The Zoeppritz equations describe the relationship between seismic properties that are useful for the interpretation of lithology and fluid properties. Various approximations to the Zoeppritz equations are often used in linear amplitude variation with offset and amplitude variation with angle inversions, assuming low-contrast boundaries. These approximations restrict the inversion method to not-too-large-angle cases and reduce the inversion accuracy. Therefore, it is necessary to develop joint PP and PS prestack inversion methods using the exact Zoeppritz equations. We have developed a method for nonlinear joint prestack inversion using the exact Zoeppritz equations. We established an objective function to combine PP and PS information based on a least-squares approach, and we used the Taylor expansion method to derive a model updating formula for the inversion. We also used a mean shift method to improve the accuracy of inversion results. We validated our inversion using synthetic and field seismic data. The model for calculating synthetic data contained high-contrast interfaces to match the reservoir layers, which included, for example, coal seams and unconsolidated sandstone. The outputs of the inversion were the elastic parameters (P- and S-wave velocities, as well as density), rather than the changes in elastic parameters. We found that the nonlinear joint prestack inversion achieved more accurate results than the linear joint prestack inversion, regardless of incidence angle size. Furthermore, if the prestack data were of high enough quality, it was possible to identify thin layers from the inversion result.

With the increase in exploration target complexity, more parameters are required to describe subsurface properties, particularly for finely stratified reservoirs with vertical transverse isotropic (VTI) features. We have developed an anisotropic amplitude variation with offset (AVO) inversion method using joint PP and PS seismic data for VTI media. Dealing with local minimum solutions is critical when using anisotropic AVO inversion because more parameters are expected to be derived. To enhance the inversion results, we adopt a hierarchical inversion strategy to solve the local minimum solution problem in the Gauss-Newton method. We perform the isotropic and anisotropic AVO inversions in two stages; however, we only use the inversion results from the first stage to form search windows for constraining the inversion in the second stage. To improve the efficiency of our method, we built stop conditions using Euclidean distance similarities to control iteration of the anisotropic AVO inversion in noisy situations. In addition, we evaluate a time-aligned amplitude variation with angle gather generation approach for our anisotropic AVO inversion using anisotropic prestack time migration. We test the proposed method on synthetic data in ideal and noisy situations, and find that the anisotropic AVO inversion method yields reasonable inversion results. Moreover, we apply our method to field data to show that it can be used to successfully identify complex lithologic and fluid information regarding fine layers in reservoirs.

With the increasing mining depth of coal mines, geologic hazards have become the main source of accidents. For this reason, coal seam prediction requires more assessment parameters, such as the degree of anisotropy, VP/VS value, and elastic modulus. These parameters are difficult to determine using only the PP-wave. We have inverted and interpreted multicomponent seismic data acquired from a 2.56 km2 area in the Guqiao mine located on the southern margin of the North China plate, under the constraints of drill, log, and rock-physics test data. The production coal seam 11-2 of the mine, located in the Permian Formation, was our focus of this study, and our objective was to determine the structure, fracture development, thickness, surrounding rock lithology, roof stability, and mining safety of the seam. To achieve this, we first performed an analysis of S-wave splitting and joint PP- and PS-wave inversions. The inversion results were then combined to derive additional parameters, such as the VP/VS value and dynamic Young’s modulus. Finally, we established a safety coefficient for assessing the safety of coal mining. The coefficient was based on the degree of anisotropy, coal bed thickness, Young’s modulus, and VP/VS value of the coal-bearing strata. The geologic data for two mining tunnels in the 11-2 coal bed, provided by Huainan Coal Mining Group, were used to verify the known structure and lithology predictions. In addition, the known structural interpretations based on the PP- and PS-wave sections were obviously superior to the results of a previous survey based on the PP-wave only. The predicted thickness of coal seam 11-2 was accurate, as confirmed by comparison with that determined from drill data. Our joint PP- and PS-wave inversion and interpretation provides more information for coal seam prediction, creating a new application for coal seismic survey.

Magnetization switching induced by spin–orbit torque is of fundamental interest for developing spintronic devices with low-power consumption and nonvolatility. Here, we report on the spin–orbit torque induced magnetization switching behavior of (001) oriented tetragonal Heusler alloy D0₂₂-Mn₃Ga films with an intrinsic ferrimagnetic spin structure grown on the GaAs(001) substrate by molecular-beam epitaxy. The out-of-plane hysteresis loop and anomalous Hall effect demonstrated a large perpendicular magnetic anisotropy and low saturation magnetization of D0₂₂-Mn₃Ga thin films. The spin–orbit torque induced magnetization switching has been realized in D0₂₂-Mn₃Ga/Pt heterostructure based Hall devices under an in-plane external field. It is found that the critical switching current density J_c is much smaller than that of the L1₀-MnGa/heavy metal system. Besides, both a dampinglike effective field H_DL and a fieldlike effective field H_FL are quantified by performing harmonic Hall voltage measurements. All these results indicate that ferrimagnetic D0₂₂-Mn₃Ga can be a promising candidate material for realizing high-density and energy-efficient spintronic devices.

Tectonically-deformed coal (TDC) is a potential source of threats to coal-mining safety. Finding out the development and distribution of TDCs is a difficult task in coalfield seismic explorations. Based on the previous investigations, the P- to S-wave velocity ratio (α/β) is a stable parameter for the identification of TDCs and most TDCs have α/β values of less than 1.7. Here, a TDC detection method using a model-based joint inversion of the multi-component seismic data is proposed. Following the least square theories, the amplitude variation with offset gathers of the PP- and PS-waves are jointly inverted into the corresponding α/β values. The prior models generated from the P- and S-wave velocity and density logs are employed in the joint inversion to enhance the inversed models. Model test results show that the model-based inversion is of high anti-noise ability and has a good recognition ability of TDCs. The proposed method is applied to a work area of the Guqiao mine in China. The TDCs developed in coal seam 13-1 are effectively identified according to their inverted α/β values of less than 1.7. The detection result is verified by the well and tunnel excavation information.

Separation of downgoing and upgoing P-waves and S-waves in an offset 2D/3D vertical seismic profile (VSP) record is a basic step in VSP data processing. Radon transform and median filtering methods are commonly used to separate wavefields. However, with the former, it is hard to strike a balance between the resolution and amplitude preservation, while the latter ignores the variation in the slowness (apparent velocity) of events. In order to solve these problems, we propose a method based on a modified linear Radon transform and dynamic time shifting. We derive the slowness spectrum of an input VSP record through the modified Radon transform. Based on the optimal slope values of the events derived from the slowness spectrum, we align the wavefields by dynamic time shifting. Then, the target wavefields are separated using a median filtering constrained by the linear Radon transform. Our wavefield separation process is iterated until the four kinds of wavefields are separated according to a priority ranking based on the order of the energy spectrum from strong to weak. The results were found to be better than those derived from wave separation using the traditional median filter or those derived using the high-resolution Radon transform method.