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This paper proposes a strategy for the detection and triangulation ofstructural anomalies in solid media. The method revolves around theconstruction of sparse representations of the medium's dynamic response,obtained by learning instructive dictionaries which form a suitable basis forthe response data. The resulting sparse coding problem is recast as a modifieddictionary learning task with additional spatial sparsity constraints enforcedon the atoms of the learned dictionaries, which provides them with a prescribedspatial topology that is designed to unveil anomalous regions in the physicaldomain. The proposed methodology is model agnostic, i.e., it forsakes the needfor a physical model and requires virtually no a priori knowledge of thestructure's material properties, as all the inferences are exclusively informedby the data through the layers of information that are available in theintrinsic salient structure of the material's dynamic response. Thischaracteristic makes the approach powerful for anomaly identification insystems with unknown or heterogeneous property distribution, for which a modelis unsuitable or unreliable. The method is validated using both synthetically

Anomaly-Sensitive Dictionary Learning for Unsupervised Diagnostics of Solid Media