# deghosting

## Spanish translation: Dejarlo igual y explicar el proceso (ver def.)

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GLOSSARY ENTRY (DERIVED FROM QUESTION BELOW)
 English term or phrase: deghosting Spanish translation: Dejarlo igual y explicar el proceso (ver def.) Entered by:

 18:52 May 19, 2005
English to Spanish translations [PRO]
Science (general)
 English term or phrase: deghosting a number of "deghosting" and water layer effect attenuation techniques are known in the art for use with OBCs
 Yo lo dejaría igual y explicaría el proceso. Ver def. Explanation:MARINE DEGHOSTING The marine ghost presents a problem that is essentially insoluble; but because it is always with us, we need to understand how to do the best we can with it. Even if an airgun could emit a perfect impulse, the impulse would reflect from the nearby water surface, thereby giving a second pulse of opposite polarity. The energy going down into the earth is therefore a doublet when we would prefer a single pulse. Likewise, hydrophones see the upcoming wave once coming up, and an instant later they see the wave with opposite polarity reflecting from the water surface. Thus the combined system is effectively a second derivative wavelet (1,-2,1) that is convolved with signals of interest. Our problem is to remove this wavelet by deconvolution. It is an omnipresent problem and is cleanly exposed on marine data where the water bottom is hard and deep. Theoretically, a double integration of the second derivative gives the desired pulse. A representation in the discrete time domain is the product of (1-Z)2 with $1+2Z+ 3Z^2+ 4Z^3+ 5Z^4+ \cdots$, which is 1. Double integration amounts to spectral division by $-\omega^2$.Mathematically the problem is that $-\omega^2$ vanishes at $\omega=0$.In practice the problem is that dividing by $\omega^2$ where it is small amplifies noises at those low frequencies. (Inversion theorists are even more frustrated because they are trying to create something like a velocity profile, roughly a step function, and they need to do something like a third integration.) Old nuts like this illustrate the dichotomy between theory and practice. --------------------------------------------------Note added at 5 mins (2005-05-19 18:57:58 GMT)--------------------------------------------------sepwww.stanford.edu/sep/ prof/pvi/ls/paper_html/node19.html --------------------------------------------------Note added at 5 mins (2005-05-19 18:58:32 GMT)--------------------------------------------------Data Association for Deghosting in Y-Shaped Passive Linear Array Sonars BONHWA KU JEHAN YOON Korea University DAVID K. HAN Johns Hopkins University HANSEOK KO, Senior Member, IEEE Korea University This paper deals with data association using three sets of passive linear array sonars (PLAS) geometrically positioned in a Y-shaped configuration, fixed in an underwater environment. The data association problem is directly transformed into a 3-D assignment problem, which is known to be NP hard. For generic passive sensors, it can be solved using conventional algorithms, while in PLAS, it becomes a formidable task due to the presence of bearing ambiguity. Thus, the central issue of the problem in PLAS is how to eliminate the bearing ambiguity without increasing tracking error. To solve this problem, the 3-D assignment algorithm used the likelihood value of only those observed bearing measurements is modified by incorporating frequency information in consecutive time-aligned scans. The region of possible ghost targets is first established by the geometrical relation of PLAS with respect to target. The ghost targets are then confirmed and eliminated by generating multiple observations in consecutive scans. Representative simulations demonstrate the effectiveness of the proposed approach.--------------------------------------------------Note added at 6 mins (2005-05-19 18:59:04 GMT)--------------------------------------------------Chapter [*] provides a theoretical solution to this problem in the Fourier domain. Here we will express the same concepts in the time domain. Define as follows: yt Given data. bt Known filter. xt Excitation (to be found). $n_t = y_t \\ -\\ x_t {\\rm *} b_t$ Noise: data minus filtered excitation. With Z-transforms the problem is given by Y(Z)=B(Z)X(Z)+N(Z). Our primary wish is $N\\approx 0$.Our secondary wish is that X not be infinity as X=Y/B threatens. This second wish is expressed as $\\epsilon X \\approx 0$and is called stabilizing\" or damping.\" In the Fourier domain the wishes are \\begin{eqnarray} Y & \\approx & B X \\ 0 & \\approx & \\epsilon X\\end{eqnarray} (50) (51) The formal expression of the regression is \\begin{eqnarray} \\min_X \\ \\ ( \\ \\vert\\vert Y-BX \\vert\\vert \\ +\\ \\epsilon^2 \\vert\\vert X \\vert\\vert \\ )\\end{eqnarray} (52) In the time domain the regression is much more explicit: \\begin{displaymath} \\left[ \\begin{array} {c} y_0 \\ y_1 \\ y_2 \\ y_3... ...x_2 \\ x_3 \\ x_4 \\ x_5 \\ x_6 \\end{array} \\right]\\end{displaymath} (53) where $\\cdot$\'\' denotes a zero. Since it is common to add $\\epsilon \\bold I$ to an operator to stabilize it, I prepared subroutine ident() for this purpose. It is used so frequently that I coded it in a special way to allow the input and output to overlie one another. subroutine ident( adj, add, epsilon, n, pp, qq ) integer i, adj, add, n real epsilon, pp(n), qq(n) # equivalence (pp,qq) OK if( adj == 0 ) { if( add == 0 ) { do i=1,n { qq(i) = epsilon * pp(i) } } else { do i=1,n { qq(i) = qq(i) + epsilon * pp(i) } } } else { if( add == 0 ) { do i=1,n { pp(i) = epsilon * qq(i) } } else { do i=1,n { pp(i) = pp(i) + epsilon * qq(i) } } } return; end We can use any convolution routine we like, but for simplicity, I selected contrunc() so the output would be the same length as the input. The two operators ident() and contrunc() could be built into a new operator. I found it easier to simply cascade them in the deghosting subroutine deghost() below. # deghost: min |rrtop| = | y - bb (contrunc) xx | # x |rrbot| | 0 - epsilon I xx | subroutine deghost( eps, nb,bb, n, yy, xx, rr, niter) integer iter, nb, n, niter real bb(nb), yy(n), eps # inputs. typically bb=(1,-2,1) real xx(n), rr(n+n) # outputs. temporary real dx(n), sx(n), dr(n+n), sr(n+n) call zero( n, xx) call copy( n, yy, rr(1 )) # top half of residual call zero( n , rr(1+n)) # bottom of residual do iter= 0, niter { call contrunc(1,0,1,nb,bb, n,dx,n,rr); call ident(1,1,eps, n,dx,rr(1+n)) call contrunc(0,0,1,nb,bb, n,dx,n,dr); call ident(0,0,eps, n,dx,dr(1+n)) call cgstep( iter, n,xx,dx,sx, _ n+n,rr,dr,sr) } return; end http://sepwww.stanford.edu/sep/prof/pvi/ls/paper_html/node19...
Selected response from:

