Matched-Field Processing for Detecting Mofette Activity in the Western Eger Rift, Czechia

Deep-derived carbon dioxide (CO₂) degassing is a globally important process linking crust–mantle fluid transport with atmospheric carbon budgets. Matched Field Processing-Bartlett Beamformer (MFP-BB) method offers a seismic approach for detecting tremor signals generated by these degassing centers (mofette). Its principle relies on comparing recorded wavefields with modeled replicas to identify the most likely source locations.

This study applies the MFP–BB technique to dense-array seismic noise data from three key mofette areas in the Cheb Basin, western Eger Rift—Bublák, Hartoušov, and Soos. We combine field observations with numerical simulations to evaluate the method’s performance. Synthetic tests with interfering noise-embedded sources (SNR = 5 dB) demonstrate that accurate localization is achievable with appropriate frequency selection, and that even 20% perturbations in the velocity model introduce only minor degradation.

Field data were processed through segmentation, noise filtering, and spectral analysis to determine persistent frequency bands used in the algorithm. Across all sites, MFP-BB energy concentrates near the surface, coinciding with known mofette fields and CO₂ discharge zones. These shallow anomalies reflect microtremors generated as ascending CO₂ interacts with groundwater and unconsolidated sediments; additional, weaker anomalies at depths < 200 m may also represent active gas migration.