25 April 1992 Cape Mendocino tsunami | |
| Larson | GPS seismology | |
| Moschas et al. | PLL bandwidth and noise in 100 Hz GPS measurements | |
| Ristau | Implementation of routine regional moment tensor analysis in New Zealand | |
| CN105589100A (en) | Micro-seismic source location and velocity model simultaneous inversion method | |
| Elisseeff et al. | Acoustic tomography of a coastal front in Haro Strait, British Columbia | |
| CN104200813A (en) | Dynamic blind signal separation method based on real-time prediction and tracking on sound source direction | |
| Crowell et al. | Hypothetical real‐time GNSS modeling of the 2016 M w 7.8 Kaikōura earthquake: Perspectives from ground motion and tsunami inundation prediction | |
| Bennett et al. | Environmental noise mapping using measurements in transit | |
| CN102628955A (en) | Method for acquiring earthquake early warning magnitude | |
| CN101086534A (en) | Demodulator probe secondary localization method | |
| CN108051853B (en) | A kind of epicentral distance Method of fast estimating based on separate unit station first arrival P wave | |
| Work | Nearshore directional wave measurements by surface-following buoy and acoustic Doppler current profiler | |
| TW201819955A (en) | On-site earthquake early warning system and method thereof accommodating automatic site effect calibration | |
| Gallo et al. | Near real-time automatic moment magnitude estimation | |
| CN103823993A (en) | Correlation coefficient-based method for weakening CME (common mode error) influence in coordinate time sequence | |
| JP4509837B2 (en) | Early earthquake specifications estimation method and system | |
| Pinzón et al. | Seismic site classification of the Costa Rican Strong-Motion Network based on V S30 measurements and site fundamental period | |
| Chandler et al. | An attenuation model for distant earthquakes | |
| Moya et al. | Comparison of coseismic displacement obtained from GEONET and seismic networks | |
| Li et al. | Adaptive denoising approach for high-rate GNSS seismic waveform preservation: Application to the 2010 EI Mayor-Cucapah earthquake and 2012 Brawley seismic swarm | |
| Catchings et al. | Fine-scale delineation of the location of and relative ground shaking within the San Andreas Fault zone at San Andreas Lake, San Mateo County, California | |
| KR101914657B1 (en) | Method for extracting phase and amplitude information of seismic signal from seismic ambient noise |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20211116 Address after: 512029 building 6, Huangshaping Innovation Park, guanshaocheng phase I, Wujiang District, Shaoguan City, Guangdong Province Patentee after: China Earthquake Science Construction (Guangdong) disaster prevention and Reduction Research Institute Co., Ltd Address before: 541004 No. 12, Jiangan Road, Qixing District, Guilin City, Guangxi Zhuang Autonomous Region Patentee before: Guilin University of Technology |