Gabriela Rodriguez
Argentina
Local time: 16:37
 Graded automatically based on peer agreement.4 KudoZ points were awarded for this answer

4 +3antifantasma
 Ernesto de Lara
4 +3Yo lo dejaría igual y explicaría el proceso. Ver def.
 Gabriela Rodriguez
3limpiar la señal- remover resonanciasGabo Pena

3 mins   confidence: peer agreement (net): +3
antifantasma

Explanation:
Si estamos hablando de la atenuaci¢n en hidrofon¡a

 Ernesto de LaraLocal time: 13:37Works in fieldNative speaker of: SpanishPRO pts in category: 24

agree  George Rabel: who you gonna call?
 1 min
-> gracias George, seguramente a los mocosos

neutral  moken: ¡Hola Ernesto! No opino sobre la respuesta pero, ¿viste el historial del colega? 404 preguntas hechas, 95 de ellas sin cerrar (la mayoría de la cerradas, por el 'robot'), contra 6 intentos de ayudar un compañero. Creo que es un récord. :O) :O)
 1 hr
-> Hola, µlvaro, los hay peores, creo

agree  Hector Aires: Agrí. con Álvaro y con Ernesto, hay mucho peores. Hay quien jamás ha contestado algo aunque no fuera aceptado. Por mi parte, gustoso haría un listado de "amigos y colegas" y los pondría en una vitrina/escaparate/vidriera sometidos al escarnio público.
 4 hrs
-> gracias H‚ctor. Me has dado la idea de montar una verduler¡a cercana a la vitrina

neutral  Gabo Pena: that's funny George!
 6 hrs

agree
 1 day22 hrs
-> gracias Cecilia

4 mins   confidence: peer agreement (net): +3
Yo lo dejaría igual y explicaría el proceso. Ver def.

