Geophysical Prospecting最新文献

Accurate seismic models with anisotropy and attenuation characteristics are crucial to accurately imaging subsurface structures. However, the anisotropic viscoelastic equations are complex and require significant computational resources. In addition, the single-mode waves have been sufficient for most practical exploration needs. However, separating the qP- and qSV-waves in anisotropic viscoelastic wavefields is challenging. Thus, we propose a new method to approximate and efficiently separate the qP- and qSV-waves in attenuated transversely isotropic media. First, we obtain the decoupled approximate phase velocities of qP- and qSV-waves by a curve-fitting method. Consequently, based on the average and maximum relative error analysis, our approximate qP- and qSV-wave phase velocities are more accurate than the existing approximations. Additionally, our approximations have broader applicability, resulting in acceptable errors during their application. Second, based on the approximate qP- and qSV-wave phase velocities, we derive the corresponding qP- and qSV-wave equations for a complete decoupling of the qP- and qSV-wave components in transversely isotropic media. Third, to combine the attenuation and anisotropy characteristics, we incorporate the Kelvin–Voigt attenuation model and obtain the decoupled qP- and qSV-wave equations in attenuated transversely isotropic media. Then, we use an efficient and stable hybrid finite-difference and pseudo-spectral method to solve the new decoupled qP- and qSV-wave equations. Finally, several numerical examples demonstrate the separability and high accuracy of the proposed qP- and qSV-wave equations. We obtain a qP-wave wavefield entirely devoid of SV-wave artefacts. In addition, the decoupled approximate qP- and qSV-wave equations are accurate and stable in heterogeneous media with different velocities and attenuation. The decoupled, approximated qP-wave and qSV-wave equations proposed in this paper can effectively separate the qP-wave and qSV-wave components, resulting in fully decoupled qP- and qSV-wave wavefields in attenuated transversely isotropic media.

Surface geometry inversion of geophysical data has recently been introduced as an effective approach for generating surface-based geological models. The models obtained through surface geometry inversion clearly delineate the contacts between distinct rock units, making them easily interpretable by geologists. Surface geometry inversion has shown promising preliminary results in other works, but the practical application of surface geometry inversion on real geophysical data has not been thoroughly investigated. To move towards a better understanding of the practicalities involved, we applied surface geometry inversion to a real magnetic dataset acquired over two kimberlite pipes located in north-central Botswana. The objective was to assess the effectiveness and limitations of the surface geometry inversion approach in accurately characterizing the subsurface geometry and identifying the boundaries of the kimberlite pipes. We first perform an anomaly separation approach to isolate the magnetic anomalies associated with the kimberlite pipes. A surface geometry inversion algorithm was applied to the original and separated datasets using various initial models and other control parameters. Several tests were performed to investigate the effects that data processing, initial models, and other parameter choices have on the surface geometry inversion results. We successfully recover the geometry, extension and dip of the two kimberlite pipes. We discuss the results of our various tests and provide advice for practitioners interested in applying surface geometry inversion methods to their data. Our work indicates that surface geometry inversion can be used as a complementary approach to voxel inversion, and we propose an iterative surface geometry inversion algorithm as a possible alternative approach to voxel inversion for simple geological scenarios. This work provides valuable insights into the appropriate application of surface geometry inversion on real geophysical datasets.

Automatic first-break picking is a basic step in seismic data processing, so much so that the quality of the picking largely determines the effect of subsequent processing. To a certain extent, artificial intelligence technology has solved the shortcomings of traditional first-break picking algorithms, such as poor applicability and low efficiency. However, some problems still remain for seismic data, with a low signal-to-noise ratio and large first-break change leading to inaccurate picking and poor generalization of the network. In order to improve the accuracy of the automatic first-break picking results of the above seismic data, we propose a multi-view automatic first-break picking method driven by multi-network. First, we analysed the single-trace boundary characteristics and the two-dimensional boundary characteristics of the first break. Based on these two characteristics of the first break, we used the Long Short-Term Memory and the ResNet attention gate UNet (resudual attention gate UNet) networks to extract the characteristics of the first arrival and its location from the seismic data, respectively. Then, we introduced the idea of multi-network learning in the first-break picking work and designed a feature fusion network. Finally, the multi-view first-break features extracted by the Long Short-Term Memory and resudual attention gate UNet networks are fused, which effectively improves the picking accuracy. The results obtained after applying the method to field seismic data show that the accuracy of the first break detected by a feature fusion network is higher than that given by the above two networks alone and has good applicability and resistance to noise.

