CN111123356B - Abnormal track intelligent identification method based on first arrival information - Google Patents
Abnormal track intelligent identification method based on first arrival information Download PDFInfo
- Publication number
- CN111123356B CN111123356B CN201811282072.4A CN201811282072A CN111123356B CN 111123356 B CN111123356 B CN 111123356B CN 201811282072 A CN201811282072 A CN 201811282072A CN 111123356 B CN111123356 B CN 111123356B
- Authority
- CN
- China
- Prior art keywords
- time
- arrival
- time window
- abnormal
- dimensional
- 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.)
- Active
Links
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. analysis, for interpretation, for correction
- 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
- G01V2210/40—Transforming data representation
- G01V2210/41—Arrival times, e.g. of P or S wave or first break
-
- 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
- G01V2210/6161—Seismic or acoustic, e.g. land or sea measurements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention provides an abnormal track intelligent identification method based on first arrival information, which comprises the steps of firstly inputting an original acquisition single shot record, and converting a time domain one-dimensional signal into a time-frequency domain two-dimensional signal by utilizing a correction S-domain conversion method; secondly, carrying out first arrival picking by using a two-dimensional domain energy ratio method, outputting first arrival automatic picking time information, and carrying out curve fitting on the first arrival time by using a polynomial fitting method to obtain a polynomial fitting formula of the first arrival information; thirdly, calculating judgment parameters such as a first arrival distance, a first arrival distance variance, time window energy, time window dominant frequency, time window bandwidth, time window spectral density, time window signal-to-signal ratio and the like; and finally, according to the calculated identification parameters, the abnormal track meeting the identification conditions of the abnormal track is the abnormal track, and the pile number information is output, so that the identification of the abnormal track is rapidly and intelligently completed. The abnormal track intelligent identification method based on the first arrival information realizes the intelligent identification of the abnormal track quickly and efficiently, thereby greatly improving the working efficiency.
Description
Technical Field
The invention relates to the technical field of oil and gas field exploration and development, in particular to an abnormal track intelligent identification method based on first-arrival information.
Background
When the seismic records are acquired in the field, the abnormal tracks exist in the original seismic records due to the influences of complex near-surface, excitation and receiving factor differences, acquisition instrument stability and the like. The abnormal way has: the method has the characteristics of no take-off before first arrival, abnormal amplitude, abnormal dominant frequency, abnormal variance, abnormal zero-crossing number and the like. With the increasingly wide application of high-density seismic exploration, the amount of seismic data is rapidly increased, and the manual identification method cannot meet the requirement. Some abnormal trace identification methods are also available in the conventional data quality monitoring system, and an adjacent trace energy ratio method is usually adopted to identify the abnormal trace according to the energy difference between different seismic traces. Due to the fact that continuous amplitude or frequency is abnormal, the identification precision of the energy ratio method is reduced, a large amount of research work is conducted by domestic and foreign scholars, and abnormal tracks are identified according to comprehensive analysis results by calculating attribute information such as energy, dominant frequency and correlation coefficient. However, because the information in the one-dimensional time domain is relatively single, the attribute information of the abnormal track which can be reflected is limited, and the identification precision of the abnormal track is restricted. Therefore, a novel abnormal track intelligent identification method based on first arrival information is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide an abnormal road intelligent identification method based on first arrival information, which can quickly and intelligently finish the identification of an abnormal road according to the evaluation parameter assignment and the comprehensive consideration.
The object of the invention can be achieved by the following technical measures: the abnormal track intelligent identification method based on the first arrival information comprises the following steps: step 1: inputting an original acquisition single shot record, and converting a time domain one-dimensional signal into a time-frequency domain two-dimensional signal by using a modified S-domain conversion method; step 2: in a two-dimensional time-frequency domain, carrying out first arrival picking by using a two-dimensional domain energy ratio method, outputting first arrival automatic picking time information, and carrying out curve fitting on the first arrival time by using a polynomial fitting method to obtain a polynomial fitting formula of the first arrival information; and step 3: calculating the first-arrival distance between each first-arrival automatic picking time and the time calculated by a polynomial fitting formula, calculating the first-arrival distances of all the data, and solving a first-arrival distance average value and a first-arrival distance variance; and 4, step 4: in a two-dimensional time-frequency domain, defining an analysis time window range, and calculating judgment parameters of time window energy, time window dominant frequency, time window bandwidth, time window spectral density and time window signal-to-signal ratio; and 5: according to the calculated identification parameters, if the abnormal track identification conditions are not met, the abnormal track is not judged, if the abnormal track identification conditions are met, the abnormal track is judged, and the pile number information of the abnormal track is output, so that the identification of the abnormal track is rapidly and intelligently completed.
