Geophysical Prospecting - Volume 72, Issue 9, 2024

The extension of the seismic bandwidth to lower frequencies enhances impedance contrasts that can be poorly represented by the broadband acquisition wavelet. Furthermore, long filters that are used to shape the wavelet of processed data can cause issues with noise, phase and interference between seismic events. In this paper, we use a mathematical technique known as mollification to resolve impedance variations with the highest detail allowed by the bandwidth of the data. The mollifier is integrated and windowed to match the low‐frequency content of the data to yield a convenient conversion to relative impedance. Synthetic data created from wedge models show that the windowed mollifier provides an improved representation of the impedance profile. This is replicated by application to an acoustic well log and a regular seismic dataset recorded in the Southern North Sea as well as a broadband dataset recorded in the North Sea.

Accurate characterization for effective elastic moduli of porous solids is crucial for better understanding their mechanical behaviour and wave propagation, which has found many applications in the fields of engineering, rock physics and exploration geophysics. We choose the spheroids with different aspect ratios to describe the various pore geometries in porous solids. The approximate equations for compressibility and shear compliance of spheroid pores and differential effective medium theory constrained by critical porosity are used to derive the asymptotic solutions for effective elastic moduli of the solids containing randomly oriented spheroids. The critical porosity in the new asymptotic solutions can be flexibly adjusted according to the elastic moduli – porosity relation of a real solid, thus extending the application of classic David‐Zimmerman model because it simply assumes the critical porosity is one. The asymptotic solutions are valid for the solids containing crack‐like oblate spheroids with aspect ratio < 0.3, nearly spherical pores (0.7 < < 1.3) and needle‐like prolate pores with  > 3, instead of just valid in the limiting cases, for example perfectly spherical pores (= 1) and infinite thin cracks (0). The modelling results also show that the accuracies of asymptotic solutions are weakly affected by the critical porosity and grain Poisson's ratio , although the elastic moduli have appreciable dependency of and . We then use the approximate equations for pore compressibility and shear compliance as inputs into the Mori–Tanaka and Kuster–Toksoz theories and compare their calculations to our results from differential effective medium theory. By comparing the published laboratory measurements with modelled results, we validate our asymptotic solutions for effective elastic moduli.

Time domain finite difference methods have been widely used for wave‐equation modelling in exploration geophysics over many decades. When using time domain finite difference methods, it is desirable to use a larger time step so as to save numerical simulation time. The Lax–Wendroff method is one of the well‐known methods to allow larger time step without increasing the time grid dispersion. However, the Lax–Wendroff method suffers from more time consumption because there are more spatial derivatives required to be approximated by the finite difference operators. We propose a new finite difference scheme for the Lax–Wendroff method so as to reduce the numerical simulation time. Then we determine the finite difference operator coefficients and analyse the dispersion error of the proposed finite difference scheme for the Lax–Wendroff method. At last, we apply the proposed finite difference scheme for the Lax–Wendroff method to different velocity models. The numerical simulation results indicate that the proposed finite difference scheme for the Lax–Wendroff method can effectively suppress time grid dispersion and is more efficient compared to the traditional finite difference scheme for the Lax–Wendroff method.

Time‐lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time‐lapse seismic exploration has been conducted using a large‐scale exploration system. However, for efficient monitoring of shallow subsurface, time‐lapse monitoring based on the small‐scale exploration system is required. Small‐scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal‐to‐noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small‐scale time‐lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre‐stack, post‐stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre‐stack data processing stage. In the post‐stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high‐resolution time‐lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.

Across various global regions abundant in oil and natural gas reserves, the presence of substantial sub‐surface salt deposits holds significant relevance. Accurate identification of salt domes becomes crucial for enterprises engaged in oil and gas exploration. Our research introduces a precise method for the automatic detection of salt domes, leveraging advanced deep learning architectures such as U‐net, transformers, artificial intelligence generative models and liquid state machines. In comparison with state‐of‐the‐art techniques, our model demonstrates superior performance, achieving a stable and validated intersection over the union metric, indicating high accuracy and robustness. Furthermore, the Dice similarity coefficient attaining underscores the model's proficiency in closely aligning with ground truth across diverse scenarios. This evaluation, conducted on 1000 seismic images, reveals that our proposed architecture is not only comparable but often surpasses existing segmentation models in effectiveness and reliability.

In seismic frequency‐domain finite‐difference modelling, the affine mixed‐grid method effectively eliminates the spatial sampling restriction associated with square meshes of the rotated mixed‐grid method. Nevertheless, the affine mixed‐grid method makes a weighted average of the entire elastic wave equations, resulting in reduced accuracy compared to the average‐derivative method in the case of rectangular meshes. It is worth noting, however, that the average‐derivative method is presently inapplicable to free‐surface scenarios, whereas the affine mixed‐grid method is applicable. By performing weighted averages of the derivative terms instead of the entire elastic wave equations in Cartesian and affine rotated coordinate systems, we have developed an improved affine mixed‐grid method for elastic‐wave frequency‐domain finite‐difference modelling. The proposed improved affine mixed‐grid method 9‐point scheme overcomes the drawback that the accuracy of affine mixed‐grid method is lower than that of average‐derivative method for unequal directional grid intervals. Moreover, the improved affine mixed‐grid method 6‐point scheme provides much higher numerical accuracy than the affine mixed‐grid method 6‐point scheme at either equal or unequal directional grid intervals. On the other hand, the proposed improved affine mixed‐grid method simplifies the coding complexity for implementing free‐surface condition in elastic‐wave frequency‐domain finite‐difference modelling by modifying the elastic parameters of the free‐surface layer and thus constructing the impedance matrix containing the free‐surface condition directly.

