Sparse reconstruction methods, such as Compressive Sensing, are powerful methods in acoustic array processing, as they make wideband reconstruction possible. However, when addressing sound fields that are not necessarily sparse (e.g., in acoustic near-fields, reflective environments, extended sources, etc.), the methods can lead to a poor reconstruction of the sound field. This study examines the use of sparse analysis priors to promote block-sparse solutions. In particular, a Fused Total Generalized Variation (F-TGV) method is developed, to analyze the sound field in the near-field of acoustic sources. The method promotes sparsity both on the spatial derivatives of the solution and on the solution itself, thus seeking solutions where the non-zero coefficients are grouped together. The performance of the method is examined numerically and experimentally, and compared with established methods. The results indicate that the F-TGV method is suitable to examine both compact and spatially extended sources. The method is promising for its generality, robustness to noise, and the capability to provide a wideband reconstruction of sound fields that are not necessarily sparse.