Lithospheric Structure of the Circum‐Pannonian Region Imaged by S‐To‐P Receiver Functions

Abstract

The lithosphere‐asthenosphere boundary and mid‐lithospheric discontinuities are primary attributes of the upper mantle. The Pannonian region is an extensional sedimentary basin enclosed by collisional orogens. Here, we estimate the negative phase depth of S‐to‐P receiver functions to image the lithospheric thickness and other discontinuities with high resolution, based on the recent dense seismological broadband networks. The lithosphere‐asthenosphere boundary is relatively shallow (<90 km) in the Pannonian Basin system, and deeper (∼90–140 km) in the surrounding orogens, where average surface heat flow values are higher (120 mW/m2) and lower (50–70 mW/m2), respectively. The 1D and 2D common conversion point migration with 3D velocity model provide comparable but different resolution images beneath the wider region of the Pannonian Basin. We obtained deeper values in the Western (∼120 km) and Southern‐Carpathians orogens (∼135 km). Furthermore, we provide new information on the lithospheric thickness and its seismic properties in the eastern part of the study region (e.g., Apuseni Mountains (∼95 km), Eastern‐Carpathians (∼120 km), Moesian Platform (∼90 km) and Transylvanian Basin (∼85 km). The shallower negative phase depth can be interpreted as the lithosphere‐asthenosphere boundary beneath the Pannonian Basin system in agreement with its high heat flow values. In contrast, the deeper negative phase depth estimates in the colder surroundings can be interpreted as intra‐ or mid‐lithospheric discontinuities, when compared with local seismic tomography models. In this region, the correlation with heat flow implies that the observed negative phase depth is of thermo‐chemical or rheological nature.

Evidence for an Imbricated Lower Crust and Its Role in Accommodating Crustal Shortening in Sikkim Himalaya

“…This resulted in a data set of 18,073 RFs. Many other automatic criteria can be applied for quality control of resulting RFs (e.g., Colavitti et al., 2022; Hetényi et al., 2018; Kalmár et al., 2023). Though the automated criterion facilitates fast sorting, it often retains bad RFs.…”