We present novel experimental measurements of acoustic velocity and attenuation in unconsolidated sand with water saturation within the sonic (well‐log analogue) frequency range of 1–20 kHz. The measurements were conducted on jacketed sand packs with 0.5‐m length and 0.069‐m diameter using a bespoke acoustic pulse tube (a water‐filled, stainless steel, thick‐walled tube) under 10 MPa of hydrostatic confining pressure and 0.1 MPa of atmospheric pore pressure. We assess the fluid distribution effect on our measurements through an effective medium rock physics model, using uniform and patchy saturation approaches. Our velocity and attenuation (Q⁻¹) are accurate to ±2.4% and ±5.8%, respectively, based on comparisons with a theoretical transmission coefficient model. Velocity decreases with increasing water saturation up to ∼75% and then increases up to the maximum saturation. The velocity profiles across all four samples show similar values with small differences observed around 70%–90% water saturation, then converging again at maximum saturation. In contrast, the attenuation increases at low saturation, followed by a slight decrease towards maximum saturation. Velocity increases with frequency across all samples, which contrasts with the complex frequency‐dependent pattern of attenuation. These results provide valuable insights into understanding elastic wave measurements over a broad frequency spectrum, particularly in the sonic range.

K‐feldspar, which constitutes about 60 of the Earth's crust, is crucial for understanding electrical conductivity in porous rocks. Its electrical properties are vital for applications in ceramics, electrical insulation and conductive polymers. In this work, we study the time evolution of electrical conductivity of K‐feldspar‐rich rocks with varying temperatures, at high and low pH, which has been studied through simulation using time domain random walk. Random walkers, mimicking ions in transport, move in accordance with appropriate hydrodynamic equations, dissolution and precipitation kinetics. Electrical conductivity has been calculated considering variations in the parameters of temperature, fluid pH and the abundance of K‐feldspar in rocks. Electrical conductivity is found to increase with temperature up to a critical value, after which it decreases. The sharpness of the rise and fall in electrical conductivity is quantified through a measure defined as the conductivity quality factor . We find that increases with a decrease in the availability of K‐feldspar mineral. Our simulated results of electrical conductivity show a good match with the experimental trends reported.

This article introduces an improved method for geological fault interpretation utilizing a 2D convolutional neural network approach with a focus on coalbed horizons, dealing with the problem as an image classification task. At the beginning, linear annotations, reflecting geological fault features, are applied to multiple seismic sections. By considering texture differences between fault and non‐fault areas, we construct samples that represent these distinct zones for training deep neural networks. Initially, fault annotations are transformed into single dots to facilitate pixel‐based processing. To depict a specific dot's geological structure, we employ a matrix clipped around the point, determined by a combination of range and step parameters. Convolutional layers generate filters equivalent to seismic data transformation, streamlining the need for analysis and selection of seismic attributes. The article discusses enhancing the efficiency of 2D convolutional neural network–based fault interpretation by optimizing sample selection, data extraction and model construction procedures. Through the incorporation of data from two mining areas (totalling 27.09 km²) in sample creation, the overall accuracy exceeds 0.99. Recognition extends seamlessly to unlabelled sections, showcasing the innovative technical route and methodology of fault interpretation with linear annotation and pixel‐based thinking. This study presents a method that integrates planar and raster thinking, transitioning from vision‐oriented geological structure annotation to algorithm‐oriented pixel location. The proposed 2D convolutional neural network–based matrix‐oriented fault/non‐fault binary classification demonstrates feasibility and reproducibility, offering a new automated approach for fault detection in coalbeds through convolutional neural network algorithms.

Due to the inherent unpredictability of geological conditions, tunnelling operations are often at risk of encountering water inrushes. Such incidents can lead to construction delays, impose financial strains and pose significant safety threats to the workers involved. Water‐bearing geological formations are the main triggers for such incidents, with factors such as the positioning, water quantity and permeability distribution of these formations being key to predicting the occurrence and severity of water inrush disasters. By leveraging the complex interplay among relaxation time, P‐wave velocity and permeability within the rock's physical properties, a series of indoor tests were conducted on 40 artificial reef limestone cores to extract the necessary parameters. Through the analysis of the data, the comprehensive permeability prediction model was established, and the correlation coefficient was 0.9420 between the model's predictions and actual measurements. At the same time, through theoretical and mechanism analysis, the relationship between permeability and relaxation time and the relationship between permeability and P‐wave velocity were analysed. Finally, 10 natural reef limestone samples were used to verify the accuracy of the model. The prediction model enables an accurate evaluation of tunnel permeability, thus providing a scientific basis for the mitigation of tunnel water inrush hazards.

