Novel Data‐Driven Receiver Function Clustering and Analysis Along the Alaska‐Aleutian Volcanic Arc

Abstract

Constraining magma storage and transport through the entire crust at a variety of arc volcanoes is essential to understand how subduction parameters may influence these processes. However, constraints throughout the crust are often limited to volcanoes that either fortuitously have properties that illuminate the entire crustal system, or volcanoes where high‐resolution geophysical imaging has been performed. Seismic receiver functions (RFs) offer sensitivity to the whole crust independent of local seismicity distribution using a few instruments. However, near volcanoes, they are often complex, requiring detailed analysis and potentially non‐unique velocity model solutions. Here, we introduce a novel, data‐driven method to identify and map RF variability using spectral clustering and kernel logistic regression. We apply it to 16 volcanoes in the Aleutian arc, enabling systematic comparisons of RF patterns without need for detailed velocity modeling. We observe that a subset of these volcanoes has RF variability that is spatially well‐separated in the crust, while others appear to have minimal spatial organization. Based on comparisons with previous results, we find that the former tends to occur at volcanoes with relatively simple magma storage systems, and the latter at volcanoes with complex, multi‐layer storage. This suggests that our technique reveals first‐order variability in crustal magmatic architecture. Furthermore, the ability to recover spatially coherent RF patterns shows no clear correlation with along‐arc subduction parameters but instead appears associated with edifice morphology. This work establishes a systematic framework for comparing arc volcanic structure and a foundation for future RF velocity modeling, extendable to other heterogeneous settings.