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Objective Our objective was to investigate the basic information of the personnel and facilities of nuclear medicine in Beijing. Methods This survey was performed by the Beijing Quality Control Center in 2018. The investigation included personnel, equipment, and clinical applications, and data were then compared with previous surveys. The paper questionnaires were used for the survey, which required information about the personnel, devices, and clinical applications. Results About 38 nuclear medicine departments in Beijing were involved in the survey. The number of nuclear medicine staff was 531 in 2018, showing an increase of 58.7% over the past decade. Positron emission tomography/computed tomography (PET/CT), single-photon emission computed tomography (SPECT), and single-photon emission computed tomography/computed tomography (SPECT/CT) represented the main nuclear medicine facilities, and the total number of surveyed departments was 18, 24, and 34, respectively. The quality control results showed significant improvement from the 2005 levels. The total number of scintigraphy procedures was estimated at 199,607 (153,185 SPECT and 46,422 PET/CT). The estimated annual number of scintigraphy images was 8.9 per 1,000 population for SPECT and 2.7 per 1,000 population for PET/CT during 2018. The most frequent radioiodine-targeted therapy was 131 I-targeted therapy for hyperthyroidism in 2018. Conclusion Nuclear medicine has experienced rapid growth in the past 10 years in Beijing, either in personnel, equipment, and scintigraphy. Future efforts will focus on the use of new isotopes in the diagnosis, implementing quality strategy, and enhancing training. [ABSTRACT FROM AUTHOR]

Surface-related multiples prediction by inverse scattering series (ISS) is a completely data-driven method, which does not require a priori subsurface velocity information, and has the advantage of being suitable for complex structures compared with model-driven methods based on wave equations. But its computation and memory requirement are substantial. We propose a high-efficiency algorithm for the prediction of surface-related multiples by ISS in the curvelet domain. Firstly, we perform curvelet transform (CT) on shot gathers; secondly, we select few curvelet coefficient matrices for surface-related multiple prediction by ISS; thirdly, we perform inverse CT to the predicted multiples in the curvelet domain; lastly, we use matched filtering and subtraction to obtain primaries. Numerical experiments show that the proposed method not only improves computational efficiency and saves memory, but also achieves the same results compared with conventional ISS. [ABSTRACT FROM AUTHOR]

Blended acquisition improves the efficiency of seismic data acquisition sharply, and deblending algorithms are still open to prepare separated data. Most deblending methods are suitable for on-the-grid blended data. However, blended data in field cases are always at off-the-grid samples which pose great challenges in providing accurate deblended results. A binning strategy can assign an off-the-grid sample at its nearest on-the-grid sample approximately with the amplitude and phase bias caused by the existing distance between them. However, the subsequent deblending accuracy is low, especially when the amplitude and phase biases are large. With true off-the-grid data constraints, we introduce a Kaiser window tapered $\mathrm{sin}c$ interpolator to link off-the-grid samples and on-the-grid samples during the procedure of compressive sensing-based functional construction. Full expressions of the interpolator and its adjoint operator are provided to generate an iterative thresholding algorithm for off-the-grid blended data deblending. Separated on-the-grid data can be obtained accurately in an iterative manner. The deblending performance of artificially off-the-grid blended data demonstrates the validity of the proposed method quantitatively no matter whether the amplitude and phase biases are large or small. Field examples of off-the-grid blended data further prove the effectiveness of the proposed method to provide accurate on-the-grid separated data. [ABSTRACT FROM AUTHOR]

The successful production of unconventional resources such as shale gas is highly dependent on its two reservoir properties, organic matter and rock brittleness. High resolution spatially characterization of these two unconventional reservoir properties needs surface reflection seismic data. However, to delineate these two parameters on seismic scale is a challenging task because poor correlation is observed between these parameters and elastic properties of the rock. To encounter this adversity in current study we proposed effective attributes method in which organic shale reservoir properties are divided into their hard and soft elastic response. From the analysis of worldwide laboratory dataset, we find that hard and soft components have shown us much better linear correlation with P- and S- wave impedance. The proposed effective attributes, helped us to reduce the gap between unconventional reservoir properties and seismic characteristics. These attributes are the main controlling factor for rock elastic properties and exhibit information about hydrocarbon generation capacity and rock brittleness. A well data example from Sembar shale has also shown successful results for proposed effective attributes methodology. These attributes application on inverted P-wave impedance seismic data of employed organic shale reservoir have shown productive results to quantify its unconventional prospect on seismic scale. The approach used in this study can be confidently employed to assess unconventional reservoir potential in other parts of the world. [ABSTRACT FROM AUTHOR]

The blended acquisition can help improve the seismic data quality or enhance the acquisition efficiency. However, the blended seismic data should first be separated for subsequent traditional seismic data processing steps. The signal is coherent in the common receiver domain, and the blending noise shows randomness when the blending operator is constructed using a random time delay series. The seismic data can be characterized sparsely by the curvelet transform which can be used for deblending. However, it has a high computational cost, especially for large-volume seismic data. The spectrum of the seismic data is band-limited with the conjugate symmetry property, and thus the principal frequency components can characterize the signal accurately. The size of the principal frequency components is at least halved. Thus, we propose to implement the curvelet transform on the principal frequency wavenumber (PFK) domain data instead of the time-space (TX) domain data. The size of the PFK domain data is at least halved compared with the TX domain data, which can improve the deblending efficiency reasonably. The related formulae are fully derived and the efficiency enhancement analysis is provided in detail. One synthetic and two field artificially blended data are provided to demonstrate the validity and flexibility of the proposed method in the efficiency improvement and the deblending performance. The separated gathers can be beneficial for subsequent traditional seismic data processing procedures. [ABSTRACT FROM AUTHOR]

In conventional vector-wave-based elastic reverse-time migration, there are two types of artifacts: low-frequency artifacts and nonphysical artifacts. Vector-decomposition-based acoustic–elastic coupled equations are effective in suppressing nonphysical artifacts by using ocean bottom four component (4C) seismic data for receiver-side tensorial extrapolation. We introduce up/down-going wave separation into the vector-decomposition-based acoustic–elastic coupled equations, and deduce novel analytic acoustic–elastic coupled equations. With these novel equations, we can obtain the source-side and receiver-side up-going and down-going P/S-wave vectors in wavefield propagation, and effectively suppress both of the artifacts in vector-wave-based elastic reverse-time migration by combining receiver-side tensorial extrapolation and the decomposed vector-wave-based imaging conditions. Examples using synthetic data and field data are presented to illustrate the validity and effectiveness of our method. [ABSTRACT FROM AUTHOR]

