CN108896456A - Aerosol Extinction inversion method based on feedback-type RBF neural - Google Patents

Aerosol Extinction inversion method based on feedback-type RBF neural Download PDF

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CN108896456A
CN108896456A CN201810397787.8A CN201810397787A CN108896456A CN 108896456 A CN108896456 A CN 108896456A CN 201810397787 A CN201810397787 A CN 201810397787A CN 108896456 A CN108896456 A CN 108896456A
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extinction
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CN108896456B (en
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常建华
李红旭
房久龙
刘振兴
杨镇博
刘秉刚
徐帆
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Nanjing University of Information Science and Technology
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Abstract

The invention discloses a kind of Aerosol Extinction inversion methods based on feedback-type RBF neural, including 1) utilize input and desired output Training RBF Neural Network；Using history echo signal power as the input of RBF neural, using the Aerosol Extinction obtained according to history echo-signal as the desired output of RBF neural；2) it is based on feedback-type RBF neural inverting Aerosol Extinction.The present invention is using feedback-type RBF neural come inverting Aerosol Extinction, the inherent mechanism between information is stored in a network by the study of sample mode, it effectively prevents many hypothesis and brings uncertainty, there is faster response speed and preferable robustness.

Description

Aerosol Extinction inversion method based on feedback-type RBF neural
Technical field
The present invention relates to a kind of Aerosol Extinction inversion methods based on feedback-type RBF neural, and it is molten to belong to gas Glue field of measuring technique.
Background technique
Atmospheric aerosol be collectively constituted by the object of different phase, suspend in an atmosphere, many kinds of solids or liquid The heterogeneous system that particle collectively constitutes will affect the occurrence and development variation of many physical and chemical processes in atmospheric environment.Aerosol The diameter range of particle can stop at least several hours or even several days, to make between 0.001 to 100 μm in an atmosphere Atmospheric composition ingredient, structure etc. change, and upset and destroy original normal ecosystem.It is mainly distributed on entire atmosphere In layer, climatic effect will affect, and then influence the health of the mankind.Therefore, improve big compression ring by detecting and studying aerosol Just there is critically important realistic meaning in border.
Compared to detection means such as satellites, laser radar is because it has many advantages, such as high-spatial and temporal resolution, high measurement accuracy, As a kind of active remote sensing prospecting tools be widely used in Laser Atmospheric Transmission, global climate detection, aerosol radiative effect with And the research fields such as atmospheric environment, realize a wide range of real-time monitoring of the parameters such as Aerosol Extinction, grain Spectral structure, shape. Aerosol detection is being carried out often by radar equation inverting Aerosol Extinction or back scattering using laser radar Coefficient, and then obtain other characteristics of aerosol.Then in inverting extinction coefficient, since there are multiple changes in radar equation Amount, for convenience of calculation, many variables often with empirical value or are assumed to replace, such as Aerosol Extinction boundary value, gas are molten Glue delustring Back-scattering ratio etc., so that there are many uncertain factors for the inverting of extinction coefficient, it is clear that it is molten to be unfavorable for gas The fine inverting of glue optical characteristics.
Summary of the invention
In order to solve the above-mentioned technical problems, the present invention provides a kind of aerosols based on feedback-type RBF neural to disappear Backscatter extinction logarithmic ratio inversion method.
In order to achieve the above object, the technical scheme adopted by the invention is that：
Aerosol Extinction inversion method based on feedback-type RBF neural, including,
1) input and desired output Training RBF Neural Network are utilized；
It is using history echo signal power as the input of RBF neural, the gas obtained according to history echo-signal is molten Desired output of the glue extinction coefficient as RBF neural；
Aerosol Extinction obtain process be：Principle based on aerosol optical depth constructs nonlinear equation, benefit Aerosol extinction Back-scattering ratio is calculated with Chord iterative method, according to aerosol extinction Back-scattering ratio and echo-signal, is used The extinction coefficient of Fernald method inverting aerosol；
2) it is based on feedback-type RBF neural inverting Aerosol Extinction.
Utilize the calculation formula and radar surveying aerosol optical depth of heliograph measurement aerosol optical depth Calculation formula constructs nonlinear equation, calculates aerosol extinction Back-scattering ratio using Chord iterative method.
Heliograph measurement aerosol optical depth calculation formula be,
Wherein, SAODFor the aerosol optical depth of heliograph detection, SALLFlood for heliograph detection is big Gas optical thickness, SMODFor the optical thickness of atmospheric molecule in effective scope of detection, raIt is effective detection range, σmIt (r) is atmosphere The extinction coefficient of molecule.
The calculation formula of radar surveying aerosol optical depth is,
Wherein, LR is the aerosol optical depth of radar detection, σa(r) be aerosol extinction coefficient, be one about Aerosol extinction Back-scattering ratio SaWith the function of detection range r.
The nonlinear equation of building is,
Wherein, LR is the aerosol optical depth of radar detection, σa(r) be aerosol extinction coefficient, be one about Aerosol extinction Back-scattering ratio SaWith the function of detection range r, SAODFor heliograph detection aerosol optical depth, SALLFor the whole atmosphere optical thickness of heliograph detection, SMODFor the optical thickness of atmospheric molecule in effective scope of detection, raIt is effective detection range, σmIt (r) is the extinction coefficient of atmospheric molecule.
Using input and desired output Training RBF Neural Network, current echo-signal is inputted into trained RBF nerve net Network obtains current Aerosol Extinction, calculates aerosol optical depth AOD according to current Aerosol Extinctionlidar, by its With the current aerosol optical depth AOD of heliograph measurementsunIt is compared, if it exists error, corrects current aerosol and disappear Backscatter extinction logarithmic ratio, using revised Aerosol Extinction with corresponding echo-signal as new sample, to feedback-type RBF nerve net Network carries out second training.
Amendment Aerosol Extinction formula be,
AECcorrected=Networkoutput×(1+ω)
Wherein, AECcorrectedFor revised Aerosol Extinction, NetworkoutputDisappear for the aerosol before amendment Backscatter extinction logarithmic ratio, ω are error.
