A high-speed train can be regarded as a moving seismic source when it travels along a railway. Seismic waves from such sources have strong energy and can be used for near-surface characterization, safety monitoring of high-speed railways, and detection of urban underground spaces. Distributed acoustic sensing (DAS) is a newly developed seismic acquisition technology. It has attracted widespread attention due to its advantages of low cost, high sensitivity, high efficiency, and dense sampling. This study investigated near-surface characterization using high-speed train seismic data recorded by DAS. The data were processed to obtain surface waves by seismic interferometry. Thereafter, the extracted surface waves were inverted to obtain the near-surface shear-wave velocity model using a multichannel analysis method. The inverted model is consistent with the subsurface geology of the study area. Our results demonstrate the effectiveness and reliability of DAS-based acquisition and data analysis in near-surface characterization using the high-speed train type of moving sources.

Identifying and locating microseismic events are critical problems in hydraulic fracturing monitoring for unconventional resources exploration. In contrast to active seismic data, microseismic data are usually recorded with unknown source excitation time and source location. In this study, we introduce deconvolution migration (DCM) by combining deconvolution interferometry with interferometric cross-correlation migration (CCM). This method avoids the need for the source excitation time and enhances both the spatial resolution and robustness by eliminating the square term of the source wavelets from cross-correlation migration. The proposed algorithm is divided into the following three steps: 1. generate the virtual gathers by deconvolving the master trace with all other traces in the microseismic gather to remove the unknown excitation time; 2. migrate the virtual gather to obtain a single image of the source location; and 3. stack all of these images together to get the final estimation image of the source location. We test the proposed method on complex synthetic and field dataset from the surface hydraulic fracturing monitoring, and compare the results with those obtained by interferometric cross-correlation migration. The results demonstrate that the proposed method can obtain a 50%higher spatial resolution image of the source location, and more robust estimation with smaller errors of the localization especially in the presence of velocity model errors. This method is also beneficial for source mechanism inversion and global seismology applications.

The traditional methods for ground-penetrating radar (GPR) wavefield simulation suffer from numerical dispersion phenomena and difficulties for models with complex geometry. We propose to use the physics-informed neural networks (PINNs) to solve the GPR wave equation and model the wavefield propagation. Deep fully connected networks are used to approximate the solution to the equations. Automatic differentiation with back propagation is taken to calculate the partial derivatives. The loss function combining the wave equation, boundary condition, and temporal constraints is constructed and minimized to train the PINNs. As the solver is mesh-free, this simulation method avoids numerical dispersion artifacts and is very flexible in implementation for inversion. The numerical examples indicate that the PINNs solver for GPR wavefield simulation has high accuracy and efficiency. We also explore the possibility of inverting the electrical parameters from the observed wavefields. The results demonstrate the promising potential of the proposed approach in GPR forward modeling.

In the implementation of migration of multiples, reverse time migration (RTM) is superior to other migration algorithms because it can handle steeply dipping structures and offer high-resolution images of the complex subsurface. However, the RTM results using two-way wave equation contain high-amplitude, low-frequency noise and false images generated by improper wave paths in migration velocity model with sharp velocity interfaces or strong velocity gradients. To improve the imaging quality in RTM of multiples, we separate the upgoing and downgoing waves in the propagation of source and receiver wavefields. A complex function involved with the Hilbert transform is used in wavefield decomposition. Our approach is cost effective and avoids the large storage of wavefield snapshots required by the conventional wavefield separation technique. We applied migration of multiples with wavefield decomposition on a simple two-layer model and the Sigsbee 2B synthetic data set. Our results demonstrate that the proposed approach can improve the image generated by migration of multiples significantly.

Locating microseismic sources is critical to monitoring the hydraulic fractures that occur during fluid extraction/injection in unconventional oil/gas exploration, geothermal operations, and CO2 sequestration. Waveform-based seismic location methods can reliably and automatically image weak microseismic source locations without phase picking. Among them, the crosscorrelation migration (CCM) method can avoid excitation time scanning by generating virtual gathers. We have adopted a CCM location method based on the hybrid imaging condition (HIC). There are four main steps in the implementation of this method: (1) selection of receivers with good azimuthal coverage, (2) generation of virtual gathers by correlating the reference receiver with the rest of the receivers, (3) summation of back projections in the virtual gathers, and (4) multiplication of all summations. The CCM-HIC method is tested on synthetic and field data sets, and the results are compared with those obtained by the conventional summation imaging condition and multiplication imaging condition. The comparison results determine that CCM-HIC is sufficiently robust to obtain a source image with better stability and higher spatial resolution, despite the presence of strong noise.