An improved understanding of the effects of gas hydrate presence on seismic attenuation is important for accurate hydrate characterization and quantification. Based on a rock‐physics model recently presented for gas hydrate‐bearing fine‐grained clay‐dominated sediments, here we establish an integrated workflow for surface seismic data from extracting seismic attenuation to estimating gas hydrate concentration (Ch) in the sediment. We apply this workflow to the high‐resolution seismic data acquired at southern Hydrate Ridge, offshore Oregon, to reveal the hydrate distribution and clarify the controlling factor of hydrate formation. We first present an adaptive‐bandwidth spectral ratio method to robustly measure attenuation. The attenuation measurements show that the presence of hydrate suppresses the attenuation of the host sediment. We then calculate Ch by applying the rock‐physics model to the attenuation measurements. The estimated Ch are mostly low (<5%) in Hydrate Ridge and agrees well with the in‐situ Ch measured from core‐ or well log‐based data. Our result also suggests that the lithology and stratigraphic structures together control the distribution of gas hydrate at Hydrate Ridge, where relatively high Ch is found in the region where a gas‐charged conduit exists and in an anticlinal structure overlying a strong bottom simulating reflection. Adjacent to the anticline, however, a low amount of gas hydrate appears present, possibly due to the gas migration blocked by the anticlinal structure or the lack of gas conduits. Our study offers an effective strategy for detecting and quantifying gas hydrate in fine‐grained clayey sediments through surface seismic data. Plain Language Summary: Seismic attenuation represents the decay of seismic energy during the travel of seismic waves. Seismic attenuation is a crucial tool in detecting and evaluating gas hydrates. Our knowledge of the relation between seismic attenuation and how hydrate forms in sediment has been improved in these years. Here we present a workflow for seismic data in which we measure seismic attenuation using a novel method and calculate the hydrate content in clay‐rich sediment based on a rock physics model. We apply this workflow to the seismic reflection data in Hydrate Ridge, Oregon. The attenuation measurements show that gas hydrate suppresses the attenuation of host sediments. The gas hydrate amount estimated in this study agrees well with (a) seismic characteristics on the seismic profile and (b) the in‐situ measurements of core‐ or well log‐based techniques. Our results show that the gas hydrate distribution at Hydrate Ridge is mainly controlled by lithology and stratigraphic structures. This good agreement suggests that our method is able to not only extract attenuation but also obtain reliable estimation of hydrate amount using seismic data. Key Points: We present a workflow from extracting attenuation to estimating gas hydrate concentration for seismic reflection dataGas hydrate presence in fine‐grained clayey sediments suppresses the seismic attenuation of host sediments at Hydrate RidgeGas hydrate distribution at Hydrate Ridge is controlled by both lithology and stratigraphic structures [ABSTRACT FROM AUTHOR]

Seismic records from reflection seismic exploration in complex surface areas often contain scattered direct waves, a type of scattered waves generated by direct waves propagating on a rugged surface. For imaging reflections, scattered direct waves are regarded as noise, which lowers the signal‐to‐noise ratio of the reflected waves and deteriorates the quality of the seismic profile. Importantly, it is very challenging to separate scattered direct waves with strong energy and complex wave field characteristics owing to the rugged surface. In recent years, the rapid development of machine‐learning technology has broadened and advanced the application of deep learning in denoising seismic data. In this context, we propose an approach to using the convolutional autoencoder, a deep learning network, to intelligently separate scattered direct waves from seismic records on a rugged surface. The spectral element method is applied to simulate the elastic wave seismic records and scattered direct waves on a rugged surface. The synthetic shot gathers on the rugged ground are employed as the input for training the convolutional autoencoder network, while the simulated scattered direct waves on the uniform half‐space with the same rugged ground are employed as the label data for training. After the network training is completed, the trained convolutional autoencoder network can be applied to predict scattered direct waves from seismic records. The numerical experiments demonstrate that the proposed approach has good potential for suppressing complex scattered direct waves generated by direct waves propagating on a rugged surface. [ABSTRACT FROM AUTHOR]

Hydrogel-based flexible sensors have exhibited great application prospect in the fields of e-skin, movement monitoring and human-computer interaction. Nevertheless, it is still a difficult problem to construct the biomass-based hydrogel sensors with considerable mechanical property, biocompatibility, self-healing property, sensitivity, self-adhesion and fatigue resistance. Herein, a gelatin-based dual-network (DN) hydrogel sensor with multiple dynamic crosslinking was successfully fabricated, in which the dynamic Schiff base bonds between gelatin and an aldehyde-containing supramolecular crosslinker (PCD-Fc-CHO) formed by the host-guest complexing of β-cyclodextrin (β-CD) to ferrocene (Fc) were used to construct the first network of the hydrogel, while the reversible borate ester bonds of PVA with borax were utilized to establish the second network. Meantime, carbon nanotubes (CNTs) were added to endow the gelatin-based DN hydrogel with good conductivity and improved mechanical property. The prepared gelatin-based DN hydrogel possesses favorable ductility (1200%), biocompatibility, double self-healing (HE = 96%), high sensitivity (GF = 6.4), self-adhesion (repeatable adhesion), dual stimuli-responsiveness (pH and oxidation), plasticity and fatigue resistance. The DN hydrogel-based strain sensor can not only monitor various human body (e.g. fingers, knees, speech, etc.) movements and vital signs (pulse), but also monitor in vitro the breathing movements of pig lungs. Interestingly, the self-healed gelatin-based DN hydrogel sensor can also be reused, and its monitoring ability is basically the same as the original hydrogel. This work puts forward a new strategy for the construction of multiple dynamically cross-linked biomass-based self-healing hydrogels as multifunctional wearable flexible sensors. A novel gelatin-based dual-network supramolecular conductive hydrogel crosslinked by multiple dynamic bonds with considerable ductility, biocompatibility, rapid self-healing, high sensitivity, self-adhesion, pH and oxidation stimuli-responsiveness, plasticity and fatigue resistance was fabricated as a bidirectional strain sensor for monitoring human motions and vital signs. [Display omitted] • A novel gelatin-based dual-network supramolecular conductive hydrogel is fabricated. • Hydrogel contains dynamic host-guest interaction, Schiff base and borate ester bonds. • Hydrogel possesses considerable ductility, self-healing, sensitivity and self-adhesion. • Hydrogel exhibits good biocompatibility, stimuli-responsiveness, plasticity and fatigue resistance. • Hydrogel as bidirectional strain sensor can accurately monitor human motions and vital signs. [ABSTRACT FROM AUTHOR]