The beneficial effects obtained by the present invention are as follows：1, the present invention utilizes feedback-type RBF neural inverting aerosol extinction system Number is stored the inherent mechanism between information in a network by the study of sample mode, is effectively prevented many hypothesis and is brought Uncertainty, simultaneously because joined the dynamical feedback adjustment process for approaching apparatus measures, so that the confidence level of inversion result is big It is big to increase, realize quick, the accurate inverting of Aerosol Extinction；2, the present invention combines echo-signal and heliograph to visit Measured data is constructed the equation about delustring Back-scattering ratio, is solved using Secant Method, pass through the delustring Back-scattering ratio meter Aerosol Extinction is calculated, delustring Back-scattering ratio bring error is avoided to a certain extent, improves aerosol detection Precision.
Detailed description of the invention
Fig. 1 is the structure principle chart based on feedback-type RBF neural inverting Aerosol Extinction；
Fig. 2 is the determination method flow diagram of aerosol extinction Back-scattering ratio；
Fig. 3 is the acquisition flow chart of training sample；
Fig. 4 is feedback-type RBF neural inversion result figure；
Fig. 5 is cloudy feedback-type RBF neural inversion result figure；
Fig. 6 is fine day feedback-type RBF neural inversion result figure.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, a kind of Aerosol Extinction inversion method based on feedback-type RBF neural, including it is following Step：
Step 1, training sample is obtained.
Training sample includes that input and desired output will using history echo signal power as the input of RBF neural Desired output of the Aerosol Extinction obtained according to history echo-signal as RBF neural.
In order to guarantee that network finally obtains extinction coefficient precision, desired output should be accurate as far as possible, therefore, is using When Fernald method obtains desired output, it is thus necessary to determine that high-precision aerosol extinction Back-scattering ratio, to obtain high-precision Aerosol Extinction.
In order to obtain high-precision aerosol extinction Back-scattering ratio, nonlinear equation can be constructed, Chord iterative method is utilized Calculate aerosol extinction Back-scattering ratio, detailed process：Utilize the calculation formula of heliograph measurement aerosol optical depth With the calculation formula of radar (being here laser radar) measurement aerosol optical depth, nonlinear equation is constructed, Secant Method is utilized Iterate to calculate aerosol extinction Back-scattering ratio.
Heliograph measurement aerosol optical depth calculation formula be：
Wherein, SAODFor the aerosol optical depth of heliograph detection, SALLFlood for heliograph detection is big Gas optical thickness, SMODFor the optical thickness of atmospheric molecule in effective scope of detection, raIt is effective detection range, σmIt (r) is atmosphere The extinction coefficient of molecule is the function of a detection range r, can be obtained according to United States standard atmosphere molecular extinction mode.
The calculation formula of radar surveying aerosol optical depth is,
Wherein, LR is the aerosol optical depth of radar detection, σa(r) be aerosol extinction coefficient, be one about Aerosol extinction Back-scattering ratio SaWith the function of detection range r.
The method of above two measurement aerosol optical depth is mutually indepedent, therefore constitutes and combine using both methods Inverting constructs the nonlinear equation about delustring Back-scattering ratio：
Aerosol extinction Back-scattering ratio is calculated as shown in Fig. 2, selecting two o'clock in value interval using Chord iterative method As iterative initial value, then according to the iterative formula of Secant Method, sequence of iterations is generated, is judged by iteration stopping condition, most Aerosol extinction Back-scattering ratio is obtained eventually.
In conclusion obtaining the process of training sample as shown in Figure 3：Principle building based on aerosol optical depth is non-thread Property equation, using Chord iterative method calculate aerosol extinction Back-scattering ratio, according to aerosol extinction Back-scattering ratio and echo Signal, using the extinction coefficient of Fernald method inverting aerosol.
Step 2, input and desired output Training RBF Neural Network are utilized.
Define X=(x1,x2,…,xn)TFor net input vector, Y=(y1,y2,…,ys)TFor network output, φi(*) is The radial basis function of i-th of hidden layer node.The distribution function of RBF neural is：
Wherein, m is hidden layer neuron number of nodes, i.e. radial basis function Center Number, coefficient wiFor connection weight.
Wherein, φ (*) is radial basis function, | | x-ci| | it is euclideam norm, ciFor i-th of center of RBF, ξiFor I-th of radius of RBF, can obtain network output is：
Therefore, the matrix expression of RBF network can be expressed as：
D=HW+E
Wherein, desired output vector is D=(d1,d2,…,dp)T, desired output and network output between error vector For E=(e1,e2,…,ep)T, weight vectors W=(w1,w2,…,wm)T, regression matrix H=(h1,h2,…,hm)T。
In view of the influence of all training samples, ci、ξiAnd wiAdjustment amount be：
In formula, φi(xj) it is i-th of implicit node to xjInput, η1, η2, η3Respectively corresponding learning rate, ci (t) and ciIt (t+1) is respectively the t times and c when t+1 iterationi, ξi(t) and ξiIt (t+1) is respectively the t times and t+1 iteration When ξi, wi(t) and wiIt (t+1) is respectively the t times and w when t+1 iterationi.Mean square error is obtained according to cost function E, Terminate trained condition with this.When reality output and the mean square error of desired output are less than the threshold value of setting, then network is considered Training is completed.
Step 3, current echo-signal is inputted into trained RBF neural, obtains current Aerosol Extinction.
Step 4, aerosol optical depth AOD is calculated according to current Aerosol Extinctionlidar, by itself and heliograph The current aerosol optical depth AOD of measurementsunIt is compared, if it exists error, is then exported according to correction formula corrective networks (i.e. current Aerosol Extinction), correction formula is as follows：
AECcorrected=Networkoutput×(1+ω)
Wherein, AECcorrectedFor revised Aerosol Extinction, NetworkoutputDisappear for the aerosol before amendment Backscatter extinction logarithmic ratio, ω are error；
Using revised Aerosol Extinction and corresponding echo-signal as new sample, to Feedback Neural Network into Row second training, so that the output Step wise approximation apparatus measures result of Feedback Neural Network.
