Geophysical Prospecting - Volume 72, Issue 2, 2024

Microseismic monitoring is a promising method for the safety monitoring of underground mines. However, it is crucial to isolate microseismic signals related to the collapse of a mine from others for successful monitoring because the monitoring system records various signals. Recently, deep learning‐based classification techniques have achieved high performance in such data classification problems. In this context, we develop an automatic signal classification technique using the modified WaveNet classifier. The main characteristic of the WaveNet structure is its ability to extract features at various frequencies from very long time‐series data, and such an advantage makes the WaveNet suitable for seismic data processing. The data imbalance problem coming from the safe condition of the monitoring target is solved by augmenting the training data with those acquired from another mine and employing class weighting. After training, an optimal classifier is chosen considering the loss function, accuracy and Fβ score. The optimal classifier shows very high accuracy and excellent performance for the test data prediction. Compared to the random forest model and another one‐dimensional convolutional neural network–based network, the suggested classifier has higher reliability in predicting microseismic signals. Even though the proposed WaveNet model has a much more complex structure than the random forest model, the actual application examples demonstrate that the proposed model achieves high efficiency without any preprocessing. The automatic signal classifier developed in this study can be directly applied to various safety monitoring problems, not only mines, to improve the efficiency and reliability of monitoring systems.

In contrast to conventional acquisition practices, simultaneous source acquisition allows for overlapping wavefields to be recorded. Relaxing the shot schedule in this manner has certain advantages, such as allowing for faster acquisition and/or denser shot sampling. This flexibility usually comes at the cost of an extra step in the processing workflow, where the wavefields are deblended, that is, separated. An inversion‐type algorithm for deblending, based on the focal transform, is investigated. The focal transform uses an approximate velocity model to focus seismic data. The combination of focusing with sparsity constraints is used to suppress blending noise in the deblended wavefield.

The focal transform can be defined in different ways to better match the spatial sampling of different types of marine surveys. To avoid solving a large inverse problem, involving a large part of the survey simultaneously, the input data can be split into sub‐sets that are processed independently. We discuss the formation of such sub‐sets for ocean bottom node and streamer‐type acquisitions. Two deblending experiments are then carried out. The first is on numerically blended ocean bottom node field data. The second is on field‐blended towed streamer data with a challenging signal overlap. The latter experiment is repeated using curvelet‐based deblending for comparison purposes, showing the virtues of the focal deblending process. Several challenges of basing deblending around the focal transform are discussed as well as some suggestions for improved implementations.

Thanks to continued performance improvements in software and hardware, wave‐equation‐based imaging technologies, such as full‐waveform inversion and reverse‐time migration, are becoming more commonplace. However, widespread adaptation of these advanced imaging modalities has not yet materialized because current implementations are not able to reap the full benefits from accelerators, in particular those offered by memory‐scarce graphics processing units. Through the use of randomized trace estimation, we overcome the memory bottleneck of this type of hardware. At the cost of limited computational overhead and controllable incoherent errors in the gradient, the memory footprint of adjoint‐state methods is reduced drastically. Thanks to this relatively simple to implement memory reduction via an approximate imaging condition, we are able to benefit from graphics processing units without memory offloading. We demonstrate the performance of the proposed algorithm on acoustic two‐ and three‐dimensional full‐waveform inversion examples and on the formation of image gathers in transverse tilted isotropic media.

Steeply dipping structural imaging is a great challenge due to its poor illumination. Conventional migration methods are unable to produce an accurate image of complex steeply dipping structures. The prismatic wave can improve the illumination of steeply dipping structures and is often used to improve the imaging results of such structures. Traditional elastic wave theory assumes that seismic waves do not attenuate when propagating through subsurface media. However, during seismic wave propagation, the wave energy decays exponentially due to the absorption and attenuation of the ground layer. Subsurface attenuation leads to amplitude loss and phase distortion of seismic waves, resulting in blurring of migration amplitudes when this attenuation is not taken into account during imaging. To address this issue, a frequency‐domain Q‐compensated prismatic reverse time migration method is proposed, which derives Q‐compensated prismatic wavefield propagation operators. In the proposed frequency‐domain Q‐compensated prismatic reverse time migration, Q attenuation is fully compensated along three propagation paths and two propagation types of prismatic waves. The optimized four‐order mixed 25‐point difference format and LU decomposition method are used to solve the Q‐compensated prismatic wavefield propagation equations with high computational efficiency. Numerical and field data examples demonstrate that the proposed frequency‐domain Q‐compensated prismatic reverse time migration method can compensate for deep attenuation energy and improve the imaging resolution of steeply dipping structures.

The oscillatory bubble pulses generated by airguns in seawater are known to produce artefacts in seismic images. Although such artefacts are suppressible by employing larger airgun arrays in acquisition, small airgun arrays are used more often now to minimize the environmental impacts, thus raising the need for further suppressing bubble pulses at data processing stage. For a deep marine reflection dataset recently acquired by a small airgun array, we compare the effectiveness of three popular debubbling methods that estimate the far‐field source signature based on theoretical simulation and wavelets extracted from seafloor reflections and direct arrivals, respectively. In this case, due to the lack of near‐field measurements for calibration, the debubbling via simulation underperforms the two wavelet extraction methods. Overlapping events in the noisy response of seafloor sediments lead to the failure of the wavelet extraction from primary seafloor reflections. The estimated source signature based on direct arrivals achieves the best bubble suppression result, indicating the importance of signal‐to‐noise ratio and a low level of directionality of the small airgun array source.