The problem of frequency-dependent seismic inversion for estimating attenuation and anisotropy based on rock physics effective model is reviewed and analyzed. Based on an extended periodically layered medium model, frequency-dependent stiffness parameters of anisotropic anelastic media as a function of attenuation factor and weaknesses are presented. For the case of an interface separating two anisotropic anelastic media, a new parameterized P-to-P reflection coefficient and anisotropic anelastic impedance A EI are proposed. Based on the anisotropic A EI , an inversion workflow of employing frequency components of seismic amplitudes to estimate attenuation factor and weaknesses for anisotropic anelastic media is presented. In the first stage inversion, the anisotropic A EI is calculated using frequency components of partially incidence-angle-stacked seismic data; and the second-stage inversion for unknown attenuation factor and weaknesses is implemented combining a Newton method and Bayesian Markov chain Monte Carlo algorithm by calculating first- and second-order derivatives of anisotropic A EI with respect to unknown parameters. Tests on synthetic seismic gathers confirm the accuracy of new parameterized reflection coefficient, and the unknown parameters are estimated reliably even in the case of seismic data of signal-to-noise ratio of 2 being employed. Applying the inversion approach and workflow to a field data set acquired over a shale reservoir, reliable attenuation factors and weaknesses that match well log curves are obtained, which are preserved as indicators for identifying potential hydrocarbon-bearing cracked reservoirs. Article Highlights: A new parameterized PP-wave reflection coefficient and anisotropic anelastic impedance are derived in terms of attenuation factor and weaknesses A two-stage inversion workflow of employing frequency components of seismic data to estimate attenuation factor and weaknesses combining Newton method and Bayesian Markov chain MonteCarlo algorithm Reliably estimated attenuation factor and weakness that match well log curves appear to be indicators or identification of potential cracked reservoirs [ABSTRACT FROM AUTHOR]

A quantitative model is proposed to estimate the magnitude of eustatic sea level fluctuations on the basis of definitions of relative and eustatic sea level. In this model, factors such as erosion, sedimentation, compaction, basement tectonic and loading subsidence, and paleo-bathymetry are considered. To estimate incremental changes in eustatic sea level, we developed methods to solve for the variables involved in the model on the basis of interpretation of seismic profiles. We reconstructed the onlap/offlap points removed by erosion by fitting the effective thickness in areas without erosion with a piecewise-defined function and extrapolating this function to zero thickness. We used decompaction and backstripping algorithms to remove the effect of compaction and to calculate loading subsidence of the basement. Regional data, however, are needed to estimate the paleo-water depths and the tectonic subsidence. For this reason, our methodology is considered semi-qualitative. As an application of this model, an eustatic sea level curve since the Pliocene (5.33 my) was deduced from interpretation of our high-resolution seismic data from the northern Sunda Shelf, South China Sea (SCS). On this curve, 36 fourth order sea level cycles were recognized with periods ranging from 0.08 to 0.29 my. The curve matches well with the smoothed deep-sea stable oxygen isotope curves from benthic foraminifera. [Copyright &y& Elsevier]

Noise components on PET images were studied. The results showed that the square of synthetic noise Δ[SUP2]and the square of signal-to-noise ratio ((N/Δ)[SUP2]varied with the signal intensity N and the relationships were respectively expressed with: Δ[SUP2] = 0.0395N[SUP2] + 0.1427N + 0.0025 (R[SUP2] = 0.9358) and ((N/Δ[SUP2])= -1.13932N[SUP2] + 7.0185N - 0.0746 (R[SUP2] = 0.9377). The synthetic noise on PET image may be categorized into three components. The first one varies directly as signal intensity, which depend on random coincidence; the second one varies directly as the square root of signal intensity, which depend on Poisson fluctuation; and the third one is independent of signal intensity. [ABSTRACT FROM AUTHOR]

• A novel simple ferrocene-containing dialdehyde cross-linker was synthesized. • Gelatin hydrogel could be fabricated using this cross-linker by Schiff base reactions. • Gelatin/PVA composite hydrogel with improved mechanical property was prepared using this cross-linker. • The composite hydrogel exhibited pH and oxidation dual-responsiveness. • The composite hydrogel could be used as a smart carrier to load functional molecules. A novel ferrocene-containing quaternary ammonium-type dialdehyde (Fc-(N+CHO) 2) was synthesized and used as a cross-linking agent to prepare the gelatin/PVA composite hydrogel with pH and oxidation dual-responsiveness. The mechanical properties, thermal stability, morphology and swelling behavior of the hydrogel were fully studied. The hydrogel exhibited excellent oxidation and acid-triggered swelling (even decomposing) properties because of oxidation destruction of Fc in Fc-(N+CHO) 2 and its acid-sensitive Schiff base cross-linkage with gelatin, which leads to the hydrogel as a smart carrier to load functional cargos in many fields including materials and medicine. [ABSTRACT FROM AUTHOR]

In controlled‐source seismology, the standard full waveform inversion (FWI) can't reliably recover the macrovelocity structure in the deep part, if the surface seismic data lack of ultra‐long offsets and very low frequencies. Reformulating the FWI for pre‐critical reflections based on model scale separation leads to reflection waveform inversion (RWI), which aims to improve the reconstruction of low‐to‐intermediate model wavenumbers. However, the state‐of‐art RWI approaches rely on the gradient‐type optimization, resulting in slow convergence and inaccurate recovery of the deep macrovelocities. Therefore, we present a Hessian‐based second‐order optimization in the context of RWI. Based on the reflection Fréchet derivative with respect to the background model, the approximate Hessian and the point spread functions (PSFs) on toy models with two and three layers are constructed to gain physical insights about parameter coupling and spatial resolution at the scale of low‐to‐intermediate wavenumbers for finite‐offset and band‐limited data. The high‐velocity anomaly and checkerboard experiments demonstrate the benefit of incorporating the inverse approximate Hessian effect on the functional gradient. Accordingly, for large‐scale applications, we propose a matrix‐free Gauss‐Newton RWI approach, in which the Hessian‐vector product is formulated with the second‐order adjoint‐state method and an optimal updating direction is estimated in the nested inner loop to accelerate the convergence. A synthetic example and an application to the real data from East China Sea demonstrate that the proposed method can improve velocity model building and seismic imaging, especially for the deep targets. Plain Language Summary: Seismic imaging of short‐spread reflection data is an important tool to map the Earth's internal structure in regional and exploration seismology. Using reflection waveform inversion (RWI) can provide the reconstruction of the deep macrovelocities, which is critical for achieving a high‐quality seismic imaging. However, the state‐of‐art RWI suffers from slow convergence and low accuracy when using a gradient‐type optimization method. To overcome this problem, we present a second‐order optimization approach by using the adjoint‐state method. The analyses of Frechet derivatives, Hessian matrix and point spread functions reveal some novel insights about this inverse problem. A matrix‐free algorithm using second‐order adjoint state method makes the proposed Gauss‐Newton RWI feasible for large‐scale applications. The synthetic and field data examples show that this approach can provide high‐resolution recovery of the background velocity model, and thus greatly improve seismic imaging in the deep part. Key Points: New insight into the functional Hessian at the long‐wavelength scale in reflection waveform inversion (RWI)Second‐order adjoint‐state formulation for the Hessian‐vector productApplications of Gauss‐Newton reflection waveform inversion (RWI) to synthetic and real offshore data [ABSTRACT FROM AUTHOR]