Fig. 4 is feedback-type RBF neural inversion result figure, is detected using test sample to network performance, is tested Sample equally includes input and desired output, and output and desired output after as can be seen from the figure correcting have higher one Cause property.In order to make the inverting of network-adaptive different weather, the inverting for having carried out cloudy day and fine day is tested, as it can be seen in figures 5 and 6, two Higher consistency is all remain between kind weather condition, with desired output, it was confirmed that the feasibility of this method.
The above method, come inverting Aerosol Extinction, passes through the study of sample mode using feedback-type RBF neural In a network by the inherent mechanism storage between information, many hypothesis are effectively prevented and brings uncertainty, simultaneously because being added The dynamical feedback adjustment process for approaching apparatus measures so that the confidence level of inversion result greatly increases realizes aerosol and disappears Quick, the accurate inverting of backscatter extinction logarithmic ratio；The above method combination echo-signal and heliograph detection data simultaneously are constructed about disappearing The equation of light Back-scattering ratio, is solved using Secant Method, calculates Aerosol Extinction by the delustring Back-scattering ratio, Delustring Back-scattering ratio bring error is avoided to a certain extent, improves the precision of aerosol detection.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations Also it should be regarded as protection scope of the present invention.

Claims (7)

1. the Aerosol Extinction inversion method based on feedback-type RBF neural, it is characterised in that：Including,
1) input and desired output Training RBF Neural Network are utilized；
Using history echo signal power as the input of RBF neural, the aerosol obtained according to history echo-signal is disappeared Desired output of the backscatter extinction logarithmic ratio as RBF neural；
Aerosol Extinction obtain process be：Principle based on aerosol optical depth constructs nonlinear equation, utilizes string The method of cutting iterates to calculate aerosol extinction Back-scattering ratio, according to aerosol extinction Back-scattering ratio and echo-signal, uses The extinction coefficient of Fernald method inverting aerosol；
2) it is based on feedback-type RBF neural inverting Aerosol Extinction.
2. the Aerosol Extinction inversion method according to claim 1 based on feedback-type RBF neural, feature It is：Utilize the calculation formula of heliograph measurement aerosol optical depth and the calculating of radar surveying aerosol optical depth Formula constructs nonlinear equation, calculates aerosol extinction Back-scattering ratio using Chord iterative method.
3. the Aerosol Extinction inversion method according to claim 2 based on feedback-type RBF neural, feature It is：Heliograph measurement aerosol optical depth calculation formula be,
Wherein, SAODFor the aerosol optical depth of heliograph detection, SALLFor the whole atmosphere light of heliograph detection Learn thickness, SMODFor the optical thickness of atmospheric molecule in effective scope of detection, raIt is effective detection range, σmIt (r) is atmospheric molecule Extinction coefficient.
4. the Aerosol Extinction inversion method according to claim 2 based on feedback-type RBF neural, feature It is：The calculation formula of radar surveying aerosol optical depth is,
Wherein, LR is the aerosol optical depth of radar detection, σa(r) it is the extinction coefficient of aerosol, is one about aerosol Delustring Back-scattering ratio SaWith the function of detection range r.
5. the Aerosol Extinction inversion method according to claim 2 based on feedback-type RBF neural, feature It is：The nonlinear equation of building is,
Wherein, LR is the aerosol optical depth of radar detection, σa(r) it is the extinction coefficient of aerosol, is one about aerosol Delustring Back-scattering ratio SaWith the function of detection range r, SAODFor the aerosol optical depth of heliograph detection, SALLFor too The whole atmosphere optical thickness of positive photometer detection, SMODFor the optical thickness of atmospheric molecule in effective scope of detection, raIt is effective Detection range, σmIt (r) is the extinction coefficient of atmospheric molecule.
6. the Aerosol Extinction inversion method according to claim 1 based on feedback-type RBF neural, feature It is：Using input and desired output Training RBF Neural Network, current echo-signal is inputted into trained RBF neural, Current Aerosol Extinction is obtained, aerosol optical depth AOD is calculated according to current Aerosol Extinctionlidar, by its with The current aerosol optical depth AOD of heliograph measurementsunIt is compared, if it exists error, corrects current aerosol extinction Coefficient, using revised Aerosol Extinction with corresponding echo-signal as new sample, to feedback-type RBF neural Carry out second training.