The possibilities offered by the use of variational mode decomposition–based Hilbert marginal spectrum and the differential cepstrum for gas‐bearing detection are studied in this paper. We propose a novel variational mode decomposition–based Hilbert marginal differential cepstrum for hydrocarbon detection. Variational mode decomposition–based Hilbert marginal spectrum is first computed. Then discrete cosine transform is carried out to the differential logarithmic variational mode decomposition–based Hilbert marginal spectrum to obtain the variational mode decomposition–based Hilbert marginal differential cepstrum. For hydrocarbon detection, the seismic amplitude anomaly section is generated by extracting the first and second common quefrency sections. Compared with the traditional Fourier‐based cepstrum, the wavelet‐based cepstrum and the Berthil cepstrum, it has the ability to effectively reveal more detailed frequency‐dependent amplitude anomalies with high accuracy and resolution. Model tests and field data applications from a carbonate reservoir in China show that the variational mode decomposition‐based Hilbert marginal differential cepstrum can provide a better gas‐prone interpretation. The proposed method can be a complementary approach to current cepstrum‐based hydrocarbon detection methods and the spectrum decomposition methods.

In this paper, we present a frequency‐domain volume integral method to model the microseismic wavefield in heterogeneous anisotropic‐elastic media. The elastic wave equation is written as an integral equation of the Lippmann–Schwinger type, and the seismic source is represented as a general moment tensor. The actual medium is split into a background medium and a scattered medium. The background part of the displacement field is computed analytically, but the scattered part requires a numerical solution. The existing matrix‐based implementation of the integral equation is computationally inefficient to model the wavefield in three‐dimensional earth. An integral equation for the particle displacement is, hence, formulated in a matrix‐free manner through the application of the Fourier transform. The biconjugate gradient stabilized method is used to iteratively obtain the solution of this equation. The integral equation method is naturally target oriented, and it is not necessary to fully discretize the model. This is very helpful in the microseismic wavefield computation at receivers in the borehole in many cases; say, for example, we want to focus only on the fluid injection zone in the reservoir–overburden system and not on the whole subsurface region. Additionally, the integral equation system matrix has a low condition number. This provides us flexibility in the selection of the grid size, especially at low frequencies for given wave velocities. Considering all these factors, we apply the numerical scheme to three different models in order of increasing geological complexity. We obtain the elastic displacement fields corresponding to the different types of moment tensor sources, which prove the utility of this method in microseismic. The generated synthetic data are intended to be used in inversion for the microseismic source and model parameters.

Many geological features of scientific and/or economic interest have structural orientations that are neither horizontal nor vertical. Being able to recover such dipping structures from geophysical inversions is, therefore, important. Different regularization strategies have been proposed to help recover dipping structures. One notable example is total variation. However, there seems to be a lack of understanding within the geophysical community regarding why total variation regularization allows dipping structures to be recovered, whereas L1 norm regularization does not. In this paper, we compare these two regularization strategies from an optimization point of view using two simple block models. We also perform three‐dimensional inversions using a synthetic example and a field data example involving gravity gradient data to demonstrate the resolving power of total variation in potential field data inversion.

Data acquisition using equal spacing has been a standard practice in geophysics. The dense uniform sampling derived from Nyquist–Shannon sampling includes redundant samples, and it is sufficient but not necessary to adequately record target signals. We propose an ergodic sampling, which avoids the redundant samples in the dense uniform sampling and possesses the ability to capture the sufficiently similar information content as does Nyquist sampling. Ergodicity means that a key part of the system can represent the average performance of the entire system. Our ergodic samples are a critical subset of dense uniform samples and can represent the full uniform Nyquist samples. To find such a critical subset, we first examine the properties of different sampling patterns, including the sampling interval distribution, sampling angle distribution, areal sample density, and resolution in the spectral domain. We define the information sampling ability of sampling patterns based on these properties. The concept of information sampling ability that we have proposed serves as the criterion to compare different sampling patterns and assess their sampling performances. The sampling patterns with the same information sampling ability have the same capability to gather information, even though the appearance of sampling patterns may be different. We formulate an optimization problem to find this critical subset of sample locations, which has the fewest number of samples but has a similar information sampling ability as that of the desired dense uniform samples. This critical subset is irregularly located, has the optimized properties and forms the ergodic sampling pattern. We define this process of sampling design and associated understanding as the ergodic sampling. Ergodic sampling can be applied to gain two major benefits in practice. First, this approach can save a significant number of samples. We demonstrate ergodic sampling using one‐dimensional synthetic data and a two‐dimensional field geophysical dataset. The simulations confirm that, compared with other sampling strategies, ergodic sampling can use fewer samples to acquire the same amount of information, so that we can save cost. Alternatively, with the same budget, we can use the same number of samples through ergodic sampling to acquire more information. The new ergodic sampling can lead to a new generation of economic and efficient geophysical data acquisition, which could assist in increasing the discovery rate in resource exploration, tackling more earth science problems with a limited budget and can also benefit the environment in the process by reducing the invasiveness in potentially sensitive regions.

