  This paper proposes a strategy for the detection and triangulation of
structural anomalies in solid media. The method revolves around the
construction of sparse representations of the medium's dynamic response,
obtained by learning instructive dictionaries which form a suitable basis for
the response data. The resulting sparse coding problem is recast as a modified
dictionary learning task with additional spatial sparsity constraints enforced
on the atoms of the learned dictionaries, which provides them with a prescribed
spatial topology that is designed to unveil anomalous regions in the physical
domain. The proposed methodology is model agnostic, i.e., it forsakes the need
for a physical model and requires virtually no a priori knowledge of the
structure's material properties, as all the inferences are exclusively informed
by the data through the layers of information that are available in the
intrinsic salient structure of the material's dynamic response. This
characteristic makes the approach powerful for anomaly identification in
systems with unknown or heterogeneous property distribution, for which a model
is unsuitable or unreliable. The method is validated using both synthetically