7. the Aerosol Extinction inversion method according to claim 6 based on feedback-type RBF neural, feature It is：Amendment Aerosol Extinction formula be,
AECcorrected=Networkoutput×(1+ω)
Wherein, AECcorrectedFor revised Aerosol Extinction, NetworkoutputFor the aerosol extinction system before amendment Number, ω is error.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110109149A (en) * 2019-05-08 2019-08-09 南京信息工程大学 A kind of laser radar low layer extinction coefficient profile bearing calibration
CN110108672A (en) * 2019-04-12 2019-08-09 南京信息工程大学 A kind of Aerosol Extinction inversion method based on deepness belief network
CN113009437A (en) * 2021-03-03 2021-06-22 云南大学 Method for solving dynamic radar ratio of laser radar according to real-time aerosol distribution
CN118033676A (en) * 2024-04-11 2024-05-14 长春理工大学 Correction method of laser radar extinction coefficient profile based on BP neural network

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102944503A (en) * 2012-10-11 2013-02-27 中国科学院安徽光学精密机械研究所 PM2.5 mass concentration value automatic inversion algorithm based on sun photometer and lidar
CN103175759A (en) * 2013-02-25 2013-06-26 中国科学院安徽光学精密机械研究所 Method for acquiring complex refractive index of urban aerosol on basis of various ground-based remote sensing technologies
CN103278479A (en) * 2013-04-23 2013-09-04 中国科学院安徽光学精密机械研究所 Atmospheric radiation transmission correction system and correction method
CN107144829A (en) * 2017-06-29 2017-09-08 南京信息工程大学 A kind of efficient laser radar echo signal antinoise method
CN107421917A (en) * 2017-05-17 2017-12-01 南京信息工程大学 A kind of multifunction high-precision atmosphere visibility meter and visibility measurement method
CN107728163A (en) * 2017-09-05 2018-02-23 兰州大学 Atmospheric Characteristics layer detection method and device
CN108845306A (en) * 2018-07-05 2018-11-20 南京信息工程大学 Laser radar echo signal antinoise method based on variation mode decomposition
CN109696412A (en) * 2018-12-20 2019-04-30 南京信息工程大学 Infrared gas sensor and atmospheric pressure compensating method based on AGNES Optimized BP Neural Network

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102944503A (en) * 2012-10-11 2013-02-27 中国科学院安徽光学精密机械研究所 PM2.5 mass concentration value automatic inversion algorithm based on sun photometer and lidar
CN103175759A (en) * 2013-02-25 2013-06-26 中国科学院安徽光学精密机械研究所 Method for acquiring complex refractive index of urban aerosol on basis of various ground-based remote sensing technologies
CN103278479A (en) * 2013-04-23 2013-09-04 中国科学院安徽光学精密机械研究所 Atmospheric radiation transmission correction system and correction method
CN107421917A (en) * 2017-05-17 2017-12-01 南京信息工程大学 A kind of multifunction high-precision atmosphere visibility meter and visibility measurement method
CN107144829A (en) * 2017-06-29 2017-09-08 南京信息工程大学 A kind of efficient laser radar echo signal antinoise method
CN107728163A (en) * 2017-09-05 2018-02-23 兰州大学 Atmospheric Characteristics layer detection method and device
CN108845306A (en) * 2018-07-05 2018-11-20 南京信息工程大学 Laser radar echo signal antinoise method based on variation mode decomposition
CN109696412A (en) * 2018-12-20 2019-04-30 南京信息工程大学 Infrared gas sensor and atmospheric pressure compensating method based on AGNES Optimized BP Neural Network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
韩道文等: "基于Madaline网络的气溶胶消光系数反演算法", 《光学学报》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110108672A (en) * 2019-04-12 2019-08-09 南京信息工程大学 A kind of Aerosol Extinction inversion method based on deepness belief network
CN110108672B (en) * 2019-04-12 2021-07-06 南京信息工程大学 Aerosol extinction coefficient inversion method based on deep belief network
CN110109149A (en) * 2019-05-08 2019-08-09 南京信息工程大学 A kind of laser radar low layer extinction coefficient profile bearing calibration
CN110109149B (en) * 2019-05-08 2022-11-25 南京信息工程大学 Laser radar low-layer extinction coefficient profile correction method
