  A major goal of helioseismology is the three-dimensional reconstruction of
the three velocity components of convective flows in the solar interior from
sets of wave travel-time measurements. For small amplitude flows, the forward
problem is described in good approximation by a large system of convolution
equations. The input observations are highly noisy random vectors with a known
dense covariance matrix. This leads to a large statistical linear inverse
problem.
  Whereas for deterministic linear inverse problems several computationally
efficient minimax optimal regularization methods exist, only one
minimax-optimal linear estimator exists for statistical linear inverse
problems: the Pinsker estimator. However, it is often computationally
inefficient because it requires a singular value decomposition of the forward
operator or it is not applicable because of an unknown noise covariance matrix,
so it is rarely used for real-world problems. These limitations do not apply in
helioseismology. We present a simplified proof of the optimality properties of
the Pinsker estimator and show that it yields significantly better
reconstructions than traditional inversion methods used in helioseismology,
i.e.\ Regularized Least Squares (Tikhonov regularization) and SOLA (approximate
inverse) methods.
  Moreover, we discuss the incorporation of the mass conservation constraint in
the Pinsker scheme using staggered grids. With this improvement we can
reconstruct not only horizontal, but also vertical velocity components that are
much smaller in amplitude.