Multi‐parameter elastic full‐waveform inversion is a technique that utilizes both P‐ and S‐waves of observed seismic data to produce high‐resolution velocity and density models with accurate amplitude information by minimizing the discrepancy between the predicted and observed multi‐component data. However, due to the nonlinear nature of the multi‐parameter inverse problem, elastic full‐waveform inversion is prone to local minima and ‘cycle‐skipping’. To overcome these challenges, this paper proposes an elastic full‐waveform inversion method that incorporates a double‐cross‐shaped discrete flux‐corrected transport. This method additionally introduces diffusion fluxes in two diagonal directions, which helps to capture low‐frequency information in the observed seismic data and maintain forward modelling stability. Multi‐scale inversion is achieved by gradually decreasing the diffusion flux correction parameter. Numerical experiments on both two typical models and a field data example demonstrate the effectiveness of the proposed elastic full‐waveform inversion method based on the double‐cross‐shaped discrete flux‐corrected transport in generating high‐precision velocity and density models.

Considerably different from sandstones, sands have point‐like contact of grains. In contrast, sandstones have cementation between grains, which has diminished the very narrow gap at contact of grains. In this paper, ultrasonic S‐wave in brine‐saturated glass beads and Ottawa sands is used to detect the very narrow space at contact of grains. The data of the dry beads/sands and brine are inputted into Biot theory to yield phase velocity of the saturated beads/sands. By fitting the theoretical velocity with the ultrasonic measurement, phase velocity, the quality factor and S‐wave permeability are determined as functions of frequency. The predicted ultrasonic quality factors appear to be very close to that of water‐saturated Berea sandstone. Our previous study showed that for Berea sandstone, the low‐frequency S‐wave permeability is approximately half of Darcy permeability. However, for glass beads and Ottawa sands, the S‐wave permeabilities at low frequencies are one‐order magnitude lower than Darcy permeabilities. This well shows that S‐wave permeability of the beads/sands is associated with the very narrow gap at contact of grains which is successfully detected by ultrasonic S‐wave in the indirect way.

Geological structures have been shown by studies to have influence on the occurrence, storage and transportation of groundwater. Understanding the structural network of an area unearths a deep insight into the groundwater dynamics of the area. A geological structural analysis was carried out to reveal the geological structural network of Ghana's Voltaian basin. Using aeromagnetic data, structural density models were generated using the Center for Exploration Targeting grid analysis technique for two depth ranges (that is up to 100 m and 300 m) over the Voltaian basin. The total length of geological structures (lineaments) delineated at depths up to 100 m and 300 m were more than 5000 km and more than 8000 km, respectively. Given this, the study area was observed to be structurally dense at each of the aforementioned depths. The structural density models were discretized into five classes (very low, low, moderate, moderately high and very high regions), each of which was evaluated to determine their spatial association with known locations of groundwater occurrences within the study area using the frequency ratio technique. Frequency ratio results for both structural density models derived at 100 m and 300 m depths show the existence of a strong correlation between high structural density model classes and the known groundwater occurrences. The structural density models were further evaluated using the receiver operating characteristics curve. The area under the receiver operating characteristics curve scores indicates that, although both structural density models showed very good performance (with receiver operating characteristics scores greater than 0.7), the 300‐m depth structural density model performed better than the structural density model generated at a depth of 100 m (with their receiver operating characteristics scores being 0.721 and 0.715, respectively). The obtained results corroborate with literature assertion that groundwater occurrence within the Voltaian basin is mainly associated with structural features. It is expected that the outputs of this study would guide future groundwater exploration programmes within the study area.

We introduce a blended‐acquisition method: temporally signatured and/or modulated and spatially dispersed source array. The former, signatured and dispersed source array has much less constraints in the encoding with operational flexibility, allowing non‐uniform sampling and non‐patterned shooting both in the space and time dimension. The latter, modulated and dispersed source array allows straightforward deblending by filtering and physically separating frequency channels in the frequency domain. We demonstrate our method by synthesizing the blended acquisition followed by deblended‐data‐reconstruction processing in order to discuss the virtues. The examples show that this method could make the blended‐acquisition encoding and operations indeed simple and robust; the same is true for the deblending processing.

Processing land seismic data, especially vibroseis data, is often challenging due to complicated near‐surface situations and source‐generated noise. The case study deals with noisy vibroseis data acquired in the Jaisalmer Basin. The near‐surface estimation in this area is difficult due to a possible velocity reversal manifested in the shingled patterns of the first breaks. The near‐surface workflow incorporates a model‐adaptive first break‐picking approach, essentially integrating two problems of first‐break‐picking and model estimation into a single problem. The signal‐conditioning workflow is based on cascaded scaling and single‐channel‐based noise reduction to prevent the removal of weak signals. Horizon‐based migration velocity analysis was used to focus reflectors on the constant velocity‐migrated stacks. This was particularly useful in areas with dubious velocity trends based on semblance panels. The velocity volume has structural consistency, which provides a better time‐migrated image. The workflow also incorporates a targeted post‐stack processing sequence to enhance continuity, sharpen discontinuities and improve the resolution, as notable by comparing the legacy‐processing results of the same dataset.