CN113009437A (en) * 2021-03-03 2021-06-22 云南大学 Method for solving dynamic radar ratio of laser radar according to real-time aerosol distribution
CN113009437B (en) * 2021-03-03 2023-10-03 云南大学 Method for solving dynamic radar ratio of laser radar according to real-time aerosol distribution
CN118033676A (en) * 2024-04-11 2024-05-14 长春理工大学 Correction method of laser radar extinction coefficient profile based on BP neural network

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LIGO Scientific Collaboration and The Virgo Collaboration lsc-spokesperson@ ligo. org virgo-spokesperson@ ego-gw. eu Abbott BP 1 Abbott R. 1 Abbott TD 2 Acernese F. 3 4 Ackley K. 5 6 Adams C. 7 Adams T. 8 Addesso P. 9 Adhikari RX 1 Adya VB 10 Affeldt C. 10 Afrough M. 11 Agarwal B. 12 Agathos M. 13 Agatsuma K. 14 Aggarwal N. 15 Aguiar OD 16 Aiello L. 17 18 Ain A. 19 Ajith P. 20 Allen B. 10 21 22 Allen G. 12 Allocca A. 23 24 Altin PA 25 Amato A. 26 Ananyeva A. 1 Anderson SB 1 Anderson WG 21 Angelova SV 27 Antier S. 28 Appert S. 1 Arai K. 1 Araya MC 1 Areeda JS 29 Arnaud N. 28 30 Arun KG 31 Ascenzi S. 32 33 Ashton G. 10 Ast M. 34 Aston SM 7 Astone P. 35 Atallah DV 36 Aufmuth P. 22 Aulbert C. 10 AultONeal K. 37 Austin C. 2 Avila-Alvarez A. 29 Babak S. 38 Bacon P. 39 Bader MKM 14 Bae S. 40 Baker PT 41 Baldaccini F. 42 43 Ballardin G. 30 Ballmer SW 44 Banagiri S. 45 Barayoga JC 1 Barclay SE 46 Barish BC 1 Barker D. 47 Barkett K. 48 Barone F. 3 4 Barr B. 46 Barsotti L. 15 Barsuglia M. 39 Barta D. 49 Bartlett J. 47 Bartos I. 50 5 Bassiri R. 51 Basti A. 23 24 Batch JC 47 Bawaj M. 52 43 Bayley JC 46 Bazzan M. 53 54 Bécsy B. 55 Beer C. 10 Bejger M. 56 Belahcene I. 28 Bell AS 46 Berger BK 1 Bergmann G. 10 Bero JJ 57 Berry CPL 58 Bersanetti D. 59 Bertolini A. 14 Betzwieser J. 7 Bhagwat S. 44 Bhandare R. 60 Bilenko IA 61 Billingsley G. 1 Billman CR 5 Birch J. 7 Birney R. 62 Birnholtz O. 10 Biscans S. 1 15 Biscoveanu S. 63 6 Bisht A. 22 Bitossi M. 30 24 Biwer C. 44 Bizouard MA 28 Blackburn JK 1 Blackman J. 48 Blair CD 1 64 Blair DG 64 Blair RM 47 Bloemen S. 65 Bock O. 10 Bode N. 10 Boer M. 66 Bogaert G. 66 Bohe A. 38 Bondu F. 67 Bonilla E. 51 Bonnand R. 8 Boom BA 14 Bork R. 1 Boschi V. 30 24 Bose S. 68 19 Bossie K. 7 Bouffanais Y. 39 Bozzi A. 30 Bradaschia C. 24 Brady PR 21 Branchesi M. 17 18 Brau JE 69 Briant T. 70 Brillet A. 66 Brinkmann M. 10 Brisson V. 28 Brockill P. 21 Broida JE 71 Brooks AF 1 Brown DA 44 Brown DD 72 Brunett S. 1 Buchanan CC 2 Buikema A. 15 Bulik T. 73 Bulten HJ 74 14 Buonanno A. 38 75 Buskulic D. 8 Buy C. 39 Byer RL 51 Cabero M. 10 Cadonati L. 76 Cagnoli G. 26 77 Cahillane C. 1 Bustillo J. Calderón 76 Callister TA 1 Calloni E. 78 4 Camp JB 79 Canepa M. 80 59 Canizares P. 65 Cannon KC 81 Cao H. 72 Cao J. 82 Capano CD 10 Capocasa E. 39 Carbognani F. 30 Caride S. 83 Carney MF 84 Diaz J. Casanueva 28 Casentini C. 32 33 Caudill S. 21 14 Cavaglià M. 11 Cavalier F. 28 Cavalieri R. 30 Cella G. 24 Cepeda CB 1 Cerdá-Durán P. 85 Cerretani G. 23 24 Cesarini E. 86 33 Chamberlin SJ 63 Chan M. 46 Chao S. 87 Charlton P. 88 Chase E. 89 Chassande-Mottin E. 39 Chatterjee D. 21 Chatziioannou K. 90 Cheeseboro BD 41 Chen HY 91 Chen X. 64 Chen Y. 48 Cheng H.-P. 5 Chia H. 5 Chincarini A. 59 Chiummo A. 30 Chmiel T. 84 Cho HS 92 Cho M. 75 Chow JH 25 Christensen N. 71 66 Chu Q. 64 Chua AJK 13 Chua S. 70 Chung AKW 93 Chung S. 64 Ciani G. 5 53 54 Ciolfi R. 94 95 Cirelli CE 51 Cirone A. 80 59 Clara F. 47 Clark JA 76 Clearwater P. 96 Cleva F. 66 Cocchieri C. 11 Coccia E. 17 18 Cohadon P.-F. 70 Cohen D. 28 Colla A. 97 35 Collette CG 98 Cominsky LR 99 Constancio M. Jr. 16 Conti L. 54 Cooper SJ 58 Corban P. 7 Corbitt TR 2 Cordero-Carrión I. 100 Corley KR 50 Cornish N. 101 Corsi A. 83 Cortese S. 30 Costa CA 16 Coughlin MW 71 1 Coughlin SB 89 Coulon J.