The object of the invention can also be achieved by the following technical measures:
in step 1, when an original acquisition single shot record is input, the information of the head of the shot point pile number, the demodulator probe pile number, the track number, the sampling interval, the number of sampling points and the total track number of the single shot record is input.
In step 1, the mathematical formula of the modified S-domain transformation method is:
in the formula: y (t) is an original single shot record, t is time and has a unit of ms, XS (tau, xi) is a time-frequency domain two-dimensional signal obtained by conversion, xi is frequency and has a unit of Hz, parameters sigma and beta are used for adjusting and correcting a resolution parameter of S domain conversion, the value range of sigma is [0.5 and 1.5], the value range of beta is [2.0 and 3.0], tau is a time position of a conversion time window and has a unit of ms.
In step 2, the first-arrival automatic picking time obtained by the two-dimensional domain energy ratio method is fpi (t), which is a one-dimensional array, that is, each data has an automatic picking first-arrival time, and polynomial fitting is performed on the first-arrival automatic picking time to obtain first-arrival information, wherein the polynomial fitting formula is y ═ ax2+ bx + c, wherein: y is fitting first-arrival time, x is automatic picking first-arrival time, a, b and c are parameters of a fitting formula, and the first-arrival time Sfp corresponding to each channel of data can be obtained through calculation according to the fitting formulaiAnd (t), t is time in ms, and i is the track number of the single shot record.
In step 3, the automatic picking time fp is determined according to the first arrival of each seismic datai(t) calculating the time Sfp with a polynomial fitting equationi(t) calculating the first arrival time distance Depth of each recordi＝|fpi(t)-Sfpi(t) |, and calculatingMean value of first arrival distance
And first arrival distance variance
And N is the total number of the single shot records.
In step 4, the time Sfp is calculated according to a polynomial fitting formulai(t) defining the analysis time window width as Widei(t), the time Window range of the data to be analyzed is Windowi(t)＝[Sfpi(t)-Widei(t)，Sfpi(t)+Widei(t)]And calculating identification parameters of time window energy, time window dominant frequency, time window frequency width, time window spectrum density and time window signal-to-noise ratio of the data in the time window range, wherein the time window energy is the change condition of the energy value in the two-dimensional time-frequency domain along with time, the time window dominant frequency is the change condition of the dominant frequency value in the two-dimensional time-frequency domain along with time, the time window frequency width is the change condition of the frequency band width in the two-dimensional time-frequency domain along with time, the time window spectrum density is the change condition of the strong energy value of different frequency bands in the two-dimensional time-frequency domain along with time, and the time window signal-to-noise ratio is the change condition of the ratio of the signal energy to the total energy in the two-dimensional time-frequency domain along with time.
In step 5, the first arrival distance variance, the time window energy, the time window dominant frequency, the time window bandwidth, the time window spectral density and the time window signal-to-signal ratio are comprehensively considered, each type of identification parameters are respectively assigned with the value of 1.5-2.0, when the comprehensive assignment meets the abnormal track identification condition, the abnormal track is determined, and when the comprehensive assignment meets the identification condition, the comprehensive assignment exceeds 8.0-8.5.
According to the abnormal channel intelligent identification method based on the first arrival information, the time-frequency domain two-dimensional signal is obtained by correcting the S domain transformation method, the abnormal channel can be accurately identified by calculating the identification parameters of the two-dimensional signal, curve fitting is carried out after the first arrival is picked up by using the two-dimensional domain energy ratio method, the identification parameters are calculated in the time window of the planning analysis, the calculation workload is reduced, and the identification of the abnormal channel can be rapidly and intelligently completed according to the assignment and comprehensive consideration of the identification parameters through the identification parameters such as the first arrival distance, the first arrival distance variance, the time window energy, the time window main frequency, the time window frequency width, the time window spectral density and the time window signal-containing ratio.
Drawings
FIG. 1 is a flowchart illustrating an embodiment of an intelligent abnormal track identification method based on first arrival information according to the present invention;
FIG. 2 is a diagram of a time-frequency domain two-dimensional signal obtained by a modified S-domain transform method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an intelligent abnormal track identification result according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
As shown in fig. 1, fig. 1 is a flowchart of an abnormal track intelligent identification method based on first arrival information according to the present invention.
