Geophysical Prospecting最新文献

The seismoelectric effect is an electrokinetic phenomenon that arises when seismic waves propagate in water-containing geological formations. Given that seismoelectric signals are sensitive to the hydraulic properties of the probed porous medium, they have the capability to provide important information during subsurface characterization efforts. In this work, we present a physics-based model for the dynamic streaming potential coupling coefficient (SPCC) in partially saturated porous media. For this, we conceptualize the porous medium as a partially saturated bundle of capillary tubes. We take into account the variation of pore size to relate the capillary pressure to the water saturation in the porous medium of interest. We then up-scale the streaming current and conduction current within the saturated capillaries under oscillatory flow conditions from pore to sample scale. The results show that the dynamic SPCC is not only a function of water saturation and the probing frequencies but also of the properties of water, mineral–water interfaces and other microstructural parameters of the porous medium. We analyse and explain the characteristics of the dynamic SPCC for two different pore size distributions (PSD): fractal and lognormal. Results show that the PSD characteristics have a strong effect on the dynamic SPCC responses. The proposed model has a remarkable ability to replicate experimental data available in the literature. In addition, it is observed that the lognormal distribution can provide a better agreement with experimental data for sand samples, which display a relatively narrow PSD. The findings of this study provide a valuable basis for interpreting seismoelectric signals under partially saturated conditions. Our proposed technique can be applied to any PSD, regardless of the complexity, providing a flexibility that is not present in alternative models found in the literature.

A comprehensive deep learning approach was introduced, encompassing data denoising, inversion imaging and uncertainty analysis. For denoising transient electromagnetic (TEM) data, we utilized a Bidirectional Long Short-Term Memory (BiLSTM) network. In the data inversion process, a combination of convolutional neural network (CNN) and BiLSTM structures was employed, and their outputs were consolidated using a multi-head attention mechanism. To ensure robust performance under challenging noise conditions, we implemented a specialized multi-channel noise training protocol during model optimization. The framework incorporates Monte Carlo (MC) dropout techniques to systematically evaluate prediction reliability throughout the inversion pipeline. This approach has not only been validated on test datasets but has also been successfully applied to the field dataset collected at the Narenbaolige Coalfield in Inner Mongolia, China. The deep learning inversion results obtained from both raw and denoised data exhibit reduced vertical continuity and increased roughness characteristics. In contrast, the Occam's inversion method with smoothness constraints yields results demonstrating superior lateral continuity and vertical smoothness. It is noteworthy that both inversion approaches show consistent interpretations regarding the scale of basalt formations and their contact interfaces with underlying sedimentary layers. Further uncertainty analysis reveals relatively higher uncertainty characteristics in the transition zones between basalt and sedimentary layers, as well as in deeper formations. The elevated uncertainty at interface regions may be attributed to model resolution limitations and inversion ill-posedness issues, whereas the higher uncertainty in deeper formations is more likely caused by the volumetric effects of electromagnetic field detection and the influence of observational data noise.

Accurate simulation of seismic waves is essential for achieving high-precision full-waveform inversion (FWI). Within the Cartesian coordinate system-based frequency-domain finite-difference (FDFD) framework, we propose a one-direction composition average-derivative optimal method for the 3D heterogeneous isotropic elastic-wave equation, referred to as the 45-point scheme. The results of dispersion analysis and weighted coefficient optimization demonstrate that the 45-point scheme achieves higher dispersion accuracy than the existing 27-point average-derivative scheme. More importantly, by constructing the impedance matrix along the ‘composition’ direction, the bandwidth of the sparse impedance matrix increases only slightly, with nonzero elements compactly distributed in strips. On the basis of the multifrontal massively parallel sparse direct solver (MUMPS) on a supercomputer platform, the 45-point scheme does not significantly increase computational complexity compared to the 27-point scheme. To further test the performance of the 45-point scheme, we provide several numerical experiments, including simple homogeneous and complex SEG/EAGE overthrust models. In comparison with the 27-point scheme, the 45-point scheme yields a notable improvement in computational accuracy, particularly for large grid ratios, while imposing only a modest increase in computational cost. These findings thus strongly suggest that the 45-point scheme holds promise as a viable option for the forward part of frequency-domain FWI in practical high-accuracy seismic imaging applications.