In this study, we propose a frequency‐domain autocorrelation as an imaging condition for time‐reversal imaging. The previous imaging conditions in time‐reversal imaging require time‐domain calculations such as inverse Fourier transform. The computational burden of these calculations is critical; therefore, time‐reversal imaging is not the proper method for real‐time event localization. We exclude these time‐domain calculations for efficiency. Instead, the maximum amplitude position of the frequency‐domain autocorrelation of the time‐reversal wavefield is determined as a source location. We conducted numerical tests to validate our imaging condition. The synthetic data test shows that our proposed algorithm provides a more credible source localization result than the conventional grid‐search method does in a noisy environment even using only one frequency component with many receivers. We also applied our algorithm to two real datasets acquired from the small monitoring network in Pohang. The real‐data test shows a comparable result with the result of the grid‐search method.

In viscoacoustic least‐squares reverse time migration methods, the reflectivity image associated with the Q factor is negligible, inverting only the velocity (v) parameter or v‐related variables such as squared slowness or bulk modulus. However, the Q factor influences the amplitude and phase of the seismic data, especially in basins containing gas reservoirs or storing . Therefore, the Q factor and its associated parameters must be considered in the context of viscoacoustic least‐squares reverse time migration. Thus, we propose a multiparameter viscoacoustic least‐squares reverse time migration procedure, which obtains the inverse of bulk modulus (κ) and the Q magnitude (τ) simultaneously. We derive and implement the multiparameter forward and adjoint pair Born operators and the gradient formulas concerning κ and τ parameters. Then, we apply these derivations in our proposed multiparameter approach, which can produce images with better balanced amplitudes and more resolution than conventional reverse time migration images.

Conventional methods solve the full‐waveform inversion making use of gradient‐based algorithms to minimize an error function, which commonly measure the Euclidean distance between observed and predicted waveforms. This deterministic approach only provides a ‘best‐fitting’ model and cannot account for the uncertainties affecting the predicted solution. Local methods are also usually prone to get trapped into local minima of the error function. On the other hand, casting this inverse problem into a probabilistic framework has to deal with the formidable computational effort of the Bayesian approach when applied to non‐linear problems with expensive forward evaluations and large model spaces. We present a gradient‐based Markov Chain Monte Carlo full‐waveform inversion in which the posterior sampling is accelerated by compressing the data and model spaces through the discrete cosine transform, and by also defining a proposal that is a local, Gaussian approximation of the target posterior probability density. This proposal is constructed using the local Hessian and gradient informations of the log posterior, which are made computationally manageable thanks to the compression of the data and model spaces. We demonstrate the applicability of the approach by performing two synthetic inversion tests on portions of the Marmousi and BP acoustic model. In these examples, the forward modelling is performed using Devito, a finite difference domain‐specific language that solves the discretized wave equation on a Cartesian grid. For both examples, the results obtained by the implemented method are also validated against those obtained using a classic deterministic approach. Our tests illustrate the efficiency of the proposed probabilistic method, which seems quite robust against cycle‐skipping issues and also characterized by a computational cost comparable to that of the local inversion. The outcomes of the proposed probabilistic inversion can also play the role of starting models for a subsequent local inversion step aimed at improving the spatial resolution of the probabilistic result, which was limited by the model compression.

Full‐waveform inversion of multicomponent data can provide an improved estimation of medium parameters using both compressional‐ and shear‐wave information. However, most earlier studies that involved a full‐waveform inversion of ocean‐bottom data are based on acoustic anisotropic or elastic isotropic approximations. Here, we consider realistic elastic anisotropic media and develop an efficient full‐waveform inversion framework for estimating model parameters. We simulate seismic wavefields using a previously developed coupled acoustic/elastic wave propagator that implements a mimetic finite‐difference method with fully staggered grids to accurately handle the fluid/solid boundary conditions. The algorithm employs a multiscale approach starting from low frequencies and incorporating higher frequency bands in the later inversion stages. We analyse the influence of different types of input data on the accuracy of the inverted anisotropy parameters for hard and soft water bottoms. The employed misfit function incorporates information from both hydrophones and ocean‐bottom geophones. Numerical examples indicate that injecting multiple data components simultaneously increases the complexity of the objective function and often degrades the quality of the estimated medium parameters. Thus, we propose a sequential strategy using a single data component at a time. Pressure (hydrophone) data alone can provide satisfactory results if long offsets (i.e., with the offset/depth ratio ≥ 3) are available. Adding the horizontal particle‐displacement or ‐velocity components increases the accuracy of the estimated shear‐wave vertical velocity () and P‐wave normal‐moveout () velocity, especially for strongly heterogeneous sub‐water‐bottom models.