In seismic data acquisition, because of several factors, such as surface barriers, receiver failure, noise contamination and budget control, seismic records often exhibit irregular sampling in the space domain. As corrupted seismic records have a negative effect on seismic migration, inversion and interpretation, seismic trace interpolation is a key step in seismic data pre‐processing. In this paper, we propose a high‐efficiency and high‐precision seismic trace interpolation method for irregularly spatially sampled data by combining an extreme gradient boosting decision tree and principal component analysis in a semi‐supervised learning method. The adjacent trace number, sampling number and amplitudes of the effective seismic data were taken as features to build the training data set for the extreme gradient boosting decision tree. Principal component analysis is applied to remove redundant information and accelerate the training speed. This is different from the traditional trace interpolation method in that the proposed method is data‐driven; therefore, it does not require any assumptions. Compared with other deep learning‐based trace interpolation methods, the proposed method has fewer control parameters and learning labels and a smaller training cost. Experiments using synthetic and field data demonstrated the validity and flexibility of this trace interpolation method. [ABSTRACT FROM AUTHOR]

Investigating the global sea level budget is essential to quantify the total sea level change (altimetry) and its components, including the steric sea level change and the ocean mass change (gravity), where the latter is mainly attributed to four mass-driven components (Greenland, Antarctica, glaciers and land water storage). In this study, a 24-year global ocean mass change is derived by the joint use of Tongji-LEO2021 and Tongji-Grace2018 monthly gravity field models over 1993–2016, with which the sea level budgets in terms of rate and acceleration are investigated over global oceans within the latitudes 66oN to 66oS together with the IGG-SLR-HYBRID gravity field models, altimetry, steric and four mass-driven components. The statistical results show that the global mean ocean mass change rate accounts for ∼54% of 2.85 ± 0.30 mm/year of global mean total sea level change. The accelerations of global mean total sea level change and its components are 0.145 ± 0.025 mm/year2 (altimetry), 0.003 ± 0.021 mm/year2 (steric), 0.139 ± 0.047 mm/year2 (ocean mass from Tongji), and 0.137 ± 0.010 mm/year2 (the sum of mass-driven components) respectively, indicating that the global sea level budget in terms of acceleration can be closed and nearly no acceleration exists in the global mean steric sea level change for the period 1993–2016. [ABSTRACT FROM AUTHOR]

A better understanding of wave attenuation in hydrate-bearing sediments is necessary for the improved geophysical quantification of marine gas hydrates. Here we compare the attenuation behavior of hydrate-saturated vs water-saturated sediments at site GC955H, in the Gulf of Mexico, which was surveyed during the JIP Leg II expedition. We compute the P-wave attenuation of the gas hydrate bearing sediments using the median frequency shift method on the monopole waveforms. The results show that P-wave attenuation due to low saturation (< 0.4) in hydrate-filled fractures of fine-grained sediment is comparable to that of the water-filled fracture case. On the contrary, P-wave attenuation due to high saturation (> 0.4) in the hydrate-filled pores of coarse-grained sediments can be up to as much as three times more than that of the water-saturated case. The correlation analysis shows that the P-wave attenuation increases with the increasing gas hydrate saturation for the highly saturated gas hydrate-bearing sand interval while the correlation of the P-wave attenuation and hydrate saturation is weak for low saturated gas hydrate-bearing shale interval. The results show that P-wave attenuation is more likely to be used as a geophysical proxy for gas hydrate quantification of highly concentrated coarse-grained sediment rather than for that of fine-grained sediment. To examine the P-wave behavior in sand, we use the improved LCAM model, which accounts for physical factors such as grain boundary roughness and squirt flow to explain the observed differences in P-wave attenuation between hydrate and water-saturated coarse-grained sediment. Our results provide further geophysical evidences for P-wave behavior in the gas hydrate-bearing sediments in the field. [ABSTRACT FROM AUTHOR]

Deep learning has been used in seismic exploration to solve seismic inversion problems, however it requires sufficient and diverse training samples and labels to obtain satisfactory results. Insufficient training labels are a common problem since labels usually come from well‐logging data, which are limited and sparsely distributed. This can result in a trained network with poor generalizability. A novel complete synthetic dataset‐driven method utilizing convolutional neural network is presented for seismic amplitude‐versus‐offset attributes P and G inversion. Gaussian simulation sampling with physical constraints is used to generate a complete elastic parameter dataset by traversing the entire elastic parameter model space. By randomly combining elastic parameters from the full elastic parameters model space, sufficient synthetic pre‐stack amplitude‐versus‐offset gathers and attribute datasets are generated for training the convolutional neural network. Compared with the limited real data‐driven convolutional neural network, the complete synthetic dataset‐driven convolutional neural network has better generalizability. Broadband training labels improve the accuracy and resolution of the complete synthetic dataset‐driven convolutional neural network's inversion results beyond that of the conventional least‐square data‐fitting method. The complete synthetic dataset‐driven convolutional neural network is robust for processing noise‐contaminated seismic data, but if the frequency band of the labels for training the network is too wide and the signal‐to‐noise ratio of pre‐stack amplitude‐versus‐offset gathers is too low, the quality of the inversion results will reduce. The Marmousi II model and field data examples show that the novel complete synthetic dataset‐driven convolutional neural network can extract higher‐resolution amplitude‐versus‐offset attributes P and G. [ABSTRACT FROM AUTHOR]

Heavy oil is considered an important alternative for traditional fossil fuels. Exploring and developing heavy oil reservoirs require time-lapse seismic data for dynamic monitoring of the reservoir elastic parameters and determining of the temperature field to evaluate the fluidity of the heavy oil. The key is establishing a relationship between the temperature and seismic elastic parameters to convert the elastic field into a temperature field. Therefore, a temperature-dependent rock physics model for heavy oil sand must be established. In this study, we investigated the temperature-dependent elastic wave velocities of natural and artificial heavy oil sands. The modified contact cement theory and the solid Gassmann equation were combined to construct a temperature-dependent rock physics model. The proposed model can predict variations in elastic wave velocities along with temperature changes in both natural and artificial heavy oil sands. Based on the model results, we propose a possible mechanism for the temperature-dependent elastic wave velocity of heavy oil sands, namely, the weakening of heavy oil cementation is the main controlling factor in the elastic wave velocity sensitivity of heavy oil sands. A temperature-dependent rock physics template (TDRPT) is proposed to diagnose the temperature, and the diagnostic results were verified using natural heavy oil sands. Our proposed model and TDRPT are essential tools to guide the thermal production of heavy oil reservoirs. • Temperature-dependent rock physics template proposed to diagnose temperature. • Modified contact cement theory and solid Gassmann equation combined in model. • Heavy oil cementation weakening controls elastic wave velocity of heavy oil sands. [ABSTRACT FROM AUTHOR]