CO₂ geological sequestration (CGS) is a crucial strategy to mitigate the greenhouse effect. The quantitative correspondence between CO₂ saturation and acoustic response serves as the essential basis for monitoring CO₂ migration. However, due to dynamic fluid interactions between supercritical CO₂ and brine/oil in porous media, acoustic propagation behaviour is extremely complicated, even at the same saturation during drainage and imbibition processes. This study is motivated to evaluate the acoustic characteristics of the above porous stratum containing CO₂. To do so, pore fluid parameter models specific to CGS are consolidated and refined, with the consideration of CO₂ solubility. Meanwhile, Lo's theory is modified to describe both partial flow and global flow in CO₂-saturated porous media, capturing key mechanisms of patchy distribution and alterations in capillary pressure and relative permeability during drainage and imbibition. By combining these procedures, the wave propagation characteristics within CGS scenarios are systematically analysed. It is shown that CO₂ exhibits higher solubility than gases, leading to a distinct two-stage acoustic response, corresponding to its dissolved and free states. Relative permeability affects both compressional and shear waves, whereas capillary pressure and patchy distribution mainly affect compressional wave propagation. Notably, compressional waves exhibit heightened sensitivity to free CO₂ content and fluid flow dynamics, especially at ultrasound frequencies. The modified acoustic propagation theory demonstrates superior performance in characterizing compressional velocities during both drainage and imbibition. These findings highlight the dynamic fluid flow effects in CGS, providing a theoretical framework for analysing acoustic propagation characteristics.

Absolute impedance estimation is crucial for quantitative interpretation of petrophysical parameters such as porosity and lithology, from band-limited seismic data. The missing low-frequency part of the conventional seismic data leads to non-uniqueness in the solution and causes a hindrance to the absolute impedance estimation. This work presents an application of seismic envelope to retrieve absolute acoustic impedance (AI) values directly from band-limited data in an innovative workflow based on a deep sequential convolutional neural network (DSCNN). Along with the band-limited data and seismic envelope, we also incorporate the instantaneous phase information (to compensate for the lost phase information in a seismic envelope) as an auxiliary input into the DSCNN model to map the band-limited data into broadband data and then to retrieve absolute AI values. We have tested the proposed workflow on two synthetic benchmark datasets of Marmousi2 and SEAM 2D subsalt Earth model, as well as one field dataset of the F3 block, the Netherlands. Our results underline that the proposed approach is efficient in recovering the deeper features quite well as compared to the conventional approach, wherein only seismic band-limited data are used as input. Numerical tests show that the estimated low-frequency impedance is recovered well with our proposed seismic envelope-driven approach. Thus, the proposed workflow provides a robust solution for broadband impedance inversion by utilizing only one regression-based unified deep learning (DL) model. This work primarily highlights the potential of seismic envelope to greatly improve the estimation of low-frequency components of subsurface impedance model in a DL framework. Such a workflow for absolute impedance inversion from band-limited seismic will play an important role in reservoir characterization and in quantifying the elastic attributes.

The data recorded by the seismometer on the InSight are contaminated by interference signals called ‘glitches’, which have a specific duration and waveform. These glitches emerge very frequently with large amplitude differences and affect the subsequent processing of the data. Traditional methods for glitches removal rely on the threshold and cannot perfectly detect non-standard and composite glitches. We propose a deep learning based method for glitch detection and removal. A detection model based on the PhaseNet network is developed for three-component data. The ConvTasNet from the field of speech signal separation is introduced into the noise removal model to separate the glitches from the single-component data. The advantages of deep learning include the ability to autonomously extract features from the training set without requiring parameter adjustment and the ability to quickly process large amounts of data. The proposed method can detect and suppress non-standard glitches and provides a novel approach to removing them from Mars exploration records.