-P. 66 Countryman ST 50 Couvares P. 1 Covas PB 102 Cowan EE 76 Coward DM 64 Cowart MJ 7 Coyne DC 1 Coyne R. 83 Creighton JDE 21 Creighton TD 103 Cripe J. 2 Crowder SG 104 Cullen TJ 29 2 Cumming A. 46 Cunningham L. 46 Cuoco E. 30 Dal Canton T. 79 Dálya G. 55 Danilishin SL 22 10 D’Antonio S. 33 Danzmann K. 22 10 Dasgupta A. 105 Da Silva Costa CF 5 Datrier LEH 46 Dattilo V. 30 Dave I. 60 Davier M. 28 Davis D. 44 Daw EJ 106 Day B. 76 De S. 44 DeBra D. 51 Degallaix J. 26 De Laurentis M. 17 4 Deléglise S. 70 Del Pozzo W. 58 23 24 Demos N. 15 Denker T. 10 Dent T. 10 De Pietri R. 107 108 Dergachev V. 38 De Rosa R. 78 4 DeRosa RT 7 De Rossi C. 26 30 DeSalvo R. 109 de Varona O. 10 Devenson J. 27 Dhurandhar S. 19 Díaz MC 103 Di Fiore L. 4 Di Giovanni M. 110 95 Di Girolamo T. 50 78 4 Di Lieto A. 23 24 Di Pace S. 97 35 Di Palma I. 97 35 Di Renzo F. 23 24 Doctor Z. 91 Dolique V. 26 Donovan F. 15 Dooley KL 11 Doravari S. 10 Dorrington I. 36 Douglas R. 46 Dovale álvarez M. 58 Downes TP 21 Drago M. 10 Dreissigacker C. 10 Driggers JC 47 Du Z. 82 Ducrot M. 8 Dupej P. 46 Dwyer SE 47 Edo TB 106 Edwards MC 71 Effler A. 7 Eggenstein H.-B. 38 10 Ehrens P. 1 Eichholz J. 1 Eikenberry SS 5 Eisenstein RA 15 Essick RC 15 Estevez D. 8 Etienne ZB 41 Etzel T. 1 Evans M. 15 Evans TM 7 Factourovich M. 50 Fafone V. 32 33 17 Fair H. 44 Fairhurst S. 36 Fan X. 82 Farinon S. 59 Farr B. 91 Farr WM 58 Fauchon-Jones EJ 36 Favata M. 111 Fays M. 36 Fee C. 84 Fehrmann H. 10 Feicht J. 1 Fejer MM 51 Fernandez-Galiana A. 15 Ferrante I. 23 24 Ferreira EC 16 Ferrini F. 30 Fidecaro F. 23 24 Finstad D. 44 Fiori I. 30 Fiorucci D. 39 Fishbach M. 91 Fisher RP 44 Fitz-Axen M. 45 Flaminio R. 26 112 Fletcher M. 46 Fong H. 90 Font JA 85 113 Forsyth PWF 25 Forsyth SS 76 Fournier J.-D. 66 Frasca S. 97 35 Frasconi F. 24 Frei Z. 55 Freise A. 58 Frey R. 69 Frey V. 28 Fries EM 1 Fritschel P. 15 Frolov VV 7 Fulda P. 5 Fyffe M. 7 Gabbard H. 46 Gadre BU 19 Gaebel SM 58 Gair JR 114 Gammaitoni L. 42 Ganija MR 72 Gaonkar SG 19 Garcia-Quiros C. 102 Garufi F. 78 4 Gateley B. 47 Gaudio S. 37 Gaur G. 115 Gayathri V. 116 Gehrels N. 79 Gemme G. 59 Genin E. 30 Gennai A. 24 George D. 12 George J. 60 Gergely L. 117 Germain V. 8 Ghonge S. 76 Ghosh Abhirup 20 Ghosh Archisman 20 14 Ghosh S. 65 14 21 Giaime JA 2 7 Giardina KD 7 Giazotto A. 24 Gill K. 37 Glover L. 109 Goetz E. 118 Goetz R. 5 Gomes S. 36 Goncharov B. 6 González G. 2 Castro JM Gonzalez 23 24 Gopakumar A. 119 Gorodetsky ML 61 Gossan SE 1 Gosselin M. 30 Gouaty R. 8 Grado A. 120 4 Graef C. 46 Granata M. 26 Grant A. 46 Gras S. 15 Gray C. 47 Greco G. 121 122 Green AC 58 Gretarsson EM 37 Groot P. 65 Grote H. 10 Grunewald S. 38 Gruning P. 28 Guidi GM 121 122 Guo X. 82 Gupta A. 63 Gupta MK 105 Gushwa KE 1 Gustafson EK 1 Gustafson R. 118 Halim O. 18 17 Hall BR 68 Hall ED 15 Hamilton EZ 36 Hammond G. 46 Haney M. 123 Hanke MM 10 Hanks J. 47 Hanna C. 63 Hannam MD 36 Hannuksela OA 93 Hanson J. 7 Hardwick T. 2 Harms J. 17 18 Harry GM 124 Harry IW 38 Hart MJ 46 Haster C.-J. 90 Haughian K. 46 Healy J. 57 Heidmann A. 70 Heintze MC 7 Heitmann H. 66 Hello P. 28 Hemming G. 30 Hendry M. 46 Heng IS 46 Hennig J. 46 Heptonstall AW 1 Heurs M. 10 22 Hild S. 46 Hinderer T. 65 Hoak D. 30 Hofman D. 26 Holt K. 7 Holz DE 91 Hopkins P. 36 Horst C. 21 Hough J. 46 Houston EA 46 Howell EJ 64 Hreibi A. 66 Hu YM 10 Huerta EA 12 Huet D. 28 Hughey B. 37 Husa S. 102 Huttner SH 46 Huynh-Dinh T. 7 Indik N. 10 Inta R. 83 Intini G. 97 35 Isa HN 46 Isac J.-M. 70 Isi M. 1 Iyer BR 20 Izumi K. 47 Jacqmin T. 70 Jani K. 76 Jaranowski P. 125 Jawahar S. 62 Jiménez-Forteza F. 102 Johnson WW 2 Jones DI 126 Jones R. 46 Jonker RJG 14 Ju L. 64 Junker J. 10 Kalaghatgi CV 36 Kalogera V. 89 Kamai B. 1 Kandhasamy S. 7 Kang G. 40 Kanner JB 1 Kapadia SJ 21 Karki S. 69 Karvinen KS 10 Kasprzack M. 2 Katolik M. 12 Katsavounidis E. 15 Katzman W. 7 Kaufer S. 22 Kawabe K. 47 Kéfélian F. 66 Keitel D. 46 Kemball AJ 12 Kennedy R. 106 Kent C. 36 Key JS 127 Khalili FY 61 Khan I. 17 33 Khan S. 10 Khan Z. 105 Khazanov EA 128 Kijbunchoo N. 25 Kim Chunglee 129 Kim JC 130 Kim K. 93 Kim W. 72 Kim WS 131 Kim Y.