Geophysical Prospecting最新文献

Over the past decades, surface wave methods have been routinely employed to retrieve the physical characteristics of the first tens of meters of the subsurface, particularly the shear wave velocity profiles. Traditional methods rely on the application of the multichannel analysis of surface waves to invert the fundamental and higher modes of Rayleigh waves. However, the limitations affecting this approach, such as the 1D model assumption and the high degree of subjectivity when extracting the dispersion curve, motivate us to apply the elastic full-waveform inversion, which, despite its higher computational cost, enables leveraging the complete information embedded in the recorded seismograms. Standard approaches solve the full-waveform inversion using gradient-based algorithms minimizing an error function, commonly measuring the misfit between observed and predicted waveforms. However, these deterministic approaches lack proper uncertainty quantification and are susceptible to get trapped in some local minima of the error function. An alternative lies in a probabilistic framework, but, in this case, we need to deal with the huge computational effort characterizing the Bayesian approach when applied to non-linear problems associated with expensive forward modelling and large model spaces. In this work, we present a gradient-based Markov chain Monte Carlo full-waveform inversion where we accelerate the sampling of the posterior distribution by compressing data and model spaces through the discrete cosine transform. Additionally, a proposal is defined as a local, Gaussian approximation of the target density, constructed using the local Hessian and gradient information of the log posterior. We first validate our method through a synthetic test where the velocity model features lateral and vertical velocity variations. Then we invert a real dataset from the InterPACIFIC project. The obtained results prove the efficiency of our proposed algorithm, which demonstrates to be robust against cycle-skipping issues and able to provide reasonable uncertainty evaluations with an affordable computational cost.

We apply a machine learning approach to automatically infer two key attributes – the location of fault or shear zone structures and the thickness of the overburden – in an 18 km² study area within and surrounding the Archean Fenelon gold deposit in Quebec, Canada. Our approach involves the inversion of carefully curated borehole lithological and structural observations truncated at 480 m below the surface, combined with magnetic and Light Detection and Ranging survey data. We take a computationally low-cost approach in which no underlying model for geological consistency is imposed. We investigated three contrasting approaches: (1) an inferred fault model, in which the borehole observations represent a direct evaluation of the presence of fault or shear zones; (2) an inferred overburden model, using borehole observations on the overburden-bedrock contact; (3) a model with three classes – overburden, faulted bedrock and unfaulted bedrock, which combines aspects of (1) and (2). In every case, we applied all 32 standard machine learning algorithms. We found that Bagged Trees, fine K-nearest neighbours and weighted K-nearest neighbour were the most successful, producing similar accuracy, sensitivity and specificity metrics. The Bagged Trees algorithm predicted fault locations with approximately 80% accuracy, 70% sensitivity and 73% specificity. Overburden thickness was predicted with 99% accuracy, 77% sensitivity and 93% specificity. Qualitatively, fault location predictions compared well to independently construct geological interpretations. Similar methods might be applicable in other areas with good borehole coverage, providing that criteria used in borehole logging are closely followed in devising classifications for the machine learning training set and might be usefully supplemented with a variety of geophysical survey data types.

By directly solving the full two-way wave equation, reverse time migration has superiority over other imaging algorithms in handling steeply dipping structures and other complicated geological models. Moreover, by incorporating the asymptotic inversion operator into reverse time migration imaging condition, the imaging algorithm is able to give a quantitative estimation of parameter perturbation in high-frequency approximation sense. However, because conventional asymptotic inversion only accounts for geometrical spreading, uneven illumination due to irregular acquisition geometry and inhomogeneous subsurface at each image point is neglected. The omit of illumination compensation significantly affects the imaging quality. Wave-equation-based illumination compensation methods have been extensively studied in the past. However, the traditional wave-equation-based illumination compensation methods usually require high computational cost and huge storage. In this paper, we propose an efficient wave-equation-based illumination compensation method. Under high-frequency approximation, we first define a Jacobian determinant to measure the regularity of subsurface illumination, and then illumination compensation operators are proposed based on the Jacobian. Through boundary integration, we further express the illumination compensation operators through extrapolated wavefields; the explicit computation of asymptotic Green's functions is thus avoided, and an efficient illumination compensation implementation for reverse time migration is achieved. Numerical results with both synthetic and field data validate the effectiveness and efficiency of the presented method.

Crystalline rocks in the subsurface are of interest for geothermal energy extraction, nuclear waste storage, and, when weathered or fractured, as aquifers. Compliant discontinuities such as microcracks, cracks and fractures may nucleate and propagate due to changes in pore pressure, stress and temperature. These discontinuities may provide flow pathways for fluids and, if fracturing extends to surrounding rocks, may allow escape of fluids to neighbouring formations. Monitoring such rocks using sonic logs, passive seismic, borehole seismic and surface seismic requires understanding of the propagation of elastic waves in the presence of such discontinuities. These may have an anisotropic orientation distribution as in situ stress may be anisotropic. As crystalline rock may display intrinsic anisotropy due to foliation and the preferential orientation of anisotropic minerals, quantification of the relative importance of intrinsic and microcrack-induced anisotropy is important. This may be achieved based on the stress sensitivity of elastic wave velocities. A method that allows both the orientation distribution of microcracks and the stress dependence of their normal and shear compliance to be estimated independently of the elastic anisotropy of the background rock is presented. Results are given for anisotropic samples of gneiss from Bukov in the Czech Republic and granite from Grimsel in Switzerland based on the ultrasonic velocity measurements of Aminzadeh et al. The microcrack orientation distribution is approximately transversely isotropic for both samples with a preferred orientation of microcrack normals perpendicular to foliation. This preferred alignment is stronger in the sample of gneiss than in the granite sample, and the normal and shear compliance of the microcracks decreases with increasing compressive stress. This occurs because the contact between opposing faces of the discontinuities grows with increasing compressive stress, and this results in a decrease in elastic anisotropy with increasing compressive stress. At low stress, the ratio of microcrack normal compliance to shear compliance is approximately 0.25 for the granite sample and 0.7 for the sample of gneiss. The normal compliance Z_N for both samples decreases faster with increasing compressive stress than the shear compliance Z_T, resulting in a decrease in Z_N/Z_T with increasing compressive stress.

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.

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.