Unconsolidated sandstones are attractive targets for underground storage of carbon due to their high porosity and permeability. Monitoring of injection and movement of CO2 in such formations using elastic waves requires an understanding of the acoustic properties of the sandstone. Current approaches often use the so‐called soft‐sand model in which a Hertz–Mindlin model of the acoustic properties at high porosity is mixed with the acoustic properties of the mineral phase to predict the acoustic properties over the entire porosity range. Using well‐log data from two unconsolidated sand formations of interest for CO2 storage, we discuss the limitations of this model and provide an alternative approach in which the mechanical properties of grain contacts are obtained by inversion, and the properties of infill material lying within the pore space are estimated. The formations considered are the Paluxy Formation in Kemper County, Mississippi, and the Frio Formation near Houston, Texas. The ratio of the normal to shear compliance of the grain contacts is found to be significantly less than unity for both formations. This implies that the grain contacts are more compliant in shear than in compression. However, the grain contact compliance is higher and the ratio of the normal to shear compliance is lower for the Frio example than for the Paluxy case, and this may lead to sliding at grain contacts with low shear compliance and transport of grains during fluid flow, particularly if CO2 acts to weaken any cement that may be present at the grain contacts. Such transport was suggested by Al Hosni et al. in explaining why the magnitude of the time‐lapse effect due to the injection of CO2 at the Frio CO2 injection site is greater than predicted using conventional rock physics models. A simple model of the mechanical properties of infill material lying within the pore space suggests that the bulk and shear moduli of infill material in the Paluxy case are significantly higher than the Frio case, consistent with the lower grain contact compliance in the Paluxy case.

We investigated the effect of shale distribution on the joint elastic wave and electrical properties of shaly reservoir sandstones using a dataset of laboratory measurements on 75 brine‐saturated (35 g/L salinity) rock samples (63 samples from the literature, 12 newly measured samples). All the data were collected using the ultrasonic (700 kHz) pulse‐echo measurement technique for P‐ and S‐wave velocities (Vp, Vs), attenuations (Qp⁻¹, Qs⁻¹), and a four‐electrode method for resistivity under elevated hydrostatic confining pressures between 10 and 50 MPa (pore fluid pressure 5 MPa). The distribution of volumetric shale content was classified by comparing the calculated dry P‐wave modulus to the modified Upper Hashin–Shtrikman bound for quartz and air mixtures, assuming pore‐filling shale. This scheme in particular allowed us to distinguish between pore‐filling and load‐bearing shale distributions according to idealized definitions, which provides new insight into the joint ultrasonic properties and resistivity behaviour for shaly sandstones. In resistivity–velocity space, the resistivity of load‐bearing shale increases with increasing velocity which form a more distinct trend with steeper gradient compared to those for partial pore‐filling shale and clean sandstones. Moreover, the pore‐filling shale trend straddles the clean sandstone trend and meets the load‐bearing shale trend between 100 and 150 apparent formation factors. In resistivity–attenuation space, the highest attenuations exist when the volumetric shale content is close to the frame porosity (for Qp⁻¹ in particular), at the transition between pore‐filling and load‐bearing shales. The results will inform the development of improved rock physics models to aid reservoir characterization from geophysical remote sensing, particularly for joint seismic and controlled source electromagnetic surveys.

Pre‐stack seismic amplitude variation with offset inversion is a crucial technique in seismic exploration, employed to estimate reservoir elastic parameters and thus reservoir fluid properties. However, traditional amplitude variation with offset inversion methods are based on elastic theory and linear approximation, neglecting the inelasticity of medium and nonlinear theory. To overcome this limitation, a quadratic scattering coefficient equation of the viscoelastic fluid factor is derived, which provides the basis for the equation for amplitude variation with offset inversion. Traditional amplitude variation with offset inversion methods typically neglect seismic dispersion and attenuation, failing to account for the influence of the seismic wave velocity attenuation and frequency variation. Quality factors of P‐ and S‐waves represent the degree of attenuation of seismic waves. To comprehensively address the effects of seismic wave dispersion and attenuation, a novel method called pre‐stack seismic nonlinear frequency‐dependent amplitude variation with offset inversion has been developed. This method builds upon the new quadratic scattering coefficient and is utilized for reservoir fluid prediction. The reliability and stability of the method are verified through synthetic and filed data examples. Further analysis reveals that the method is more reasonable and accurate compared to the traditional linear amplitude variation with offset inversion method. The results demonstrate that the proposed pre‐stack seismic nonlinear frequency‐dependent amplitude variation with offset inversion method can effectively identify reservoir fluids, providing a novel solution for reservoir fluid identification.

Triaxial compression experiments were carried out on samples of fine‐grained Aji granite to measure the evolution of permeability during deformation prior to failure under confining pressures of 20 and 40 MPa. During the initial stages of deformation, a small decrease in permeability was observed, due to the closure of pre‐existing microcracks; permeability then increased with increasing differential stress. During deformation, permeability varied by up to two orders of magnitude, and we observed a small pressure dependence, with a larger variation observed at 20 MPa than at 40 MPa. This suggests that more cracks developed during brittle deformation under the lower confining pressure. The observed increase in permeability during our experiments was approximately proportional to inelastic volumetric strain, which corresponded to the volume of dilatant cracks. On the other hand, prior to brittle failure, we observed a further increase in permeability that was greater than the inelastic volumetric strain, suggesting crack aperture opening accelerated at this stress level (>∼80%). The permeability enhancement resulting from the crack dilation affects the gas‐sealing capacity of the fluid‐saturated caprock. Our experimental findings would be beneficial for the safety assessment in applications such as the long‐term storage of various gaseous wastes.