-M. 92 Kimbrell SJ 76 King EJ 72 King PJ 47 Kinley-Hanlon M. 124 Kirchhoff R. 10 Kissel JS 47 Kleybolte L. 34 Klimenko S. 5 Knowles TD 41 Koch P. 10 Koehlenbeck SM 10 Koley S. 14 Kondrashov V. 1 Kontos A. 15 Korobko M. 34 Korth WZ 1 Kowalska I. 73 Kozak DB 1 Krämer C. 10 Kringel V. 10 Krishnan B. 10 Królak A. 132 133 Kuehn G. 10 Kumar P. 90 Kumar R. 105 Kumar S. 20 Kuo L. 87 Kutynia A. 132 Kwang S. 21 Lackey BD 38 Lai KH 93 Landry M. 47 Lang RN 134 Lange J. 57 Lantz B. 51 Lanza RK 15 Lartaux-Vollard A. 28 Lasky PD 6 Laxen M. 7 Lazzarini A. 1 Lazzaro C. 54 Leaci P. 97 35 Leavey S. 46 Lee CH 92 Lee HK 135 Lee HM 136 Lee HW 130 Lee K. 46 Lehmann J. 10 Lenon A. 41 Leonardi M. 110 95 Leroy N. 28 Letendre N. 8 Levin Y. 6 Li TGF 93 Linker SD 109 Littenberg TB 137 Liu J. 64 Liu X. 21 Lo RKL 93 Lockerbie NA 62 London LT 36 Lord JE 44 Lorenzini M. 17 18 Loriette V. 138 Lormand M. 7 Losurdo G. 24 Lough JD 10 Lousto CO 57 Lovelace G. 29 Lück H. 22 10 Lumaca D. 32 33 Lundgren AP 10 Lynch R. 15 Ma Y. 48 Macas R. 36 Macfoy S. 27 Machenschalk B. 10 MacInnis M. 15 Macleod DM 36 Hernandez I. Magaña 21 Magaña-Sandoval F. 44 Zertuche L. Magaña 44 Magee RM 63 Majorana E. 35 Maksimovic I. 138 Man N. 66 Mandic V. 45 Mangano V. 46 Mansell GL 25 Manske M. 21 25 Mantovani M. 30 Marchesoni F. 52 43 Marion F. 8 Márka S. 50 Márka Z. 50 Markakis C. 12 Markosyan AS 51 Markowitz A. 1 Maros E. 1 Marquina A. 100 Martelli F. 121 122 Martellini L. 66 Martin IW 46 Martin RM 111 Martynov DV 15 Mason K. 15 Massera E. 106 Masserot A. 8 Massinger TJ 1 Masso-Reid M. 46 Mastrogiovanni S. 97 35 Matas A. 45 Matichard F. 1 15 Matone L. 50 Mavalvala N. 15 Mazumder N. 68 McCarthy R. 47 McClelland DE 25 McCormick S. 7 McCuller L. 15 McGuire SC 139 McIntyre G. 1 McIver J. 1 McManus DJ 25 McNeill L. 6 McRae T. 25 McWilliams ST 41 Meacher D. 63 Meadors GD 38 10 Mehmet M. 10 Meidam J. 14 Mejuto-Villa E. 9 Melatos A. 96 Mendell G. 47 Mercer RA 21 Merilh EL 47 Merzougui M. 66 Meshkov S. 1 Messenger C. 46 Messick C. 63 Metzdorff R. 70 Meyers PM 45 Miao H. 58 Michel C. 26 Middleton H. 58 Mikhailov EE 140 Milano L. 78 4 Miller AL 5 97 35 Miller BB 89 Miller J. 15 Millhouse M. 101 Milovich-Goff MC 109 Minazzoli O. 66 141 Minenkov Y. 33 Ming J. 38 Mishra C. 142 Mitra S. 19 Mitrofanov VP 61 Mitselmakher G. 5 Mittleman R. 15 Moffa D. 84 Moggi A. 24 Mogushi K. 11 Mohan M. 30 Mohapatra SRP 15 Montani M. 121 122 Moore CJ 13 Moraru D. 47 Moreno G. 47 Morriss SR 103 Mours B. 8 Mow-Lowry CM 58 Mueller G. 5 Muir AW 36 Mukherjee Arunava 10 Mukherjee D. 21 Mukherjee S. 103 Mukund N. 19 Mullavey A. 7 Munch J. 72 Muñiz EA 44 Muratore M. 37 Murray PG 46 Napier K. 76 Nardecchia I. 32 33 Naticchioni L. 97 35 Nayak RK 143 Neilson J. 109 Nelemans G. 65 14 Nelson TJN 7 Nery M. 10 Neunzert A. 118 Nevin L. 1 Newport JM 124 Newton G. 46 Ng KKY 93 Nguyen TT 25 Nichols D. 65 Nielsen AB 10 Nissanke S. 65 14 Nitz A. 10 Noack A. 10 Nocera F. 30 Nolting D. 7 North C. 36 Nuttall LK 36 Oberling J. 47 O’Dea GD 109 Ogin GH 144 Oh JJ 131 Oh SH 131 Ohme F. 10 Okada MA 16 Oliver M. 102 Oppermann P. 10 Oram Richard J. 7 O’Reilly B. 7 Ormiston R. 45 Ortega LF 5 O’Shaughnessy R. 57 Ossokine S. 38 Ottaway DJ 72 Overmier H. 7 Owen BJ 83 Pace AE 63 Page J. 137 Page MA 64 Pai A. 116 145 Pai SA 60 Palamos JR 69 Palashov O. 128 Palomba C. 35 Pal-Singh A. 34 Pan Howard 87 Pan Huang-Wei 87 Pang B. 48 Pang PTH 93 Pankow C. 89 Pannarale F. 36 Pant BC 60 Paoletti F. 24 Paoli A. 30 Papa MA 38 21 10 Parida A. 19 Parker W. 7 Pascucci D. 46 Pasqualetti A. 30 Passaquieti R. 23 24 Passuello D. 24 Patil M. 133 Patricelli B. 146 24 Pearlstone BL 46 Pedraza M. 1 Pedurand R. 26 147 Pekowsky L. 44 Pele A. 7 Penn S. 148 Perez CJ 47 Perreca A. 1 110 95 Perri LM 89 Pfeiffer HP 90 38 Phelps M. 46 Piccinni OJ 97 35 Pichot M. 66 Piergiovanni F. 121 122 Pierro V. 9 Pillant G. 30 Pinard L. 26 Pinto IM 9 Pirello M. 47 Pitkin M. 46 Poe M. 21 Poggiani R. 23 24 Popolizio P. 30 Porter EK 39 Post A. 10 Powell J. 46 149 Prasad J. 19 Pratt JWW 37 Pratten G. 102 Predoi V. 36 Prestegard T. 21 Prijatelj M. 10 Principe M. 9 Privitera S. 38 Prodi GA 110 95 Prokhorov LG 61 Puncken O. 10 Punturo M. 43 Puppo P. 35 Pürrer M. 38 Qi H. 21 