[Display omitted] [ABSTRACT FROM AUTHOR]

Accurate estimates of global sea-level change from the observations of Altimetry, Argo and Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) are of great value for investigating the global sea-level budget. In this study, we analyzed the global sea-level change over the period from January 2005 to December 2019 by considering all potential impact factors, i.e. three factors for Altimetry observations (two Altimetry products, ocean bottom deformation (OBD) and glacial isostatic adjustment (GIA)), three factors for Argo observations (four Argo products, salinity product error and deep-ocean steric sea-level change), and seven factors for GRACE/GRACE-FO observations including three official RL06 solutions, five spatial filtering methods, three GIA models, two C20 (degree 2 order 0) products, Geocenter motion, GAD field and global mass conservation. The seven impact factors of GRACE/GRACE-FO observations lead to ninety combinations for the post-procession of global mean barystatic sea-level change estimation, whose rates range from 2.00 to 2.45 mm/year. The total uncertainty of global barystatic sea-level change rate is ± 0.27 mm/year at the 95% confidence level, estimated as the standard deviation of the differences between the different datasets constituting the ensembles. The statistical results show that the preferred GIA model developed by Caron et al. in 2018 can improve the closure of the global sea-level budget by 0.20–0.30 mm/year, which is comparable with that of neglecting the halosteric component. About 30.8% of total combinations (GRACE/GRACE-FO plus Argo) can close the global sea-level budget within 1-sigma (0.23 mm/year) of Altimetry observations, 88.9% within 2-sigma. Once the adopted factors including GRACE/GRACE-FO solutions from Center for Space Research (CSR), Caron18 GIA model, SWENSON filtering and Argo product from China Second Institute of Oceanography, the linear trend of global sterodynamic sea-level change derived from GRACE/GRACE-FO plus Argo observations is 3.85 ± 0.14 mm/year, nearly closed to 3.90 ± 0.23 mm/year of Altimetry observations. [ABSTRACT FROM AUTHOR]

The first-order (or linear) Rytov or Born approximation is the foundation for formulation of wave-equation tomography and waveform inversion, so the validity of the Rytov/Born approximation can substantially affect the applicability of these theories. However, discussions and research reported in literature on this topic are insufficient or limited. In this paper we introduce five variables in scattering theory to help us discuss conditions under which the Rytov approximation, in the form of the finite frequency sensitivity kernels (RFFSK), the basis of waveform inversion and tomography, is valid. The five variables are propagation length L, heterogeneity scale a, wavenumber k, anisotropy ratio ξ, and perturbation strength ɛ. Combined with theoretical analysis and numerical experiments, we conclude that varying the conditions used to establish the Rytov approximation can lead to uninterpretable or undesired results. This conclusion has two consequences. First, one cannot rigorously apply the linear Rytov approximation to all theoretical or practical cases without discussing its validity. Second, the nonlinear Rytov approximation is essential if the linear Rytov approximation is not valid. Different from previous literature, only phase (or travel time) terms for the whole wavefield are discussed. The time shifts of two specific events between the background and observed wavefields measured by cross-correlation will serve as a reference for evaluation of whether the time shifts predicted by the FFSKs are reasonably acceptable. Significantly, the reference 'cross-correlation' should be regarded as reliable only if the condition 'two specific similar signals' is satisfied. We cannot expect it to provide a reasonable result if this condition is not met. This paper reports its reliability and experimental limitations. Using cross-correlation (CC) samples as the X axis and sensitivity kernel (SK) or ray tracing (RT) samples as the Y axis, a chart of cross validation among the three is established. According to experimental analysis for a random and deterministic medium, the RFFSK works well for diffraction and geometric optics enclosed by the boundaries ka > 1 and (Λ < 1, ΛΦ < 2 π ∪ Λ > 1, Φ < 2 π), where Λ is a diffraction variable and Φ is a variable used to measure scattering strength. Both Λ and Φ depend on the five variables mentioned above. However, a large perturbation will limit the applicability of the Rytov approximation and narrow the range in which it is valid, because larger ɛ results in higher-amplitude fluctuations and larger back-scattering. Therefore, large ɛ is important in making SK/RT predictions discrepant from correct CC measurements. We also confirm that the RFFSK theory has wider range of application than ray theory. All the experiments point out the importance, both practical and theoretical, of respecting the conditions used to establish the Rytov approximation. [ABSTRACT FROM AUTHOR]

Natural gas hydrates (NGH) are considered as a potential energy resource in the future because of its dramatic energy reserves. NGH formation owns the characteristic of viscoelasticity, to some extent behaves both like liquid and solid. Ignoring the viscoelastic properties of NGH can lead to inaccurate prediction of related characteristics. In 2020, horizontal-well drilling technology was adopted to improve the stimulation performance of NGH reservoirs in the Shenhu area of China. This indicates that it is possible to apply hydraulic fracturing or CO 2 fracturing in the reservoir reconstruction of NGH. CO 2 fracturing can generate more fracture networks and connect micro fractures. Additionally, CO 2 can be stored geologically and integrated with injection, production, and storage. This study provides a relatively detailed demonstration of the formation and basic mechanical properties of NGH. Then, a comprehensive overview of potential NGH production enhancement methods is conducted. Finally, an evaluation method for dual conductivity considering creep effect is proposed, and the results show that the creep effect of NGH can reduce the conductivity of fractures and matrices. Thus, it is of great importance to investigate the viscoelastic properties of NGH formation, for the long-term prediction of the geo-mechanical response to gas extraction. • A detailed feasibility study of CO 2 fracturing for extracting NGH is conducted. • The permeability of NGH decreases with the increment of creep strain. • The relationships between creep strain and both of matrix and fracture conductivity are investigated. • A dual conductivity model is established to provide a guideline for NGH exploitation. [ABSTRACT FROM AUTHOR]

The blended acquisition allows multiple sources to be simulated simultaneously in a narrow time interval, which can improve the acquisition efficiency and reduce the acquisition cost tremendously. However, the overlapped information from multiple sources poses challenges for traditional seismic data migration or inversion algorithms. Thus, accurate and efficient deblending should be implemented as a pre-requisite. Traditional inversion-based deblending algorithms can provide deblended data with a high computational burden, especially for a large volume of seismic data. As a deep learning strategy can match seismic data accurately in a nonlinear way through supervised learning, we propose a U-net-based accurate deblending algorithm, which incorporates transfer learning and an iterative strategy. A set of labeled synthetic data with a blending fold of 2 are classified into the training and validation data for U-net training and validation. Field data are regarded as the test data to assess the performance of the trained U-net. To guarantee the deblending performance of the field data to some extent, parts of field data with labels are used to fine-tune the trained U-net based on transfer learning. The fine-tuning procedure is relatively fast within several minutes. To further improve the deblending performance, we incorporate an iterative strategy with the fine-tuned U-net. The deblending performance is promising in the quality and computational efficiency compared with the curvelet-thresholding-based deblending method, which demonstrates the validity of the proposed intelligent deblending method. [ABSTRACT FROM AUTHOR]

