The classical pre-stack seismic inversion technique uses the Zoeppritz equation and its simplified versions to calculate the PP and PS reflection coefficients at different incidence angles, aiding in inverting the subsurface velocity and density parameters. Despite its widespread application, the amplitude variation with angle (AVA) inversion based on the Zoeppritz equation has limitations regarding the accuracy. The AVA neglects transmission losses and the effects of multiple reflections during seismic wave propagation, resulting in reduced resolution. In contrast, the propagation matrix theory offers a comprehensive range of reflection coefficients for P- and S-waves in multilayered media at arbitrary incidence angles, thereby theoretically enhancing the inversion accuracy. However, the seismic responses obtained using this method exist in the slowness–frequency domain and require constant slowness for consistency along a profile. This assumption is violated when variations in the P-wave velocity occur within the subsurface, affecting the incidence angle of propagating seismic waves. This study modifies the propagation matrix theory to compute AVA seismic responses and applies it to pre-stack multiparameter inversion. The effectiveness of the modified method was validated by deriving theoretical AVA seismic responses and comparing them to solutions from a typical layered media model. The modified theory was also employed for seismic pre-stack inversion. Numerical simulations and field data tests demonstrated that the new propagation matrix method offers a high accuracy and stability.

An important way to improve the resolution of electromagnetic exploration is by using known seismic and logging data. Based on previous work, 2D magnetotelluric (MT) parallel-constrained-inversion, based on an artificial-fish-swarm algorithm is further developed. The finite-difference (FD) method with paralleling frequency is used for 2D MT-forward-modeling, to improve computational efficiency. The results of the FD and finite-element (FE) methods show that the accuracy of FD is comparable to FE in the case of suitable mesh-generation; however, the calculation speed is ten times faster than that of the FE. The artificial-fish-swarm algorithm is introduced and applied to parallel-constrained-inversion of 2D MT data. The results of the synthetic-model test show that the artificial-fish-swarm-inversion based on paralleling forward can recover the model well and effectively improve the inversion speed. The processing and interpretation results of the field data are verified by drilling, which shows that the proposed inversion-method has good practicability.

Magnetotelluric (MT) data inversion reconstructs the subsurface resistivity structure using measured natural electromagnetic (EM) fields. Due to the attenuation of low-frequency EM waves in geologic conductors, MT inversion has lower sensitivity and resolution for subsurface structures compared with seismic reflection imaging. Seismic reflection provides rich, detailed subsurface information that complements EM data, such as the spatial locations of geologic boundaries and fine-scale geophysical attributes. This work develops a super-resolution MT inversion approach incorporating the seismic texture constraint. We develop a multiresolution model parameterization scheme to represent the unknown model with the coarse-scale resistivity background and fine-scale resistivity details. A modified, pretrained VGG19 neural network (NN) serves as an effective texture extraction operator. This inversion method requires no NN training and simultaneously optimizes MT data misfit, texture discrepancy, and various regularization penalties using the adaptive moment estimation optimizer. Experiments show that our inversion can reduce or at least maintain the data misfit and effectively improve MT inversion resolution to a seismic-like level. This approach has promising application potential in the oil industry for highlighting quasi-layered subsurface structures.

This study addresses the limitations of conventional evaluation methods caused by low porosity, strong heterogeneity, and complex pore structures in tight sandstone reservoirs. Through integrated rock physics experiments and multi-physical field modeling, the research systematically investigates the coupled response mechanisms between electrical and elastic parameters. The experimental approach includes pore structure characterization, quantitative mineral composition analysis, resistivity and polarizability measurements under various saturation conditions, P- and S-wave velocity testing, and scanning electron microscopy (SEM) imaging. The key findings show that increasing porosity leads to significant reductions in resistivity and elastic wave velocities, while also increasing surface conductivity. Specifically, clay minerals enhance surface conductivity through interfacial polarization effects and decrease rock stiffness, which exacerbates wave velocity attenuation. Furthermore, resistivity exhibits a nonlinear negative correlation with water saturation, with sharp increases at low saturation levels due to the disruption of conductive pathways. By integrating the Modified Generalized Effective Medium Theory of Induced Polarization (MGEMTIP) and Kuster–Toksöz models, this study establishes quantitative relationships between porosity, saturation, and electrical/elastic parameters, and constructs cross-plot templates that correlate elastic wave velocities with resistivity and surface conductivity. These analyses reveal that high-porosity, high-saturation zones are characterized by lower resistivity and wave velocities, coupled with significantly higher surface conductivity. The proposed methodology significantly improves the accuracy of reservoir evaluation and enhances fluid identification capabilities, providing a solid theoretical foundation for the efficient exploration and development of tight sandstone reservoirs.

In the complex piedmont area, the quality of seismic data is generally poor, and it is difficult to build the shallow velocity model, which leads to the inaccurate determination of the stratigraphic and structural positions. The combination of seismic and non-seismic exploration is an effective way for oil and gas exploration in complex piedmont belt. In the northwest of Sichuan Province, the joint exploration of three-dimensional audio-magnetotelluric (AMT) and transient electromagnetic (TEM) is carried out. The transient electromagnetic data is used as static correction for audio-magnetotelluric data, and the AMT data is used to detect the shallow surface structure within 1.5km buried depth. The three-dimensional geoelectric model of the study area is obtained by using AMT three-dimensional inversion, and the geological structure interpretation is carried out by combining well logging with the statistical data of rock physical properties, which provides a basis for seismic building the shallow surface model.

GeoGME is a software that can run on Linux and Windows operating systems and has the function of processing and interpreting the whole process of gravity, magnetic and electromagnetic data from field data to final results. In terms of preprocessing of magnetotelluric (MT) data, GeoGME provides several single point denoising methods, in which spatial filtering can be used to achieve good results. For static correction, the influence of static displacement can be suppressed by using man-machine interaction and 2D filtering static correction technique. In terms of inversion, the software also has a variety of inversion processing modules of 1D and 2D. The 2D regularization inversion method is used to process the field MT data from ES basin, and the inversion results show that the electrical characteristics of the shallow layer in resistivity profile are in good agreement with the known electrical logging curves. According to the electrical characteristics of petroliferous formation, the potential target oil and gas enrichment area is preliminarily inferred. The results of test line show that using GeoGME software in shallow target exploration can achieve good geological result.