Quetschke V. 103 Quintero EA 1 Quitzow-James R. 69 Raab FJ 47 Rabeling DS 25 Radkins H. 47 Raffai P. 55 Raja S. 60 Rajan C. 60 Rajbhandari B. 83 Rakhmanov M. 103 Ramirez KE 103 Ramos-Buades A. 102 Rapagnani P. 97 35 Raymond V. 38 Razzano M. 23 24 Read J. 29 Regimbau T. 66 Rei L. 59 Reid S. 62 Reitze DH 1 5 Ren W. 12 Reyes SD 44 Ricci F. 97 35 Ricker PM 12 Rieger S. 10 Riles K. 118 Rizzo M. 57 Robertson NA 1 46 Robie R. 46 Robinet F. 28 Rocchi A. 33 Rolland L. 8 Rollins JG 1 Roma VJ 69 Romano JD 103 Romano R. 3 4 Romel CL 47 Romie JH 7 Rosińska D. 150 56 Ross MP 151 Rowan S. 46 Rüdiger A. 10 Ruggi P. 30 Rutins G. 27 Ryan K. 47 Sachdev S. 1 Sadecki T. 47 Sadeghian L. 21 Sakellariadou M. 152 Salconi L. 30 Saleem M. 116 Salemi F. 10 Samajdar A. 143 Sammut L. 6 Sampson LM 89 Sanchez EJ 1 Sanchez LE 1 Sanchis-Gual N. 85 Sandberg V. 47 Sanders JR 44 Sassolas B. 26 Sathyaprakash BS 63 36 Saulson PR 44 Sauter O. 118 Savage RL 47 Sawadsky A. 34 Schale P. 69 Scheel M. 48 Scheuer J. 89 Schmidt J. 10 Schmidt P. 1 65 Schnabel R. 34 Schofield RMS 69 Schönbeck A. 34 Schreiber E. 10 Schuette D. 10 22 Schulte BW 10 Schutz BF 36 10 Schwalbe SG 37 Scott J. 46 Scott SM 25 Seidel E. 12 Sellers D. 7 Sengupta AS 153 Sentenac D. 30 Sequino V. 32 33 17 Sergeev A. 128 Shaddock DA 25 Shaffer TJ 47 Shah AA 137 Shahriar MS 89 Shaner MB 109 Shao L. 38 Shapiro B. 51 Shawhan P. 75 Sheperd A. 21 Shoemaker DH 15 Shoemaker DM 76 Siellez K. 76 Siemens X. 21 Sieniawska M. 56 Sigg D. 47 Silva AD 16 Singer LP 79 Singh A. 38 10 22 Singhal A. 17 35 Sintes AM 102 Slagmolen BJJ 25 Smith B. 7 Smith JR 29 Smith RJE 1 6 Somala S. 154 Son EJ 131 Sonnenberg JA 21 Sorazu B. 46 Sorrentino F. 59 Souradeep T. 19 Spencer AP 46 Srivastava AK 105 Staats K. 37 Staley A. 50 Steer D. 39 Steinke M. 10 Steinlechner J. 34 46 Steinlechner S. 34 Steinmeyer D. 10 Stevenson SP 58 149 Stone R. 103 Stops DJ 58 Strain KA 46 Stratta G. 121 122 Strigin SE 61 Strunk A. 47 Sturani R. 155 Stuver AL 7 Summerscales TZ 156 Sun L. 96 Sunil S. 105 Suresh J. 19 Sutton PJ 36 Swinkels BL 30 Szczepańczyk MJ 37 Tacca M. 14 Tait SC 46 Talbot C. 6 Talukder D. 69 Tanner DB 5 Tápai M. 117 Taracchini A. 38 Tasson JD 71 Taylor JA 137 Taylor R. 1 Tewari SV 148 Theeg T. 10 Thies F. 10 Thomas EG 58 Thomas M. 7 Thomas P. 47 Thorne KA 7 Thrane E. 6 Tiwari S. 17 95 Tiwari V. 36 Tokmakov KV 62 Toland K. 46 Tonelli M. 23 24 Tornasi Z. 46 Torres-Forné A. 85 Torrie CI 1 Töyrä D. 58 Travasso F. 30 43 Traylor G. 7 Trinastic J. 5 Tringali MC 110 95 Trozzo L. 157 24 Tsang KW 14 Tse M. 15 Tso R. 1 Tsukada L. 81 Tsuna D. 81 Tuyenbayev D. 103 Ueno K. 21 Ugolini D. 158 Unnikrishnan CS 119 Urban AL 1 Usman SA 36 Vahlbruch H. 22 Vajente G. 1 Valdes G. 2 van Bakel N. 14 van Beuzekom M. 14 van den Brand JFJ 74 14 Van Den Broeck C. 14 Vander-Hyde DC 44 van der Schaaf L. 14 van Heijningen JV 14 van Veggel AA 46 Vardaro M. 53 54 Varma V. 48 Vass S. 1 Vasúth M. 49 Vecchio A. 58 Vedovato G. 54 Veitch J. 46 Veitch PJ 72 Venkateswara K. 151 Venugopalan G. 1 Verkindt D. 8 Vetrano F. 121 122 Viceré A. 121 122 Viets AD 21 Vinciguerra S. 58 Vine DJ 27 Vinet J.-Y. 66 Vitale S. 15 Vo T. 44 Vocca H. 42 43 Vorvick C. 47 Vyatchanin SP 61 Wade AR 1 Wade LE 84 Wade M. 84 Walet R. 14 Walker M. 29 Wallace L. 1 Walsh S. 38 10 21 Wang G. 17 122 Wang H. 58 Wang JZ 63 Wang WH 103 Wang YF 93 Ward RL 25 Warner J. 47 Was M. 8 Watchi J. 98 Weaver B. 47 Wei L.-W. 10 22 Weinert M. 10 Weinstein AJ 1 Weiss R. 15 Wen L. 64 Wessel EK 12 Weßels P. 10 Westerweck J. 10 Westphal T. 10 Wette K. 25 Whelan JT 57 Whitcomb SE 1 Whiting BF 5 Whittle C. 6 Wilken D. 10 Williams D. 46 Williams RD 1 Williamson AR 65 Willis JL 1 159 Willke B. 22 10 Wimmer MH 10 Winkler W. 10 Wipf CC 1 Wittel H. 10 22 Woan G. 46 Woehler J. 10 Wofford J. 57 Wong KWK 93 Worden J. 47 Wright JL 46 Wu DS 10 Wysocki DM 57 Xiao S. 1 Yamamoto H. 1 Yancey CC 75 Yang L. 160 Yap MJ 25 Yazback M. 5 Yu Hang 15 Yu Haocun 15 Yvert M. 8 Zadrożny A. 132 Zanolin M. 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