Seismic diffractions contain valuable information regarding small-scale inhomogeneities or discontinuities, and therefore they can be used for seismic interpretation in the exploitation of hydrocarbon reservoirs. Velocity analysis is a necessary step for accurate imaging of these diffractions. A new method for diffraction velocity analysis and imaging is proposed that uses an improved adaptive minimum variance beamforming technique. This method incorporates the minimum variance, coherence factor, and correlation properties to improve the signal-to-noise ratio and enhance correlations. Our method can make seismic diffractions become better focused in semblance panels, allowing for the optimal migration velocity for diffractions to be accurately picked. Synthetic and field examples demonstrate that the migration velocity for the diffractions can differ from that for the reflections. The results suggest that the diffraction velocity analysis and imaging method is feasible for accurately locating and identifying small-scale discontinuities, which leads to the possibility of using this approach for practical application and seismic interpretation.

Groundwater distribution influenced by mining activities is hard to be tracked due to the lack of accurate and real-time monitoring data. To address the groundwater dynamic change data under the mining conditions of open-pit coal mines, a three-dimensional automatic observation network of groundwater was constructed, and this observation network takes open-pit as the center, showing a radioactive distribution, based on hydrogeological conditions, and observes different aquifers. The data was analyzed by integrating hydrology, mining activity, and meteorological data. Analytic hierarchy process was adopted to reveal the underground disturbance influencing factors, and suggestions for groundwater protection were given. The results show that the dynamic variations of the groundwater with time can be divided into 3 types (precipitation affected, unaffected type, and mining affected types). The drainage operation during the open-pit mining triggered the drop of groundwater level, resulting in a cone of depression. The maximum drawdown of the central groundwater level was 60 m. And the disturbance mainly occurred in the mining area where the maximum groundwater disturbance radius was 8 km. Artificial drainage was the main disturbance factor of groundwater, and the range of groundwater level drop within the disturbance radius was closely related to the distance from the open-pit coal mine and the drainage volume. The closer the distance to the mines and the higher the drainage volume led to the deeper the groundwater level drop. This study shows the importance of the three-dimensional observation network of groundwater and provides a good reference for groundwater resources protection and ecological restoration in open-pit coal mines.

Time-lapse seismic difference refers to the comprehensive response of fluid saturation, pore pressure, and porosity. However, the contribution of different parameters to the seismic response is difficult to distinguish. The high-precision prediction of these reservoir parameters is of great significance in CO2 geologic storage and oil and gas development. Therefore, a simultaneous time-lapse reservoir multiparameter prediction method based on a multitask learning network is proposed. Combined with CO2 geologic storage monitoring, the process of generating training data is described, involving numerical simulation, petrophysical models, and seismic forward modeling. Moreover, the Hertz-Mindlin formula, which considers pressure changes, is used to establish the relationship between formation elasticity and physical parameters in CO2 storage. The effects of fluid saturation, pressure, and porosity on P- and S-wave velocities and densities are analyzed, and the amplitude-variation-with-offset response characteristics of fluid saturation, pressure, and porosity changes are discussed. In total, 4700 and 300 sets of reservoir parameters and seismic angle gather data are used for network training and testing, respectively. The prediction results of synthetic and field data find that the time-lapse reservoir multiparameter prediction method based on multitask learning can effectively distinguish changes in each parameter and simultaneously obtain high-precision prediction results of fluid saturation, pressure, and porosity. Once the network is constructed, the prediction will take only a few seconds, which will promote the further development of the CO2 geologic storage theory and technology.

The frequency band limitation of seismic data limits the resolution of full-waveform inversion (FWI) results. In addition, the high computational cost seriously affects the practical application of FWI. To alleviate these concerns, an FWI method based on superresolution (SR-FWI) is developed to improve the prediction efficiency and accuracy of the reservoir parameters. A channel attention mechanism is introduced for the multifrequency characteristics of the model images. A constrained residual channel attention network (CRCAN) is built for superresolution (SR) by adding structural constraints to the loss function of a deep learning network. A total of 65,000 sets of geologic models and natural images constitute the network training data, 90% of which are used for training with the rest used for testing. The iterative calculation for FWI is time-consuming; hence, SR is applied to the iterative process to reduce the number of iterations and accelerate the model update. Low-resolution images along with the synthetic and field data are used for the evaluation of the CRCAN and SR-FWI algorithms, respectively. The test results find that CRCAN can effectively improve the image resolution, whereas SR-FWI is beneficial due to its high efficiency and precision, especially in predicting the stratum edge and small-scale anomalies. Therefore, SR-FWI is a powerful means of reservoir static and dynamic detection and can provide high-resolution information for projects, such as resource development and CO2 storage.