The elastic properties of argillaceous sandstones are significantly controlled, however, by the perplexing distribution of dispersed, cemented and matrix (including structural and layered) clay. The corresponding rock physics models are established to investigate the influence of different clay distribution on the elastic properties of sandstone. The rock physics modelling and laboratory experimental results exhibit that the higher the content of the matrix clay, the lower the elastic wave velocity. The dispersed and cemented clay increases the sandstone's velocity by reducing the porosity; the increase of dispersed clay only causes a slight increase in the elastic wave velocity in sandstone. In contrast, a small amount of cemented clay can significantly increase the velocity in sandstone. Based on these understandings, we construct a cross‐plot of P‐wave velocity and clay content as a template to diagnose clay distribution. Based on the rock physics model and empirical knowledge, we divide the template into four zones, namely, matrix, dispersed, cemented and mixed clay distribution zones. Then, we use the published experimental data and numerical modelling data to validate the template. Based on diagnosing the clay distribution, we introduce how to use the diagnostic results of the template to select a suitable rock physics model for argillaceous sandstone in Shengli Oil Field, East China. The selected rock physics model, guided by diagnosing the clay distribution, predicts P‐ and S‐wave velocity very well. The proposed rock physics template for diagnosing clay distribution also has potential application in well‐logging data interpretation, rock physics modelling and reservoir characterization. [ABSTRACT FROM AUTHOR]

Inferring porosity of subsurface from seismic data is of profound significance to many fields of Earth science and engineering applications, including but not limited to: hydrocarbon reservoir characterization, underground water flow modeling, geological CO2 storage, and geothermal energy exploitation. Traditional model‐driven approaches confront the problems of strong nonlinearity and geological heterogeneity, while machine learning is good at nonlinear mapping, providing higher efficiency and accuracy as well. We propose a Random Forest (RF) based method using multiple seismic attributes to predict the underground porosity distribution with uncertainty quantification. The standard deviation of base models' predictions is used to quantify the regression uncertainty of RF. The uncertainty can robustly indicate the prediction quality in numerous experiments, where low uncertainty corresponds to relatively precise prediction and high uncertainty gives a possibility of larger errors. Furthermore, we utilize the quantified uncertainty to improve the RF regression accuracy by correcting the originally predicted porosity according to the statistical relationship between the absolute error and the standard deviation. The application of the proposed method on seismic data shows its potential to characterize spatially varying reservoir parameters, and the quantified uncertainty profile offers insights into risk evaluation for hydrocarbon exploration and development. Plain Language Summary: Estimating underground porosity distributions from seismic data is of considerable interest in a broad range of Earth science, from hydrocarbon reservoir characterization to geological CO2 storage. The relationship between the related seismic attributes and porosity is highly nonlinear, especially in complex geological conditions, which impedes conventional approaches of porosity estimation. However, machine learning provides a more flexible and intelligent means to map directly from seismic data to porosity. In this study, we use a machine learning algorithm named Random Forest, which is an ensemble of many decision trees, to predict the underground porosity distribution from multiple seismic attributes. Meanwhile, the prediction uncertainty that measures the prediction reliability is quantified based on the diversity of predictions from different trees within a forest. We find that the uncertainty is approximately positively correlated with the prediction error. Thus, we further develop a strategy to correct the originally predicted porosity based on the quantified uncertainty for higher accuracy. Using the proposed method, a two‐dimensional profile of porosity distribution with the uncertainty profile is produced, which demonstrates its potential to delineate spatially varying reservoir properties from large‐scale seismic data. Key Points: A machine learning based method is developed to directly estimate porosity from multi‐seismic attributesThe standard deviation derived from the Random Forest regressor is capable of indicating the porosity prediction uncertaintyThe quantified uncertainty is further utilized to improve the performance of porosity prediction [ABSTRACT FROM AUTHOR]

Understanding and modeling the elastic and anisotropic responses of organic shale is crucial for interpreting sonic and seismic data, and hence play a vital role in many fields of Earth and Energy science such as hydrocarbon source rock evaluation, reservoir characterization of unconventional shale plays, cap rock assessment for geological storage of CO 2 and nuclear waste disposal. The thermal maturity level significantly controls the geochemistry signature, geological characteristics and geophysical responses of organic shale. We review the rock physics modeling that links the geological feature with elastic responses of organic shale in general. However, current rock physics modeling of organic shales either ignore or qualitative consider the effects of thermal maturity. To construct a quantitatively maturity-constrained rock physics model for shale, we review the evolutionary characteristics of two types of organic matter (kerogen and bitumen) and organic porosity during thermal maturation. We summarize and quantitatively characterize the maturity-dependent elastic properties of organic matter and fluids. We propose a joint geochemistry-rock physics modeling that use various effective medium theories to quantify the effects of thermal maturity level (Vitrinite reflectance, % R o) on the elastic signatures of inorganic frame, fluids filled porous organic matter as well as their elastic interactions. In particular, the role of kerogen and bitumen on the elastic properties of the organic shale are separately incorporated into the rock physics modeling. The developed rock physics model demonstrates that reservoir parameters (e.g. TOC, porosity and clay content) have distinct impacts on the elastic and anisotropic behaviors of shale reservoirs at different maturity levels. We also use several ultrasonic and sonic logging data sets at various maturity levels, including Jimsar shale formation in NW China, Qingshankou shale formation in NE China and Longmaxi shale formation in SW China, to benchmark the effectiveness of the proposed model. The modeling results show satisfactory agreement with the P-wave velocity, S-wave velocity, density, P-wave anisotropy magnitude, and S-wave anisotropy magnitude, only when the appropriate maturity level is given, illustrating the importance of combining organic geochemistry with rock physics modeling. We also discussed the influence of organic matter morphology, the degree of clay alignment as well as overpressure-induced micro-fractures on the elastic and anisotropic responses of organic shale, which can be also considered within the developed rock physics modeling framework. [ABSTRACT FROM AUTHOR]