Cenozoic sediments from the continental rise off the George V Land consist of silty/clayey facies variably rich in diatom ooze; these sediments hold a record of the glacial history of the Wilkes Subglacial Basin and are important for estimating the contribution of the East Antarctic Ice Sheet to global sea level changes during past transition to warmer climates. This is fundamental to predict future scenarios related to the global warming. The petrophysical properties of Antarctic marine sediments are influenced by the ice sheet dynamics and may affect the amplitude of seismic reflections. Through a seismic‐to‐well tie procedure, we investigate the origin of high amplitude reflections from Miocene‐early Pliocene deposits identified in two seismic lines crossing at the Integrated Ocean Drilling Program Expedition 318 drill site U1359. Downhole and core log measurements are preconditioned and merged to obtain complete velocity and density records from the sea floor to the bottom of the deepest of the four wells drilled at this site. We generate a synthetic trace by convolving the reflectivity series with the seismic wavelet obtained from the sea‐floor reflection and match the synthetic trace to the seismic data with a time variant cross‐correlation procedure. This procedure established a robust time‐depth relationship, not achievable from the available small‐offset seismic data. To our knowledge, this is the first seismic‐to‐well tying in the George V Land area. Based on results from synthetic data, the anomalous high amplitude seismic package can be linked to changes in density of sediments. Such changes are interpreted as representative for the alternation of diatom‐rich (warm climate) and silty‐clay layers with ice‐rafted debris (cold climate) inside the deposits. We suggest that the analysis of the characteristics and the distribution of similar seismic anomaly around Antarctica can give insight into the modality of past Antarctic ice sheet dynamics.

Accurate estimation of hydrocarbon saturation is significant for log interpreters to evaluate tight sandstone formations. It is difficult to use conventional wireline logs to calculate hydrocarbon saturation in complex reservoirs. Dielectric dispersion logs are processed using dielectric models to obtain accurate saturation assessment, especially in high salinity shaly sand formations. However, the dielectric models consist of complex refractive index method model and shaly sand model ignore the impact of clay effects on complex refractive index method model, and the dielectric dispersion logs generated by them appear several unphysical phenomena in the clay‐bearing formations, such as the permittivity data at the highest frequency are greater than those at the second highest frequency and the conductivity data at the highest frequency are lower than those at the second highest frequency. This paper modifies the dielectric models by introducing a combined conductive phase in complex refractive index method model to correct the unphysical behaviours. Revised models are combined with four permittivity and four conductivity logs acquired by dielectric scanner at 24, 102, 360 and 960 MHz to inverse the water saturation, water salinity, textural index for water‐bearing pore and fraction of clay‐related phase. The complex refractive index method model interprets the logs measured at 960 MHz and shaly‐sand model interprets other dielectric dispersion logs. The particle swarm optimization algorithm is used to search inversion parameters. The results of forward simulation show that the revised models can correct unphysical behaviours of dielectric dispersion logs generated by the original models. Moreover, we apply original models and revised models to laboratory core data. The results of experiments show that the mean relative error values of the original models are 19.38% and 23.60% and of the revised models are 5.54% and 6.28% in two cases. It shows that the revised models are more accurate than the original models to interpret dielectric dispersion logs of tight sandstone reservoirs.

Coal elastic parameters are an important index reflecting the material composition and porosity, which can be used to guide reservoir evaluation and the safe mining of coal and coalbed methane. In this study, coal samples collected from coal seams #2, #3, #7 + 8 and #9 in the Yuwang colliery, eastern Yunnan, were studied, and the influences of organic matter, ash content and porosity on the elastic parameters of coal samples were investigated. The results show that the elastic parameters of coal are negatively correlated with organic matter content and porosity, and positively correlated with ash content. This means that the bulk modulus and primary‐wave velocity of coal decrease with a gradual increase in organic matter content or porosity and increase with a gradual increase in ash content. The main reason for this is that the ash modulus is typically higher than that of coal. When the ash content gradually increases, the contribution of ash to the equivalent modulus of coal increases, whereas the contribution of organic matter and pores decreases. In this study, the primary‐wave velocity of the target coal seam was fitted based on the generalized Gassmann equation. The calculation results were consistent with the observations and the relative error was within 7%. According to the distribution behaviour of elastic parameters, this provides adequate data support for the prediction of gas content in coal mines and ensures safe and green mining of coal resources.

