Seismicity of several intraplate seismic zones in the North American midcontinent is believed to be related to reactivation of ancient faults in Precambrian continental rifts by the contemporary stress field. Existence of such a rift system beneath the Wabash Valley Seismic Zone (WVSZ) is not clear. Here we obtained a crustal structural image along a 300-km-long profile across WVSZ using a dense linear seismic array. We first calculated teleseismic receiver functions of stations and applied the Common-Conversion-Point stacking method to image crustal interfaces and the Moho. We then used ambient noise cross correlation to obtain phase and group velocities of Rayleigh and Love waves. Finally, we jointly inverted the receiver function and surface wave dispersion data to determine shear wave velocity structure along the profile. The results show a thick (50-to 60-km) crust with a typical Proterozoic crustal layering: a 1-to 2-km thick Phanerozoic sedimentary layer, an upper crust ∼15 km thick, and a 30-to 40-km-thick lower crust. The unprecedented high-resolution image also reveals a 50-km-wide high-velocity body above an uplifted Moho and several velocity anomalies in the upper and middle crust beneath the La Salle Deformation Belt. We interpreted them as features produced by magmatic intrusions in a failed, immature continental rift during the end of Precambrian. Current seismicity in WVSZ is likely due to reactivation of ancient faults of the rift system by a combination of stress fields from the far-field plate motion and prominent crustal and upper mantle heterogeneities in the region.

Low frequencies are vital for full-waveform inversion (FWI) to retrieve long-scale features and reliable subsurface properties from seismic data. Unfortunately, low frequencies are missing because of limitations in seismic acquisition steps. Furthermore, there is no explicit expression for transforming high frequencies into low frequencies. Therefore, low-frequency reconstruction (LFR) is imperative. Recently developed deep-learning (DL)-based LFR methods are based on either 1D or 2D convolutional neural networks (CNNs), which cannot take full advantage of the information contained in 3D prestack seismic data. Therefore, we present a DL-based LFR approach in which high frequencies are transformed into low frequencies by training an approximately symmetric encoding-decoding-type bridge-shaped 3D CNN. Our motivation is that the 3D CNN can naturally exploit more information that can be effectively used to improve the LFR result. We designed a Hanning-based window for suppressing the Gibbs effect associated with the hard splitting of the low- and high-frequency data. We report the significance of the convolutional kernel size on the training stage convergence rate and the performance of CNN’s generalization ability. CNN with reasonably large kernel sizes has a large receptive field and is beneficial to long-wavelength LFR. Experiments indicate that our approach can accurately reconstruct low frequencies from bandlimited high frequencies. The results of 3D CNN are distinctly superior to those of 2D CNN in terms of precision and highly relevant low-frequency energy. FWI on synthetic data indicates that the DL-predicted low frequencies nearly resemble those of actual low frequencies, and the DL-predicted low frequencies are accurate enough to mitigate the FWI’s cycle-skipping problems. Codes and data of this work are shared via a public repository.

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