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.

The separation of upgoing and downgoing wavefields is an important technique in the processing of vertical seismic profiling data and ocean bottom cable data. It is also used in reverse time migration (RTM) based on the two-way wave equation to suppress low-frequency, high-amplitude noises and false images. Therefore, we model upgoing and downgoing wavefields directly in the wavefield propagation. There are several methods to obtain separated wavefields. The methods using the Fourier transform require storage of the wavefields, which is not practical due to the extremely high disk-space requirements. Methods using Poynting vectors have an ambiguity problem when crossing a peak or a trough of the wavefields. To improve the accuracy and stability of the modeled upgoing and downgoing wavefields in a complicated velocity model, we evaluate an efficient forward-modeling approach purely based on the Hilbert transform in 3D acoustic wavefield simulation. This method is implemented by the Hilbert transform along the time and depth axis, instead of the Fourier transform. We explicitly derive the formulas for upgoing and downgoing wavefield propagation and attach reproducible source codes. Applications to synthetic models indicate that this method can forward propagate upgoing and downgoing wavefields effectively and improve the imaging quality in migration. This method has various potential applications, e.g., 3D seismic imaging with high computation efficiency.

To eliminate crosstalk within the imaging results of elastic reverse time migration (ERTM), we can separate the coupled P- and S-waves from the forward source wavefield and the backpropagated receiver wavefield. The P- and S-wave decoupling method retains the original phase, amplitude, and physical meaning in the separated wavefields. Thus, it is a vital wavefield separation method in ERTM. However, because these decomposed wavefields are vectors, we could consider how to retrieve scalar images that reveal the real reflectivity of the subsurface. For this purpose, we derive a scalar P-wave equation from the velocity-stress relationship for PP imaging. The phase and amplitude of this scalar P-wave are consistent with the scalarized P-wave. Therefore, this scalar P-wave can be exploited to perform PP imaging directly, with the imaging result retaining the amplitude characteristics. For PS imaging, it is difficult to calculate a dynamic preserved scalar S-wave. However, we have developed a scalar PS imaging method that divides the PS image into energy and sign components according to the geometric relationship between the wavefield vibration and propagation directions. The energy is calculated through the amplitude crosscorrelation of the forward P-wave and backpropagated S-wave from the receivers. The sign is obtained from the dot product of the forward P-wave vector and the backpropagated S-wave vector. These PP and PS imaging methods are suitable for 2D and 3D isotropic media and maintain the correct amplitude information while eliminating polarity-reversal phenomena. Several numerical models are used to verify the robustness and effectiveness of our method.

The interpolation of sparse seismic data is important for seismic data processing, especially in the case of marine data acquisition. The conventional interferometric interpolation method interpolates missing data based on seismic interferometry in the time-space domain. However, missing traces and the limited acquisition aperture will reduce the quality and accuracy of virtual shot gathers, thereby affecting the final interpolation results. To improve the conventional method, we have adopted a four-step Radon domain interferometric interpolation method for sparse seismic data. First, we generate synthetic seismic data with a water-layer model and replace missing traces in the input data with synthetic traces. Second, we sort the input shot gathers into common-receiver-point (CRP) gathers and transform the CRP gathers into the Radon domain using a sparse Radon transform method. Third, we perform Radon domain seismic interferometric interpolation by cross-correlating traces with the same ray parameter and summing over all different ray parameters to obtain a virtual shot gather. Fourth, we apply a 1D least-squares matching filter to the virtual shot gather to obtain the interpolation result. Numerical and field examples demonstrate that our method can effectively interpolate sparse seismic data. Compared with the conventional time-space domain interferometric interpolation method, our method can provide more accurate interpolation results. Moreover, it is less sensitive to the depth of the water layer and an inaccurate impedance contrast of the water bottom.