Component-dependent sensitivity in tracking rainfall-related seismic velocity changes with a single-station at a tropical weathered soil site in central Brazil

Rainfall infiltration influences mechanical properties of the near surface and plays a fundamental role in controlling subsurface hydrological processes. Seismic ambient noise-based methods have emerged as valuable, non-invasive tools for monitoring hydrological dynamics. In this study, we investigate seismic velocity variations (δν/ν) derived from the coda portion of same-component autocorrelations and cross-component correlations of ambient seismic noise recorded by a single triaxial station installed at the Água Limpa Farm, in Brasília, Brazil. The objective of this work is to evaluate how δν/ν computed from different component combinations respond to precipitation and to assess whether rainfall-related hydrological processes can be tracked through single-station monitoring. A total of 420 days of continuous seismic data were acquired using a RaspberryShake RS3D sensor and processed through a workflow involving preprocessing, phase cross-correlation, temporal stacking, and velocity variation estimation via the Moving Window Cross-Spectral (MWCS) method. Seismic velocity series were then compared with rainfall records and Change Point Analysis was applied to identify significant velocity drops. Most component pairs showed increasing δν/ν during the climatological dry period and decreasing values during the rainy period, although component-dependent differences were observed. Strong correlations were observed between δν/ν drops and cumulative rainfall, particularly for horizontal component combinations. In contrast, vertical-component autocorrelations showed weaker and less consistent relationships with precipitation. We interpret the seismic velocity variations derived from single-station cross-component ambient-noise analysis as consistent with hydrological modulation. Moreover, the observed component-dependent sensitivity highlights the importance of incorporating multi-component data to enhance the detection and interpretation of subsurface hydromechanical processes.