(1) Inputting an original acquisition single shot record, wherein the single shot record is in an SEGD or SEGY format, and inputting shot point pile number, wave detection point pile number, track number, sampling interval, sampling point number, total track number and other track head information of the single shot record;
(2) converting the time domain one-dimensional signal into a time-frequency domain two-dimensional signal by using a modified S domain conversion method, wherein the mathematical formula is as follows:
in the formula: y (t) is original single shot record, t is time, unit is ms, XS (tau, xi) is time-frequency domain two-dimensional signal obtained by conversion, xi is frequency, unit is Hz, parameters sigma and beta are used for adjusting and correcting resolution parameter of S domain transformation, and the value range of sigma is [0.5, 1.5]The value range of beta is [2.0, 3.0]]τ is the time position of the transform time window in ms, and the time-frequency domain two-dimensional signal obtained by the modified S-domain transform method is shown in fig. 2.
(3) In the two-dimensional time-frequency domainThe method comprises the steps of carrying out first arrival picking by using a two-dimensional domain energy ratio method, carrying out first arrival automatic picking through energy, frequency and time, effectively improving the accuracy of first arrival picking, and outputting first arrival automatic picking time fpi(t)，fpi(t) is a one-dimensional array, i.e. each track has an automatic pick-up first arrival time, t is time, unit is ms, i is track number recorded by single shot.
(4) Performing curve fitting on the first arrival time by adopting a polynomial fitting method to obtain a polynomial fitting formula of the first arrival information, wherein the polynomial fitting formula is y-ax2+ bx + c, wherein: y is fitting first-arrival time, x is automatic picking first-arrival time, a, b and c are parameters of a fitting formula, and the first-arrival time Sfp corresponding to each channel of data can be obtained through calculation according to the fitting formulai(t), t is time in ms and i is the track number of the shot record.
(5) Defining an analysis time window width as Widei(t), the time Window range of the data to be analyzed is Windowi(t)＝[Sfpi(t)-Widei(t)，Sfpi(t)+Widei(t)]And calculating the identification parameters of the data in the time window range.
(6) According to the fact that each seismic data has the first arrival automatic picking time fpi(t) calculating the time Sfp with a polynomial fitting equationi(t) calculating the first arrival time distance Depth of each recordi＝|fpi(t)-Sfpi(t) |, and calculating the mean value of the first arrival distances
And first arrival distance variance
And N is the total number of the single shot records.
(7) Window timei(t)＝[Sfpi(t)-Widei(t)，Sfpi(t)+Widei(t)]Calculating judgment parameters such as time window energy, time window dominant frequency, time window bandwidth, time window spectral density and time window signal-to-signal ratio of data in the range, wherein the time window energy is the change of an energy value in a two-dimensional time-frequency domain along with timeThe condition is that the time window dominant frequency is the change condition of the dominant frequency value in the two-dimensional time-frequency domain along with time, the time window bandwidth is the change condition of the frequency bandwidth in the two-dimensional time-frequency domain along with time, the time window spectral density is the change condition of the strong energy values of different frequency bands in the two-dimensional time-frequency domain along with time, and the time window signal-containing ratio is the change condition of the ratio of the signal energy to the total energy in the two-dimensional time-frequency domain along with time.
(8) And comprehensively considering identification parameters such as a first arrival distance, a first arrival distance variance, time window energy, time window dominant frequency, time window bandwidth, time window spectral density, time window signal-to-noise ratio and the like, wherein each type of identification parameters are respectively assigned with a value of 1.5-2.0, and when the comprehensive assignment meets the abnormal track identification condition, the abnormal track is determined, and when the comprehensive assignment meets the identification condition, the comprehensive assignment exceeds 8.0-8.5.
(9) According to the calculated identification parameters, if the abnormal track identification conditions are not met, the abnormal track is not judged, if the abnormal track identification conditions are met, the abnormal track is judged, and the pile number information of the abnormal track is output, so that the abnormal track is quickly and intelligently judged, and the intelligent identification result of the abnormal track is shown in fig. 3.
Aiming at abnormal tracks in mass data, firstly inputting an original acquisition single shot record, and converting a time domain one-dimensional signal into a time-frequency domain two-dimensional signal by using a correction S domain conversion method; secondly, carrying out first arrival picking by using a two-dimensional domain energy ratio method, outputting first arrival automatic picking time information, and carrying out curve fitting on the first arrival time by using a polynomial fitting method to obtain a polynomial fitting formula of the first arrival information; thirdly, calculating judgment parameters such as a first arrival distance, a first arrival distance variance, time window energy, time window dominant frequency, time window bandwidth, time window spectral density, time window signal-to-signal ratio and the like; and finally, according to the calculated identification parameters, the abnormal track meeting the identification conditions of the abnormal track is the abnormal track, and the pile number information is output, so that the identification of the abnormal track is rapidly and intelligently completed.
The above-described embodiment is only one of the preferred embodiments of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.