Time‐lapse images carry out important information about dynamic changes in Earth's interior, which can be inferred using different full waveform inversion schemes. The estimation process is performed by manipulating more than one seismic dataset, associated with the baseline and monitors surveys. The time‐lapse variations can be so minute and localized that quantifying the uncertainties becomes fundamental to assessing the reliability of the results. The Bayesian formulation of the full waveform inversion problem naturally provides confidence levels in the solution, but evaluating the uncertainty of time‐lapse seismic inversion remains a challenge due to the ill‐posedness and high dimensionality of the problem. The Hamiltonian Monte Carlo can effectively sample over high‐dimensional distributions with affordable computational efforts. In this context, we explore the sequential approach in a Bayesian fashion for time‐lapse full waveform inversion using the Hamiltonian Monte Carlo method. The idea relies on integrating the baseline survey information as prior knowledge to the monitor estimation. We compare this methodology with a parallel scheme in perfect and a simple perturbed acquisition geometry scenario considering the Marmousi and a typical Brazilian pre‐salt velocity model. We also investigate the correlation effect between baseline and monitor samples on the propagated uncertainties. The results show that samples between different surveys are weakly correlated in the sequential case, while the parallel strategy provides time‐lapse images with lower dispersion. Our findings demonstrate that both methodologies are robust in providing uncertainties even in non‐repeatable scenarios.

Train noise is a kind of green, non‐destructive and strong‐energy artificial seismic sources, which is widely used in railway safety monitoring, near‐surface imaging and urban underground space exploration. Distributed acoustic sensing is a new seismic acquisition technology, which has the advantages of dense sampling, simple deployment and strong anti‐electromagnetic interference ability. In recent years, distributed acoustic sensing has been gradually applied in the fields of urban traffic microseism monitoring, crack detection and underground space imaging. However, previous studies mainly focused on microseism interferometry using train event coda noise, and there is limited research on the workflow of interferometry imaging using distributed acoustic sensing–based heavy train events noise (with short coda windows), which produces an abundant of near‐source interference. Aiming at proving the effectiveness of this idea, we investigated a process workflow to get underground shear‐velocity structure based on distributed acoustic sensing recorded heavy traffic noise near Qinhuangdao train station. A weighted sliding absolute average method is used to weaken the strong amplitude to the coda wave level and reduce the near‐source influence. We demonstrated that the cross‐coherence interferometry method, after spectral whitening, has the best effect on sidelobe suppression in the virtual source surface wave shot gathers, through a comparative analysis of cross‐correlation and cross‐coherence results. For obtaining concentrated energy and strong continuity in phase velocity spectra, we selected the time windows with high spatial coherence and signal‐to‐noise ratio not less than 1.2 for stacking from 720 time windows in F–K domain. When dividing subarrays to extract pseudo‐two‐dimensional profile, we set the overlap rate between adjacent time windows to 80% to increase stacking times, enhancing the precision of phase velocity spectra and reducing the errors of picking dispersion curve. Our results show that heavy traffic train events noise (non‐pure coda) can be used to detect underground velocity structure with clear dispersion and high inversion reliability. This research provides a new processing flow for distributed acoustic sensing train noise imaging and can be applied in future urban underground space exploration.

To address low detection accuracy and speed due to the multisolvability of the ground‐penetrating radar signal, we proposed a novel centralized feature pyramid‐YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra‐layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground‐penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three‐dimensional imaging. Our model is suitable for object detection in ground‐penetrating radar images, thereby providing technical support for road damage and underground pipeline detection.

Reflectivity inversion is a key step in reservoir prediction. Conventional sparse‐spike deconvolution assumes that the reflectivity (reflection coefficient series) is sparse and solves for the reflection coefficients by an L1‐norm inversion process. Spectral inversion is an alternative to sparse‐spike deconvolution, which is based on the odd–even decomposition algorithm and can accurately identify thin layers and reduce the wavelet tuning effect without using constraints from logging data, from horizon interpretations or from an initial model of the reflectivity. In seismic processing, an error exists in wavelet extraction because of complex geological structures, resulting in the low accuracy of deconvolution and inversion. Blind deconvolution is an effective method for solving the problem mentioned above, which comprises seismic wavelet and reflectivity sequence, assuming that the wavelets that affect some subsets of the seismic data are approximately the same. Therefore, we combined blind deconvolution with spectral inversion to propose blind spectral inversion. Given an initial wavelet, we can calculate the reflectivity based on spectral inversion and update the wavelet for the next iteration. During the update processing, we add the smoothness of the wavelet amplitude spectrum as a regularization term, thus reducing the wavelet oscillation in the time domain, increasing the similarity between inverted and initial wavelets, and improving the stability of the solution. The blind spectral inversion method inherits the wavelet robustness of blind deconvolution and high resolution of spectral inversion, which is suitable for reflectivity inversion. Applications to synthetic and field seismic datasets demonstrate that the blind spectral inversion method can accurately calculate the reflectivity even when there is an error in wavelet extraction.

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.

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.