In Section 4.4.1 we introduce the basic computations of helioseismic holography, the ingression and egression. In Section 4.4.2 we review the various calculations that have been used to obtain the Green’s functions used in holography. In Section 4.4.3 we introduce the notion of the local control correlation. Acoustic power measurements are described in Section 4.4.4. Phasesensitive holography is introduced in Section 4.4.5. Section 4.4.6 describes the technique of farside imaging. In Section 4.4.7 we describe acoustic imaging (Chang et al., 1997), a technique closely related to holography.
The ingression and egression are attempts to the answer the following question: Given the wavefield observed at the solar surface, what is the best estimate of the wavefield at some point in the solar interior assuming that the observed wavefield resulted entirely from waves diverging from that point (for the egression) or waves converging towards that point (for the ingression)?
The development of holography has, historically, been motived by analogy with optics (e.g., Lindsey and Braun, 2000a) and as a result the holography literature often employs optical terminology. The target point in the solar interior at which we attempt to estimate the wavefield is termed the “focus point”. In practice, only data in a restricted area on the surface above the focus point is used to compute the egression and ingression. This region is called the “pupil”. The choice of pupil geometry depends on the particular application and will be described when we discuss particular applications of holography.
As described by Lindsey and Braun (2000a), a mathematical motivation for the ingression and egression is the Kirchoff integral solution to the wave equation (e.g., Jackson, 1975). Suppose that a scalar field solves the equation
The Green’s function, , for this problem is defined as the solution to where is the 3D Dirac delta function. The Kirchoff integral equation for is The integration variable ranges over any surface enclosing the locations . For a derivation see for example the introduction in Jackson (1975), noting that the Kirchoff integral equation is a special case of Green’s second identity. The normal derivative, outwards, is denoted by . Equation (69) is interesting as it says that given the value of the field on the surface of a region we can determine the value of the field anywhere inside this region. Holography is an attempt to do just that, determine the wavefield in the solar interior given the wavefield on the solar surface. As discussed in detail by Lindsey and Braun (2004), helioseismic holography is much more complicated than solving the simple scalar wave Equation (67) addressed by the Kirchoff integral.Motivated by the Kirchoff identity (Equation 69), the egression and ingression are defined as
The focus point is located at position in the solar interior. The integration variable denotes horizontal position on the solar surface and ranges over the surface region denoted by the pupil , typically an annular region, or a sector of an annular region, centered on the horizontal position of the focus point, . For examples of pupils see Lindsey and Braun (2000a). The wavefield on the surface is and is temporal angular frequency. Recall that the egression, , is an attempt to estimate the wavefield at the location and frequency based on the waves that are seen, in the pupil at the surface, diverging away . Likewise, the ingression, is an attempt to estimate the wavefield at the location and frequency based on the waves seen, within the pupil, converging towards the focus point. The holography Green’s functions, which depend on a horizontal displacement , a focus depth , and a temporal frequency are denoted by . In the time domain, the causal Green’s function , obtained by temporal inverse Fourier transform from , is zero for . In the time domain, the anticausal Green’s function is zero for . The timedomain causal Green’s function and anticausal Green’s function are related by . The holography Green’s function are not defined as the solution to any particular set of equations, but rather we use the symbol to denote whatever function is used in practice in the computation of the ingression and egression. There are a number of choices that have been used for these Green’s functions (see Section 4.4.2 for details).An intuitive way to interpret Equation (70) is to view the Green’s functions as propagators. In this way, the egression can be seen as the result of using the anticausal Green’s function to propagate the surface wavefield backwards in time. Likewise, the ingression can be seen as the result of using the causal Green’s function to propagate the observed surface wavefield forwards in time.
The egression and ingression are the essential quantities in helioseismic holography. There are many particular ways in which these quantities can be combined to learn about the solar interior. The main techniques are controlcorrelations (Section 4.4.3), acoustic power holography (Section 4.4.4), phasesensitive holography (Section 4.4.5), and farside imaging (Section 4.4.6) which is a special case of phasesensitive holography. Of central importance to all of these methods is the choice of the holography Green’s function , which we now describe.
The two approaches that have been used to construct the holography Green’s functions, , involve ray theory and wave theory.

The local control correlation is the correlation between the observed signal at a particular point on the solar surface and the holographic reconstruction of the signal at that same point computed from the data in a surrounding region. In particular the local control correlations are defined as (see, e.g., Lindsey and Braun, 2004)
Here the depth denotes the coordinate of the solar surface. The angle brackets denote the average over a frequency band of width centered at frequency . Notice that we have not specified the pupil which goes into the ingression/egression calculations. The pupil that is used depends on the particular situation. Figure 26 shows quiet Sun local control correlations, measured using both ray theory and wave theory Green’s functions.