Heterogeneity is an inherent feature of subsurface rocks. Unconventional reservoir shales, a group of typically fined-grained sedimentary rocks, usually exhibit strong heterogeneities due to the diversity of their constituents, including stiff silicate and carbonate minerals, ductile clay minerals, and organic matter. Understanding the influence of heterogeneities on the mechanical response as well as the failure behavior of reservoir shales plays a crucial role in numerous fields of Earth and Energy sciences, such as unconventional reservoir exploration, sealing of CO 2 sequestration, nuclear waste disposal, and hydrogen storage. In this study, we propose two heterogeneity indexes, intending to quantitatively characterize the heterogeneity of mineralogical compositions in unconventional reservoir shales. The first index takes into account the heterogeneity of mineral content, and the other one comprehensively considers the mineralogical heterogeneity and its corresponding mechanical properties. We apply these heterogeneities indexes to two experimental datasets of marine and lacustrine shales. The results show that the heterogeneity index significantly influences the mechanical properties and deformation behaviors of reservoir shales. As the increment of heterogeneous indexes, the ultimate strength and static Young's modulus show a general decreasing trend. However, the effect of the heterogeneity index on the static Poisson's ratio is weak. The numerical simulation results also confirm our experimental analysis, where the ultimate strength and Young's modulus of rock have a negative correlation with the heterogeneity indexes. The proposed heterogeneity indexes provide insights into understanding the mechanical behaviors and fracability assessment of reservoir shales. [ABSTRACT FROM AUTHOR]

• Systematic zircon ages from offshore basins of eastern China are firstly reported. • Zircon age spectra of the modern Yangtze River firstly appeared in the late Miocene. • The Yangtze River with present erosion patterns was formed in the late Miocene. The Yangtze River originates in the Tibetan Plateau and supplies tremendous sediments to the East China Sea. To date, when and how the Yangtze River was formed is still highly debated. This paper presents systematic detrital zircon U-Pb ages (3575 grain ages from a total of 29 samples) from the Cenozoic sediments of offshore basins in East China. The zircon age spectra vary significantly in the South Yellow Sea Basin, but are relatively stable in the East China Sea Shelf Basin (ECB) and significantly different from those of present-day Yangtze River (PYR) before the late Miocene. Comparisons of zircon ages with potential source terranes suggest that the ECB has received detrital sediments that PYR features since the late Miocene, whereas before that time, the sediment provenances show a North China and Korea affinity. Considering the overwhelming sediment input from the eastern Tibetan Plateau, the first occurrence of the PYR-featured sediments in the ECB in the late Miocene suggests that the Yangtze River with approximately present erosion patterns was established at that time. This agrees well with the exhumation history of the eastern Tibetan Plateau. The unique zircon ages in the Eocene to mid-Miocene sediments imply that the Yangtze River with headwaters in eastern Tibet might not exist before the late Miocene. This study provides new insights into the Yangtze River evolution although more lines of evidence are required, which is mainly because of the complex river sediment source-to-sink processes and tectonic and climatic forcings in the large catchment. [ABSTRACT FROM AUTHOR]

The trace interval in the common shot and receiver gathers is always inconsistent. The inconsistency affects the final performance of seismic data processing, and the reconstruction methods can enhance the consistency. Unfortunately, most interpolation algorithms are suitable in randomly missing cases, and the difficulty increases sharply in regularly missing cases, especially with big gaps. As deep learning (DL) has a strong self-learning ability in nonlinear characterizations to avoid linear events, sparsity, and low rank assumptions, we introduce DL into missing shots’ reconstruction. The spatial reciprocity of Green’s function is used to provide reasonable training data sets. First, the residual learning networks (ResNets) and the interpolation issue are briefly illustrated. Then, the spatial reciprocity is reviewed and illustrated qualitatively using the common shot and receiver gathers. The similar features in the common shot and receiver gathers guarantee the reasonability to regard the common shot gathers as the training sets and to regard the common receiver gathers as the test sets. The common shot gathers are divided into the training sets to train ResNets and the validation sets to verify the performance of the trained ResNets. Finally, the trained ResNets are used to reconstruct missing shots intelligently in the common receiver gather. Three different data sets are used to prove the validity of the proposed strategy. After reconstruction, the events are more continuous with less serrations and serious frequency wavenumber (FK) aliasing is attenuated effectively. The reconstructed data with a better consistency can improve the accuracy of migration and the final reservoir characterization. [ABSTRACT FROM AUTHOR]

By evaluating the consistency of the Gassmann theory with various inclusion‐based effective medium theories, we investigate the impact of elastic interactions between ellipsoidal pores on the poroelasticity. To rule out any factors that can violate the Gassmann condition, other than elastic interactions, we first construct idealized models that contain only a single set of isolated, identical, and vertically aligned ellipsoidal pores. The numerical simulation suggests that the periodic distribution of ellipsoidal pores generate uniform pore pressure distribution, whereas random distribution of ellipsoidal pores generates heterogeneous pore pressure distributions. Then we analyze the precise conditions under which the underlying Gassmann relationship is valid for various inclusion‐based models. The results reveal the following: (1) Noninteracting effective medium theories are always consistent with the Gassmann prediction, simply because the elastic interactions are ignored. (2) The elastic interactions between randomly distributed pores cause heterogeneous pore pressure that violates the essential requirement of the Gassmann theory. The differential effective medium and self‐consistent approximation theories corresponding to this model thus are inconsistent with the Gassmann prediction. (3) The elastic interactions between periodically distributed pores cause uniform pore pressure; therefore, the Gassmann condition is fully satisfied. The T‐matrix approach explicitly takes into account such elastic interactions and thus is consistent with the Gassmann theory. It is interesting to notice that on top of other well‐known common types of heterogeneities, like pore structure or fluid heterogeneities, the distribution of pores and its associated elastic interactions can be a separate source of heterogeneity, and this makes Gassmann equations not valid anymore. Key Points: The essential conditions and physics for various inclusion‐based EMT to be consistent with the Gassmann theory are analyzedThe inconsistency of DEM and SCA with the Gassmann theory is mainly attributed to the random distribution of poresThe consistency of T‐matrix with the Gassmann theory is mainly attributed to the periodic distribution of pores [ABSTRACT FROM AUTHOR]

The Radon transform (RT) has been widely used as a powerful tool, especially in exploration geophysics fields, such as multiple removal, interpolation, and velocity analysis. However, the existing strong outlier effects can seriously decrease the accuracy of the traditional RT. Therefore, a robust time-invariant RT (TIRT) is proposed in the mixed time–frequency domain to attenuate the outlier effects by using double L1-norm sparse constraints performed on the data misfit and the Radon model in the time domain. For the TIRT, the forward RT and its adjoint can be implemented in the frequency domain efficiently. Only one matrix inversion for each frequency component is involved in all iterations to speed up the iterations. Then, the 1-D alternating split Bregman (ASB) algorithm is introduced and improved for 2-D Radon model updating efficiently. It involves matrix-vector multiplication operators and two proximity operators. These two proximity operators can guarantee the robustness and sparseness of the proposed method. Numerical examples of synthetic and field data demonstrate the effectiveness and validity of the proposed method. The proposed method is also used for interpolation to decrease the trace interval. After interpolation, seismic data are more continuous with less serrations along the spatial direction and the frequency-wavenumber spectrum is more focused. The interpolated data have wider potential applications in improving the accuracy of the following seismic processing. It should be noted that the proposed robust and efficient RT can also be used in remote sensing and computerized tomography fields instead of the traditional RT. [ABSTRACT FROM AUTHOR]

