We use cross correlation of the ambient seismic field to estimate seasonal variations of seismic velocity in the Mississippi Embayment and to determine the underlying physical mechanisms. Our main observation is that the δt/t variations correlate primarily with the water table fluctuation, with the largest positive value from May to July and the largest negative value in September/October relative to the annual mean. The δv/v residuals after water level fluctuation correction correlate with air pressure in the short term and follow the trend of temperature in the long term. The δv/v residuals after water level fluctuation and air pressure correction correlate inversely with the wind speed. The correlation coefficients between water table fluctuation and δt/t are independent of the interstation distance and frequency, but high coefficients are observed more often between 0.3 and 1 Hz than between 1 and 2 Hz because high‐frequency coherent signals attenuate faster than low‐frequency ones. The δt/t variations lag behind the water table fluctuation by about 20 days, which suggests that the velocity changes can be attributed to the pore pressure diffusion effect. The seasonal variations of δt/t are azimuthally independent, and a large increase of noise amplitude only introduces a small increase to the δt/t variation. At close distances, the maximum δt/t holds a wide range of values, which is likely related to local structure. At larger distances, velocity variations sample a larger region so that it stabilizes to a more uniform value. We find that the observed changes in wave speed can be explained by a poroelastic model.

Cross-correlations of ambient seismic noise from 277 broadband stations within the Mississippi embayment (ME) with at least 1-month of recording time between 1990 and 2018 are used to estimate source locations of primary and secondary microseisms. We investigate source locations by analyzing the azimuthal distribution of the signal-to- noise ratio (SNR) and positive/negative amplitude differences. We use 84 stations with continuous 1-year recordings to explore seasonal variations of SNRs and amplitude differences. We also investigate the seasonal ambient noise ground motions using 2D frequency-wavenumber analysis of a 50-station array composed of the Northern Embayment Lithosphere Experiment. We observe that (1) two major azimuths can be identified in the azimuthal distribution of SNRs and amplitude difference. We also observe two minor azimuths in the seasonal variation of SNRs, amplitude difference, and 2D FK power spectra. Monthly 2D FK power spectra reveal that two energy sources are active in northern hemisphere winter and two relatively weak sources are active in summer. (2) Back-projection suggests that primary microseisms originate along the coasts of Australia or New Zealand, Canada and Alaska, Newfoundland or Greenland, and South America. (3) Secondary microseisms are generated in the deep water of the northern and southern Pacific Ocean, along the coasts of Canada and Alaska associated with near-shore reflections, and in the deep water of south of Greenland. (4) The azimuthal distribution of amplitude difference of sedimentary Love waves in the period band of 1-5s indicates a local source related to the basin-edge of the ME.

We propose a new data processing flow to compute empirical Green’s functions (EGF) from ambient seismic noise based on a soft thresholding designaling and denoising method using the continuous wavelet transform. The designaling algorithm is carried out during the initial data processing to remove earthquakes and other transient signals in the seismic record. A continuous wavelet transform denoising algorithm removes the noise in the final stacked cross-correlogram. The overall data processing procedure is divided into four stages: (1) single station data preparation, (2) remove earthquakes and other signals in the seismic record, (3) spectrum whitening, cross-correlation and temporal stacking, (4) remove the noise in the stacked cross-correlogram to deliver the final EGF. The whole process is automated to make it accessible for large datasets. Synthetic data constructed with a recorded earthquake and recorded ambient noise is used to test the designaling method. We then apply the new processing flow to data recorded by the USArray Transportable Array stations near the New Madrid Seismic Zone where many seismic events and transient signals are observed in the data. We compare the EGFs calculated from our new flow with time domain normalization and our results show improved signal-to-noise ratios and deliver more reliable measurements that can be used for further processing. The designaling method improves the homogeneity of the ambient noise wavefield which is an intrinsic requirement for seismic interferometry. The final denoising step suppresses random noise and provides clearer EGFs for the next processing step.

We observe the minimum wave speed from May to July due to the increased pore pressure from the water table fluctuation and the maximum wave speed in September/October.• The δt/t correlates primarily with the water table fluctuation and does not show an obvious relationship with the atmospheric pressure, temperature, precipitation, and wind speed.• A poroelastic model can explain the velocity variations in the crust.

An airgun source in a water reservoir has been developed in the past decade as a green active source that had been proven effective to derive short-term subsurface structural changes. However, seasonal water level fluctuation in the reservoir affects the airgun signal, and thus whether the airgun signals can be used to derive robust seasonal variation in subsurface structure remains unclear. We use the airgun data observed in the Binchuan basin to estimate the seasonal variation of seismic travel time and compare the results with those derived from ambient noise data in the same frequency band. Our main observation is that seasonal change δt/t from airgun is negatively correlated to the variation of dominant frequency and water table fluctuation in the reservoir. One possible explanation is that water table fluctuation in the reservoir affects the dominant frequency of the airgun signal and causes significant phase shift. We also compute the travel time changes in P-wave from the empirical Green’s function after deconvolving the waveforms from a reference station that is 50 m from the airgun source. The dominant frequency after deconvolution still shows seasonal variation and correlates inversely to the travel time changes, suggesting that deconvolution cannot completely eliminate the source effect on travel time changes. We also use ambient noise cross-correlation to retrieve coda waves and then derive travel time changes in monthly stacked cross-correlations relative to a yearly average cross-correlation. We observe that seismic travel time increases to its local maximum in the end of August. The travel time changes lag behind the precipitation for about one month. We apply a poroelastic physical model to explain seismic travel time changes and find that a combined effect from precipitation and evaporation might induce the seasonal changes as shown in the ambient noise data. However, the pattern of travel time changes from the airgun differs from that from ambient noise, reflecting the strong effects of airgun source property changes. Therefore, we should be cautious to derive long-term subsurface structural variation from the airgun source and put more attention on stabilizing the dominant frequency of each excitation in the future experiments.