If the egression/ingressions were perfect reconstructions of the observed signal, then the phases of the would both, on average, be zero in the quiet Sun. In practice, this is not the case (Lindsey and Braun, 2004). The phases of the holography Green’s functions are typically corrected so that the phase of the average quiet Sun local control correlation is zero (e.g., Lindsey and Braun, 2000a). We refer to this correction as the empirical dispersion correction.
The goal of acoustic power holography is to estimate the amount of wave power emitted from a particular region, either at particular time or a particular frequency. The estimate of the power emitted at a particular frequency, , at horizontal location and depth is
and the estimate of the power emitted at a particular time is Here is the temporal Fourier transform of . Notice that these estimates depend on the estimate of the Green’s function and the pupil that are used to compute . For further discussions of acoustic power holography see, for example, Lindsey and Braun (1997) and Donea et al. (1999). Acoustic power holography has been used in studies of wave excitation by flares (e.g., Donea et al., 1999) and around active regions (e.g., Donea et al., 2000).
The basic computation in phasesensitive holography is the ingressionegression correlation (see, e.g., Lindsey and Braun, 2000a, 2004),
where and are two pupils and the angle brackets denote averaging over a frequency range of width centered on frequency . Phasesensitive holography has been used to look for soundspeed perturbations and mass flows. In both cases the geometry is the quadrant geometry that is also used in timedistance measurements. In particular the pupil is chosen to be a quarter of an annulus, let us call it for “left”, and is chosen to be the quarter annulus located 180° away, call it for “right”. The symmetric phase is defined as Here the operator returns the phase. The phase is mostly sensitive to soundspeed; this can be seen from the symmetry of the definition. In particular, does not change when the pupils and are interchanged. Notice that for a horizontally uniform soundspeed perturbation . On the other hand, a uniform horizontal flow gives . Thus is zero for a horizontally uniform flow and nonzero for a horizontally uniform soundspeed perturbation. The antisymmetric phase is defined as The symmetry of is such that changes sign under interchange of the pupils and and, thus, the antisymmetric phase is sensitive to horizontal flows. The phases and are analogous to the mean and difference travel times commonly employed in timedistance helioseismology.
Farside imaging is a special case of phasesensitive holography (e.g., Lindsey and Braun, 2000b; Braun and Lindsey, 2001). The idea is to use the wavefield on the visible disk to learn about active regions on the farside of the Sun. Figure 27 shows the geometry that was used by Braun and Lindsey (2001). In order to obtain full coverage of the farside of the Sun, two different geometries were employed. For regions near the antipode of the center of the visible disk a twoskip geometry and twoskip Green’s functions were employed (panel (a) of Figure 27). For focus positions near the limb, the ingression/egression were computed using a singleskip pupil and corresponding Green’s functions, and then correlated with the egression/ingression computed using a threeskip pupil and Green’s functions (the geometry is shown in panel (b) of Figure 27).

Acoustic imaging was first introduced by Chang et al. (1997); for a recent review see Chou et al. (2003). We have included acoustic imaging in the section on helioseismic holography as the definitions and philosophical motivation for the two techniques are quite similar.
The central computations in acoustic imaging (see, e.g., Chou et al., 1999, 2003) are wavefield reconstructions in the solar interior, and , defined by
The focus position is . The function denotes the azimuthal average, around , of the surface wavefield measured a horizontal distance away from . The quantity represents the raytheoretical travel times from the subsurface focus point at to surface points a horizontal distance away from the focus point. For a given , the distance satisfies the timedistance relation established between the focus point and a surface point. This relation, , is computed from a standard solar model, using the ray approximation (see Figure 28). The function is a smooth weight function of (or ), explicitly given by Chou et al. (1999). The sum in Equation (81) involves the observed wavefield inside an annulus with inner and outer radii specified by the range .

In some sense, acoustic imaging is a special case of holography. Here, the pupil is an annulus with inner radius and outer radius given by the timedistance relation . The equivalent holography Green’s function is given by . The signal corresponds to the egression, i.e., is the signal reconstructed from the observations of waves diverging from the focal point, while corresponds to the ingression, the signal estimated from the waves seen converging towards the focus position.
As with holography, both the amplitudes and phases of are used to learn about the solar interior. The square of the amplitude of is an estimate of the power contained in the waves seen diverging from the focus position (Chang et al., 1997). In the terminology of holography, the squared of the amplitude of is called the “egression power.” Chen et al. (1998) introduced the correlation
and then measured phase and group times by fitting a Gaussian wavelet to at fixed target point . Changes in the phase between and result in changes in and thus shift the travel times. A phase shift between the two reconstructions, and , is evidence for local changes in sound speed (Chen et al., 1998). Chou and Duvall Jr (2000) discuss the relationship between timedistance travel times and the travel times measured by acoustic imaging.The forward problem that has received the most attention in acoustic imaging is the dependence of travel times on changes in the sound speed. Chou and Sun (2001) used the ray approximation to estimate the sensitivity of acoustic imaging phase travel times to changes in sound speed. The results showed that in general the horizontal resolution is greater than the vertical resolution and that the resolution decreases with increasing focus depth.
The inverse problem of determining the sound speed from a given set of travel times has been studied in the ray approximation as well. Sun and Chou (2002) tested raytheory RLS inversions of phasetime measurements on artificial data. These tests showed that the RLS inversions were, for good choices of the regularization parameter, capable of reconstructing the model used to generate the artificial data. Inversions of travel times for sound speed were also discussed in detail by Chou and Sun (2001).
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