Explanation:
MARINE DEGHOSTING
The marine ghost presents a problem that is essentially insoluble; but because it is always with us, we need to understand how to do the best we can with it. Even if an airgun could emit a perfect impulse, the impulse would reflect from the nearby water surface, thereby giving a second pulse of opposite polarity. The energy going down into the earth is therefore a doublet when we would prefer a single pulse. Likewise, hydrophones see the upcoming wave once coming up, and an instant later they see the wave with opposite polarity reflecting from the water surface. Thus the combined system is effectively a second derivative wavelet (1,-2,1) that is convolved with signals of interest. Our problem is to remove this wavelet by deconvolution. It is an omnipresent problem and is cleanly exposed on marine data where the water bottom is hard and deep.

Theoretically, a double integration of the second derivative gives the desired pulse. A representation in the discrete time domain is the product of (1-Z)2 with $1+2Z+ 3Z^2+ 4Z^3+ 5Z^4+ \cdots$, which is 1. Double integration amounts to spectral division by $-\omega^2$.Mathematically the problem is that $-\omega^2$ vanishes at $\omega=0$.In practice the problem is that dividing by $\omega^2$ where it is small amplifies noises at those low frequencies. (Inversion theorists are even more frustrated because they are trying to create something like a velocity profile, roughly a step function, and they need to do something like a third integration.) Old nuts like this illustrate the dichotomy between theory and practice.

--------------------------------------------------
Note added at 5 mins (2005-05-19 18:57:58 GMT)
--------------------------------------------------

sepwww.stanford.edu/sep/ prof/pvi/ls/paper_html/node19.html

--------------------------------------------------
Note added at 5 mins (2005-05-19 18:58:32 GMT)
--------------------------------------------------

Data Association for
Deghosting in Y-Shaped
Passive Linear Array Sonars

BONHWA KU

JEHAN YOON
Korea University

DAVID K. HAN
Johns Hopkins University

HANSEOK KO, Senior Member, IEEE
Korea University

This paper deals with data association using three sets of passive linear array sonars (PLAS) geometrically positioned in a Y-shaped configuration, fixed in an underwater environment. The data association problem is directly transformed into a 3-D assignment problem, which is known to be NP hard. For generic passive sensors, it can be solved using conventional algorithms, while in PLAS, it becomes a formidable task due to the presence of bearing ambiguity. Thus, the central issue of the problem in PLAS is how to eliminate the bearing ambiguity without increasing tracking error. To solve this problem, the 3-D assignment algorithm used the likelihood value of only those observed bearing measurements is modified by incorporating frequency information in consecutive time-aligned scans. The region of possible ghost targets is first established by the geometrical relation of PLAS with respect to target. The ghost targets are then confirmed and eliminated by generating multiple observations in consecutive scans. Representative simulations demonstrate the effectiveness of the proposed approach.

--------------------------------------------------
Note added at 6 mins (2005-05-19 18:59:04 GMT)
--------------------------------------------------

Chapter [*] provides a theoretical solution to this problem in the Fourier domain. Here we will express the same concepts in the time domain. Define as follows:

yt Given data.
bt Known filter.
xt Excitation (to be found).
$n_t = y_t \\ -\\ x_t {\\rm *} b_t$ Noise: data minus filtered excitation.

With Z-transforms the problem is given by Y(Z)=B(Z)X(Z)+N(Z). Our primary wish is $N\\approx 0$.Our secondary wish is that X not be infinity as X=Y/B threatens. This second wish is expressed as $\\epsilon X \\approx 0$and is called stabilizing\" or damping.\" In the Fourier domain the wishes are
\\begin{eqnarray} Y & \\approx & B X \\ 0 & \\approx & \\epsilon X\\end{eqnarray} (50)
(51)
The formal expression of the regression is
\\begin{eqnarray} \\min_X \\ \\ ( \\ \\vert\\vert Y-BX \\vert\\vert \\ +\\ \\epsilon^2 \\vert\\vert X \\vert\\vert \\ )\\end{eqnarray} (52)
In the time domain the regression is much more explicit:

\\begin{displaymath} \\left[ \\begin{array} {c} y_0 \\ y_1 \\ y_2 \\ y_3... ...x_2 \\ x_3 \\ x_4 \\ x_5 \\ x_6 \\end{array} \\right]\\end{displaymath} (53)
where $\\cdot$\'\' denotes a zero. Since it is common to add $\\epsilon \\bold I$ to an operator to stabilize it, I prepared subroutine ident() for this purpose. It is used so frequently that I coded it in a special way to allow the input and output to overlie one another.

real epsilon, pp(n), qq(n) # equivalence (pp,qq) OK
if( adj == 0 ) {
if( add == 0 ) { do i=1,n { qq(i) = epsilon * pp(i) } }
else { do i=1,n { qq(i) = qq(i) + epsilon * pp(i) } }
}
else { if( add == 0 ) { do i=1,n { pp(i) = epsilon * qq(i) } }
else { do i=1,n { pp(i) = pp(i) + epsilon * qq(i) } }
}
return; end

We can use any convolution routine we like, but for simplicity, I selected contrunc() so the output would be the same length as the input. The two operators ident() and contrunc() could be built into a new operator. I found it easier to simply cascade them in the deghosting subroutine deghost() below.

# deghost: min |rrtop| = | y - bb (contrunc) xx |
# x |rrbot| | 0 - epsilon I xx |
subroutine deghost( eps, nb,bb, n, yy, xx, rr, niter)
integer iter, nb, n, niter
real bb(nb), yy(n), eps # inputs. typically bb=(1,-2,1)
real xx(n), rr(n+n) # outputs.
temporary real dx(n), sx(n), dr(n+n), sr(n+n)
call zero( n, xx)
call copy( n, yy, rr(1 )) # top half of residual
call zero( n , rr(1+n)) # bottom of residual
do iter= 0, niter {
call contrunc(1,0,1,nb,bb, n,dx,n,rr); call ident(1,1,eps, n,dx,rr(1+n))
call contrunc(0,0,1,nb,bb, n,dx,n,dr); call ident(0,0,eps, n,dx,dr(1+n))
call cgstep( iter, n,xx,dx,sx, _
n+n,rr,dr,sr)
}
return; end

http://sepwww.stanford.edu/sep/prof/pvi/ls/paper_html/node19...

 Gabriela RodriguezArgentinaLocal time: 16:37Native speaker of: SpanishPRO pts in category: 12
 Graded automatically based on peer agreement.

agree  moken: ¡Hola Gaby! No opino sobre la respuesta pero, ¿viste el historial del colega? 404 preguntas hechas, 95 de ellas sin cerrar (la mayoría de la cerradas, por el 'robot'), contra 6 intentos de ayudar un compañero. Creo que es un récord. :O) :O)
 1 hr
-> Hola Álvaro, no tenía ni idea jaja, ayer una chica hizo 20 y pico preguntas seguidas (ya parecía una tomada de pelo),sos muy amable (da gusto hablar con vos, hay algunos que sólo critican en el sitio, te juro que yo soy nueva y asustan). Muchos saludos!!!

agree  Hector Aires: Agrí con Álvaro y con Ernesto, hay mucho peores. Hay quien jamás ha contestado algo aunque no fuera aceptado. Por mi parte, gustoso haría un listado de "amigos y colegas" y los pondría en una vitrina/escaparate/vidriera sometidos al escarnio público.
 4 hrs
-> Gracias Hector, me levantás un poco el ánimo (realmente sos otra de las personas con las que da gusto hablar), hay quienes parece que compitieran por un título mundial en lugar de querer ayudar. Te mando muchos saludos y te vuelvo a agradecer!!!!!!!!!!!

agree  Gabo Pena: no te asustes che, que no te pueden morder por el internet..=8^7
 6 hrs
-> Hola Bo, lo que pasa es que por poco te insultan (por decirlo finamente jajjaa), y no estaba acostumbrada, pero ahora les contesto (por supupuesto "cortésmente"). Muchos saludos y vos también sos otro de los amables del sitio con los que da gusto cruzarse

neutral  charlesink: no sólohace muchas preguntas sino que no da ningún dato sobre él ¿ella?
 15 days
-> Tenés razón Carlos y ni siquiera cierra las preguntas. Te deseo que pases un lindo fin de semana!!!!!!!!

6 hrs   confidence:
limpiar la señal- remover resonancias

Explanation:
Ghostbusters

 Gabo PenaLocal time: 12:37Native speaker of: English, SpanishPRO pts in category: 4

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