Claims (7)
1. The abnormal track intelligent identification method based on the first arrival information is characterized by comprising the following steps of:
step 1: inputting an original acquisition single shot record, and converting a time domain one-dimensional signal into a time-frequency domain two-dimensional signal by using a modified S-domain conversion method;
step 2: in a two-dimensional time-frequency domain, carrying out first arrival picking by using a two-dimensional domain energy ratio method, outputting first arrival automatic picking time information, and carrying out curve fitting on the first arrival time by using a polynomial fitting method to obtain a polynomial fitting formula of the first arrival information;
and step 3: calculating the first arrival distance between the automatic picking time of each first arrival and the calculation time of a polynomial fitting formula, and calculating the first arrival distances of all the data to obtain a first arrival distance average value and a first arrival distance variance;
and 4, step 4: in a two-dimensional time-frequency domain, a time window analysis range is defined, and judgment parameters of time window energy, time window dominant frequency, time window bandwidth, time window spectral density and time window signal-to-signal ratio are calculated;
and 5: according to the calculated identification parameters, if the abnormal track identification conditions are not met, the abnormal track is not judged, if the abnormal track identification conditions are met, the abnormal track is judged, and the pile number information of the abnormal track is output, so that the identification of the abnormal track is rapidly and intelligently completed.
2. The method for intelligently identifying abnormal tracks based on first arrival information as claimed in claim 1, wherein in step 1, when an originally collected single shot record is input, track head information of shot point pile number, demodulator probe pile number, track number, sampling interval, sampling point number and total track number of the single shot record is input.
3. The method for intelligently identifying abnormal tracks based on first arrival information as claimed in claim 1, wherein in step 1, the mathematical formula of the modified S-domain transformation method is as follows:
in the formula: y (t) is the original single shot record, t is time and is in unit of ms, XS (tau, xi) is a time-frequency domain two-dimensional signal obtained by conversion, xi is frequency and is in unit of Hz, parameters sigma and beta are used for adjusting and correcting a resolution parameter of S domain conversion, the value range of sigma is [0.5 and 1.5], the value range of beta is [2.0 and 3.0], tau is the time position of a conversion time window and is in unit of ms.
4. The method for intelligently judging abnormal tracks based on first-arrival information as claimed in claim 1, wherein in step 2, the first-arrival automatic picking-up time obtained by the two-dimensional domain energy ratio method is fpi(t)，fpi(t) is a one-dimensional array, that is, each data has a first arrival automatic picking time, and polynomial fitting is performed on the first arrival automatic picking time to obtain a polynomial fitting formula of first arrival information, wherein y is ax2+ bx + c, wherein: y is fitting first arrival time, x is first arrival automatic picking time, a, b and c are fitting formula parameters, the first arrival time corresponding to each data can be obtained through calculation according to the fitting formula, t is time, the unit is ms, and i is the track number of the single shot record.
5. The method for intelligently judging abnormal tracks based on first arrival information as claimed in claim 1, wherein in step 3, the automatic picking time fp is determined according to the first arrival of each track of seismic datai(t) calculating the time Sfp with a polynomial fitting equationi(t) calculating the first arrival time distance Depth of each recordi＝|fpi(t)-Sfpi(t) |, and calculating the mean value of the first arrival distances
And first arrival distance variance
And N is the total number of the single shot records.
6. The method for intelligently identifying abnormal tracks based on first arrival information as claimed in claim 1, wherein in step 4, the time Sfp is calculated according to a polynomial fitting formulai(t) defining the analysis time window width as Widei(t), the time Window range of the data to be analyzed is Windowi(t)＝[Sfpi(t)-Widei(t)，Sfpi(t)+Widei(t)]And calculating judgment parameters of time window energy, time window dominant frequency, time window frequency width, time window spectral density and time window signal-to-noise ratio of the data in the time window range, wherein the time window energy is the change situation of an energy value in a two-dimensional time-frequency domain along with time, the time window dominant frequency is the change situation of a dominant frequency value in the two-dimensional time-frequency domain along with time, the time window frequency width is the change situation of a frequency bandwidth in the two-dimensional time-frequency domain along with time, the time window spectral density is the change situation of strong energy values of different frequency bands in the two-dimensional time-frequency domain along with time, and the time window signal-to-noise ratio is the change situation of the ratio of the signal energy to the total energy in the two-dimensional time-frequency domain along with time.