Elastic wave velocities of compressional and shear waves propagating through sedimentary rocks are often coupled with information of bulk density to derive the rock stiffness. Acquiring the transit time of compressional and shear waves often involves manual picking of wave arrival times from wave trains recorded in the laboratory or by well‐logging tools. Picking the compressional wave arrival time is commonly accepted as straightforward. Oppositely, detecting the shear wave arrival and picking its arrival time is often troublesome because the transmitted shear wave partly converts to compressional waves and back to a secondary shear wave, concealing the transmitted shear wave arrival in the wave train. In laboratory settings, we illustrate the difficulty of shear wave detection in wave trains recorded on highly porous chalk plug samples from the Danish North Sea Basin. Wave trains were recorded on plugs dry, Tap‐water or Isopar‐L saturated during uniaxial strain compaction. The recorded shear wave trains showed two distinct features, which could be interpreted as the transmitted shear wave first arrival; we denoted them as early and late arrivals. However, as only one feature can mark the arrival of the transmitted shear wave, we propose a semi‐empirical disclosure strategy combining a graphical representation of stacked wave trains with rock physical modelling. By stacking recorded wave trains in a graphical strain–time–amplitude domain, we demonstrate that an early shear wave feature marks a converted shear to compressional to shear wave and not the transmitted shear wave. We used physical modelling to identify early shear wave features and illustrate the consequences of adopting a falsely interpreted shear wave on stiffness properties.

Pre‐stack seismic inversion is an effective method for elastic parameter inversion using seismic data, which facilitates seismic fluid identification. However, pure PP‐wave inversion has issues of strong multi‐solution and limited prediction accuracy. Therefore, we propose a seismic fluid identification approach based on the joint PP‐ and SH–SH‐wave stochastic inversion. First, the linearized SH–SH‐wave amplitude variation with offset approximation parameterized by shear modulus and density is derived. Numerical simulations demonstrate that the SH–SH‐wave amplitude variation with offset approximation has a good accuracy. Reflection coefficient contribution analysis indicates that the new formulation has better parameter sensitivity to shear modulus and density than the PP wave amplitude variation with offset approximation derived by Russell, which helps one to improve the inversion of shear modulus and density. On this basis, we construct a joint inversion equation of PP and SH–SH waves for a Russell fluid indicator, a shear modulus and density and present a novel joint stochastic inversion method based on the ensemble smoother with multiple data assimilation. Stanford VI‐E model tests reveal that the Russell fluid indicator factor, shear modulus and density obtained from the joint PP‐ and SH–SH‐wave inversion have higher identification accuracy and smaller relative errors than those from pure PP‐wave inversion. Furthermore, field data tests indicate that this method has practical applicability in seismic fluid identification.

The field of geophysics faces the daunting task of monitoring complex reservoir dynamics and imaging carbon dioxide storage up to several decades into the future. This presents numerous challenges, including sensitivity to parameter changes, resolution of obtained results and the cost of long‐term deployment. To effectively store CO2 subsurface, it is necessary to monitor and account for the injected CO2. The gravity method provides several advantages for CO2 monitoring, as changes in fluid saturation correspond directly and uniquely to observed density changes. Three‐axis borehole gravity has demonstrated significant promise as a next‐generation tool for reliably monitoring reservoir dynamics across a range of depths and sizes. However, the gravity inverse problem is highly ill‐posed, necessitating regularization that incorporates prior knowledge. To address this issue, we propose using a feed‐forward neural network, a machine learning method, to invert time‐lapse three‐axis borehole gravity data and monitor CO2 movement within a reservoir. By training the neural network on models that analyse changes in density and corresponding gravity responses resulting from perturbations made to the reservoir model, we can create scenarios that train the algorithm to identify unexpected CO2 migration in addition to the normal movement of CO2. Our method is demonstrated using reservoir models for the Johansen formation in offshore Norway. We convert reservoir saturation models into density changes and generate their corresponding three‐axis gravity data in a set of boreholes. Our results show that the developed machine learning inversion algorithm has high reliability and resolution for imaging density change associated with CO2 plumes, as demonstrated in the Johansen reservoir models utilized by the simulator. We also investigate machine learning inversion using regularization parameters and show that it is robust, with a strong tolerance for higher levels of noise. Our study demonstrates that the developed machine learning algorithm is a powerful tool for inverting three‐axis borehole gravity data and monitoring the migration and long‐term storage of injected CO2.

An approximative second order gravity inversion scheme by a truncated power series expansion is applied to derive the thickness of the Neogene, mostly clastic, sedimentary section of the Styrian Basins in South–East Austria, which are sub‐basins of the Pannonian Basin System. These sub‐basins with a derived thickness of up to 4 km are of interest for geothermal exploitation because of the increased geothermal gradient and heat flow observed in the Pannonian Basin in general and a geothermal gradient of 4°–5°/100 m measured in some wells in the Styrian Basin. The Styrian Basin also has been an area for hydrocarbon exploration in the past 50 years, with oil and gas show encountered in several exploration wells and one sub‐commercial gas discovery. The Miocene and Plio–Pleistocene volcanism in the Styrian Basin caused by Miocene crustal thinning is discussed in terms of the influence to the gravity inversion taking the aeromagnetic field into account. The volcanism is of relevance for the geothermal prospectivity but poses problems for the single layer‐based gravity inversion scheme. Results are discussed from a computational side comparing observed and calculated gravity fields but also the match with well data is discussed. In terms of gravity inversion methodology, the presented can be viewed as an approximative fast‐track approach.