Understanding the physical properties of fault zones is essential for various subsurface applications, including carbon capture and geologic storage, geothermal energy and seismic hazard assessment. Although three-dimensional seismic reflection data can image the geometries of faults in the sub-surface, it does not provide any direct information on the physical properties of fault zones. We currently cannot use seismic reflection data to infer directly which faults may be leaking or sealing and are reliant instead on shale-gauge ratio type calculations, which are fraught with uncertainties. In this paper, we propose that full-waveform inversion P-wave velocity models can be used to extract information on fault zone acoustic properties directly, which may be a proxy for subsurface fault transmissibility. In this study, we use high-quality post-stack depth–migrated seismic reflection and full-waveform inversion velocity data to investigate the characteristics of fault zones in the Samson Dome in the SW Barents Sea. We analyse the variance attribute of the post-stack depth migrated and full-waveform inversion volumes, revealing linear features that consistently appear in both datasets. These features correspond to locations of rapid velocity changes and seismic trace distortions, which we interpret as faults. These observations demonstrate the capability of full-waveform inversion to recover fault zone velocity structures. Our findings also reveal the natural heterogeneity and complexity of fault zones, with varying P-wave velocity anomalies within the studied fault network and along individual faults. Our results indicate a correlation between P-wave velocity anomalies within fault zones and the modern-day stress orientation. Faults with high P-wave velocity are the ones that are perpendicular to the present-day maximum horizontal stress orientation and are likely under compression. Faults with lower P-wave velocity are the ones more parallel to the present-day maximum horizontal stress orientation and are likely in extension. We propose that these P-wave velocity anomalies may indicate differences in how ‘open’ and fluid filled the fault zones are (i.e. faults in extension are more open, more fluid filled and have lower V_P) and therefore may provide a promising proxy for fault transmissibility.

Quantification of data misfits and model structures is an important step in the non-linear iterative inverse scheme, allowing medium parameters to be iteratively refined through minimization. This study developed a new three-dimensional controlled-source electromagnetic inversion algorithm that allows general measures to be made selectively available for this evaluation. We adopt $�{\ell }_2$, $�{\ell }_1$, Huber, hybrid $�{\ell }_1$/$�{\ell }_2$, Sech, Cauchy, biweight and $�{\ell }_0$ norms as general measures. The inversion implementation is based on a regularized Gauss–Newton method, and non-quadratic measures are incorporated via the use of an iteratively reweighted least-squares scheme. To exploit current computing power, forward solutions are computed on an edge finite-element discretization using a parallel version of a direct sparse solver, while dense matrix operations in inversion are optimized using the LAPACK library. The behaviours of general measures for evaluating data misfits and model structures are examined in synthetic inversion experiments, focusing on elucidating weighting mechanisms and setting user-defined parameters. A preliminary demonstration is presented, showcasing simultaneous regularization in imaging a toy model containing both sharp and smooth property changes, alongside a field data application for imaging subsurface artificial structures. Our findings highlight the seamless integration of general measures, contributing to improved robustness against data outliers and enhanced spatial properties provided in output models.

Slope failure rates may be exacerbated by increased precipitation patterns associated with climate change. Such events are extremely disruptive for local communities affected. Rapid engineering remediation solutions generally require immediate site characterization, including information on depth to intact bedrock and groundwater conditions – often on dangerous or still-failing slopes. Horizontal-to-vertical spectral ratio is a low-impact technique capable of rapidly providing key information on the subsurface. Here we develop a robust workflow for constrained minimization of horizontal-to-vertical spectral ratio data and develop constrained horizontal-to-vertical spectral ratio profiling methodologies. We present results from a number of landslide sites in eastern Australia and demonstrate the utility of horizontal-to-vertical spectral ratio in delineating both fractured-rock aquifers at high-risk sites and colluvium–bedrock contact on active landslide sites, where traditional seismic methods were not practical.

The basin environment is a widely studied subject in both geology and geophysics for its economic significance in energy and mineral explorations. However, the estimation of the basement depth is often a challenging task given the complexity of the basement relief and lateral physical property change. Previous works simplify the problem by only inverting for the depth to the basement, and more recent studies have suggested the need to incorporate the variation of physical properties to improve basement structure imaging. In this study, we develop an inversion method with the associated workflow to simultaneously recover both the depth to a magnetic basement and a laterally varying magnetic susceptibility in the basement rock. To achieve this, we employ a set of constraints on the inverse problem. Particularly, both the recovered susceptibility and basement depth models are bounded below a possible maximum value, and the depth model is guided by a few depth points obtained from the resistivity models that are obtained from the one-dimensional blocky inversions of magnetotelluric (MT) data. In addition, we apply the fuzzy C-means (FCM) clustering to the susceptibility model during the inversion and use the inverted cluster centers to differentiate for different geological units in the basement. To show the effectiveness of our work, we compare the existing approaches and our method using two test inversions on one synthetic model resembling the basin–basement environment before demonstrating our method on a field data example with magnetic data collected by the U.S. Geological Survey (USGS) over the Illinois Basin. Our results show improved recovery in both basement relief and susceptibility in the basement rock, and inversion with field data is able to identify three different susceptibility zones in basement rock below the Illinois Basin.