7. The method according to claim 1, wherein in step 5, the first-arrival distance, the variance of the first-arrival distance, the energy of the time window, the dominant frequency of the time window, the bandwidth of the time window, the spectral density of the time window, and the signal-to-noise ratio of the time window are considered comprehensively, each type of the identification parameters is assigned with a value of 1.5-2.0, when the comprehensive assignment satisfies the identification condition of the abnormal channel, the abnormal channel is identified, and the identification condition satisfying the abnormal channel is identified as the comprehensive assignment exceeding 8.0-8.5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811282072.4A CN111123356B (en) | 2018-10-30 | 2018-10-30 | Abnormal track intelligent identification method based on first arrival information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811282072.4A CN111123356B (en) | 2018-10-30 | 2018-10-30 | Abnormal track intelligent identification method based on first arrival information |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111123356A CN111123356A (en) | 2020-05-08 |
| CN111123356B true CN111123356B (en) | 2022-07-12 |
Family
ID=70484892
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811282072.4A Active CN111123356B (en) | 2018-10-30 | 2018-10-30 | Abnormal track intelligent identification method based on first arrival information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111123356B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112162321B (en) * | 2020-09-17 | 2023-03-14 | 中海油田服务股份有限公司 | Detection method and device for reverse-polarity seismic channel |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2838831B1 (en) * | 2002-04-17 | 2004-08-20 | Inst Francais Du Petrole | METHOD FOR DETERMINING THE PRIMARY STATIC CORRECTIONS TO BE APPLIED TO SEISMIC TRACES |
| US6850062B2 (en) * | 2002-05-10 | 2005-02-01 | 976076 Alberta Inc. | Local multi-scale fourier analysis for MRI |
| CN103439738B (en) * | 2013-08-27 | 2016-09-28 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Seismic prospecting single shot record exception road recognition methods |
| CN104749621A (en) * | 2013-12-26 | 2015-07-01 | 中国石油化工股份有限公司 | Relative amplitude-preserved point spectrum analog high-resolution processing method based on improved S-transform |
| CN104749460B (en) * | 2015-03-04 | 2016-06-08 | 广东电网有限责任公司电力调度控制中心 | A kind of visualizing monitor method of the electricity grid oscillating based on S-transformation |
| CN107272066B (en) * | 2017-06-22 | 2019-01-25 | 东华理工大学 | A kind of noisy seismic signal first-arrival traveltime pick-up method and device |
-
2018
- 2018-10-30 CN CN201811282072.4A patent/CN111123356B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111123356A (en) | 2020-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111723329B (en) | Seismic phase feature recognition waveform inversion method based on full convolution neural network | |
| CN103376464A (en) | Inversion method for stratigraphic quality factor | |
| CN110687592B (en) | Microseism seismic phase identification first arrival picking method, device and storage medium | |
| CN109343118B (en) | Abnormal first arrival time correction method | |
| CN103344989A (en) | Method for analyzing impulse noise interference in vibroseis earthquake records | |
| CN110617982A (en) | Rotating machinery equipment fault identification method based on voiceprint signals | |
| CN101551467A (en) | Automatic first break picking method based on edge detection | |
| CN110954952B (en) | Method for discriminating type of first-motion wave of microseismic signal and correcting wave velocity | |
| CN109425894A (en) | A kind of seismic anomaly road detection method and device | |
| CN111123356B (en) | Abnormal track intelligent identification method based on first arrival information | |
| CN112379439A (en) | Method and device for matching longitudinal wave and transverse wave in seismic data | |
| CN106680873A (en) | Amplitude spectrum ratio method for automatically measuring intensity of harmonic noise of seismic data | |
| CN105277984A (en) | Time-shifting seismic mutual constraint frequency consistency processing method | |
| CN114114400B (en) | Microseism event effective signal pickup method | |
| CN105572733B (en) | A kind of seismic velocity spectrum automatic pick method | |
| CN105467270B (en) | Single Terminal Traveling Wave Fault Location back wave identification algorithm based on frequency spectrum similarity evaluation | |
| CN107679614B (en) | Particle swarm optimization-based real-time sound wave time difference extraction method | |
| WO2002001252A1 (en) | Quality control of data | |
| CN111538082B (en) | Automatic first arrival picking method for seismic wave time-frequency domain | |
| CN112526611A (en) | Method and device for extracting surface seismic wave quality factor | |
| CN104991273A (en) | Method for extracting pre-stack correction trace gathering seismic wavelets | |
| CN112099080B (en) | Method for detecting ground microseism event based on local superposition | |
| CN116047604B (en) | Deep seismic phase rapid pickup method based on amplitude statistics and time-frequency analysis | |
| CN117665923A (en) | Real-time determination method and device for slowness corresponding to array waveform data | |
| CN113138419B (en) | Method and device for extracting downlink wavelet and attenuation parameters |
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 |