Forward modelling of potential field data is an important part of optimization algorithms used to invert large datasets such as those involving rugged terrain or borehole data. Two‐dimensional fast Fourier transform modelling with a prism or a dipole is one of the most efficient methods compared to the forward modelling in the space domain. However, the exact solution of a prismatic source is limited to the case of a half‐space with the computation of data on a horizontal datum above the topography. Starting from the three‐dimensional Fourier forward modelling analytical formulation for a prism, an integration according to the wavenumber w is accomplished which allowed to find a two‐dimensional Fourier exact analytical formulation outside, at the interfaces of, and inside a prism for all potential field components. This new formulation requires the calculation of only four integrals. The gravity and magnetic fields are computed with this two‐dimensional fast Fourier transform formulation in the entire domain and compared with the analytical space domain and the three‐dimensional fast Fourier transform formulations. From the three‐dimensional calculated field, each component can be interpolated with the tri‐linear interpolation method along a borehole or on a drape surface simulating an airborne survey. Based on experiments demonstrated in this work, the two‐dimensional formulation in the Fourier domain gave accurate results with greater speed of execution in comparison to modelling in the space domain. The forward modelling method is tested on real gravity data from the north of Alberta (Canada).

The normalized full gradient was developed to determine anomalous bodies, such as oil and gas fields or simple geological structures studies. We believe that even in complicated geology, normalized full gradient is practical. We introduce data preprocessing and use step‐by‐step simple‐to‐complicated synthetic tests to develop previous researchers’ ideas for regional‐scale gravity modelling. One of the most important steps of the normalized full gradient is the determination of the N optimum value. We found that prevalent methods such as the standard spectral or maxima method are feasible in simple structures only. So, we have suggested the imaging criteria routine for complicated cases. We trace maximum normalized full gradient responses to detect the normalized full gradient responses at the increasing harmonic numbers as the transition of the extensive part of the anomaly to the sharp part of that. With imaging criteria for the determination of N optimum values, the complicated synthetic test results show the success of the normalized full gradient to understand complicated gravity signals. In the real case, we have studied the Northwestern Iran normalized full gradient model of the Bouguer ground gravity data beneath the seismic profile and prepared a P receiver function depth section to uncover the geometry of the Moho boundary and important interfaces in the crust. We suggest the inferred synthetic model from the Bouguer ground gravity anomaly and P receiver function depth section to normalized full gradient trustworthy test in real cases. According to the synthetic test results, we understand the frame of the normalized full gradient responses in the semi‐real case and truthful responses in the real case. Along with this, we study the second ground gravity profile of Northwestern Iran in a good resolution to uncover the deeper structures. The real case results show the possibility of Moho offset and thinning lithosphere beneath the North Tabriz Fault lithospheric boundary, the possible source of Sahand volcanic centre at the west side of the Moho offset beneath North Tabriz Fault, the deep root of the Sabalan volcanic centre in the lower crust and the lithospheric and asthenospheric wedge with the density contrast beneath Sahand–Sabalan volcanic centres. One of the most important results of our study is the lithosphere–asthenosphere boundary offset and stepped Moho possibility beneath the Talesh Mts next to the South Caspian Basin boundary.

The Bakreswar geothermal province represents a medium enthalpy geothermal system with its Bakreswar and Tantloie hot springs. It lies within the Chotanagpur Granite Gneissic Complex in the eastern part of the Indian Peninsula. The province has a high heat flow and a high geothermal gradient of 90°C/km. Magnetotelluric data from 95 sites in a frequency range of 10 kHz–10 Hz were acquired over the Bakreswar geothermal province to obtain an electrical conductivity model and map the geothermal reservoir with its fluid pathways and related geological structures. Subsurface conductivity models obtained from three‐dimensional inversions of the Magnetotelluric data exhibit several prominent anomalies, which are supplemented by gravity results. The conductivity model maps three features which act as a conduit (a) a northwest–southeast trending feature, (b) an east–west trending feature to the south of the northwest–southeast trending feature (which lies 1 km north of the Oil and Natural Gas Corporation fault marked by previous studies) and (c) shallow conducting features close to Bakreswar hot spring. The northwest–southeast trending feature coincides with the boundary of the high‐density intrusive block. This northwest–southeast trending feature provides the pathway for the meteoric water to reach a maximum depth of 2.7 km, where it gets heated by interacting with deep‐seated structures and then it rises towards the surface. The radiogenic process occurring within the granites of Chotanagpur Granite Gneissic Complex provides the heat responsible for heating the meteoric water. The northwest–southeast and east–west trending features are responsible for the transport of meteoric water to deeper depths and then towards the shallow regions of the Earth. The near surface features close to the Bakreswar hot spring are responsible for carrying the water further towards the hot spring. The resistivity of these structures plotted as a function of salinity and temperatures for saline crustal fluids suggests the involvement of meteoric water. Further, applying Archie's law to this resistivity suggests that the conduit path has a porosity greater than 10%. This study successfully maps the anomalous structures which might foster the migration of geothermal fluid in Bakreswar geothermal province.