Sweet spots identification for unconventional shale reservoirs involves detection of organic-rich zones with abundant porosity. However, commonly used elastic attributes, such as P- and S-impedances, often show poor correlations with porosity and organic matter content separately and thus make the seismic characterization of sweet spots challenging. Based on an extensive analysis of worldwide laboratory database of core measurements, we find that P- and S-impedances exhibit much improved linear correlations with the sum of volume fraction of organic matter and porosity than the single parameter of organic matter volume fraction or porosity. Importantly, from the geological perspective, porosity in conjunction with organic matter content is also directly indicative of the total hydrocarbon content of shale resources plays. Consequently, we propose an effective reservoir parameter (ERP), the sum of volume fraction of organic matter and porosity, to bridge the gap between hydrocarbon accumulation and seismic measurements in organic shale reservoirs. ERP acts as the first-order factor in controlling the elastic properties as well as characterizing the hydrocarbon storage capacity of organic shale reservoirs. We also use rock physics modeling to demonstrate why there exists an improved linear correlation between elastic impedances and ERP. A case study in a shale gas reservoir illustrates that seismic-derived ERP can be effectively used to characterize the total gas content in place, which is also confirmed by the production well. [ABSTRACT FROM AUTHOR]

Conventional seismic analysis in partially saturated rocks normally lays emphasis on estimating pore fluid content and saturation, typically ignoring the effect of mobility, which decides the ability of fluids moving in the porous rocks. Deformation resulting from a seismic wave in heterogeneous partially saturated media can cause pore fluid pressure relaxation at mesoscopic scale, thereby making the fluid mobility inherently associated with poroelastic reflectivity. For two typical gas-brine reservoir models, with the given rock and fluid properties, the numerical analysis suggests that variations of patchy fluid saturation, fluid compressibility contrast, and acoustic stiffness of rock frame collectively affect the seismic reflection dependence on mobility. In particular, the realistic compressibility contrast of fluid patches in shallow and deep reservoir environments plays an important role in determining the reflection sensitivity to mobility. We also use a time-lapse seismic data set from a Steam-Assisted Gravity Drainage producing heavy oil reservoir to demonstrate that mobility change coupled with patchy saturation possibly leads to seismic spectral energy shifting from the baseline to monitor line. Our workflow starts from performing seismic spectral analysis on the targeted reflectivity interface. Then, on the basis of mesoscopic fluid pressure diffusion between patches of steam and heavy oil, poroelastic reflectivity modeling is conducted to understand the shift of the central frequency toward low frequencies after the steam injection. The presented results open the possibility of monitoring mobility change of a partially saturated geological formation from dissipation-related seismic attributes. [ABSTRACT FROM AUTHOR]

ABSTRACT We extend the frequency- and angle-dependent poroelastic reflectivity to systematically analyse the characteristic of seismic waveforms for highly attenuating reservoir rocks. It is found that the mesoscopic fluid pressure diffusion can significantly affect the root-mean-square amplitude, frequency content, and phase signatures of seismic waveforms. We loosely group the seismic amplitude-versus-angle and -frequency characteristics into three classes under different geological circumstances: (i) for Class-I amplitude-versus-angle and -frequency, which corresponds to well-compacted reservoirs having Class-I amplitude-versus-offset characteristic, the root-mean-square amplitude at near offset is boosted at high frequency, whereas seismic energy at far offset is concentrated at low frequency; (ii) for Class-II amplitude-versus-angle and -frequency, which corresponds to moderately compacted reservoirs having Class-II amplitude-versus-offset characteristic, the weak seismic amplitude might exhibit a phase-reversal trend, hence distorting both the seismic waveform and energy distribution; (iii) for Class-III amplitude-versus-angle and -frequency, which corresponds to unconsolidated reservoir having Class-III amplitude-versus-offset characteristic, the mesoscopic fluid flow does not exercise an appreciable effect on the seismic waveforms, but there exists a non-negligible amplitude decay compared with the elastic seismic responses based on the Zoeppritz equation. [ABSTRACT FROM AUTHOR]

Understanding the relationship between compressional and shear wave velocities and their anisotropic characteristics in self-resourcing reservoir shales is of considerable interest for petrophysical, geophysical, and geomechanical applications of unconventional resources exploration and production. We compile laboratory measurements on the ultrasonic velocity of known shale reservoirs worldwide. Their compressional and shear wave velocities propagating in the vertical and horizontal directions can be characterized by a linear model with the coefficients of determination (R2) close to 0.9. The P-to-S-wave velocity ratio in the vertical direction is overall higher than that in the horizontal direction. The P-to-S-wave velocity ratio decreases with decreasing P-wave velocity due to the increasing organic matter content and hydrocarbon-filled porosity. Mineralogy influences the relationship between compressional and shear wave velocities to a minor degree. Carbonate-rich shales have a higher P-to-S-wave velocity ratio than silica-rich shale when the P-wave velocity is lower than 4 km/s and a lower ratio when the P-wave velocity is higher than 5 km/s. By employing anisotropic fluid substitution, we justify that the model derived from the laboratory measurements can apply to shale reservoirs containing gas or volatile oil. We apply the linear models to predict the shear wave velocity in two vertical wells of shale reservoirs. The predictions of shear wave velocity in the targeted reservoirs have less than 3% errors, demonstrating that the proposed linear models, to the first order, can predict shear wave velocity in unconventional shale reservoirs. • P- and S-wave velocity relationships in anisotropic organic shales are characterized with a linear model. • P-to-S wave velocity ratio decreases with decreasing P-wave velocity due to the increasing TOC and porosity. • The linear relationship is applicable for prediction of S-wave velocity in shale reservoir containing volatile oil and gas. [ABSTRACT FROM AUTHOR]

Abstract: In an effort to further advance our understanding of the evolution of the Sulu–Dabie orogen and its complex interactions with the Tanlu fault, and to tackle non-unique geophysical interpretations, we characterize the 3D geological structures and dynamics of the Sulu–Dabie orogen and its environs using various data processing and interpretation of a very large suite of gravimetric, magnetic, magnetotelluric, geothermal, and seismic data. We have modeled regional geothermal field of the lithosphere by incorporating both surface heat flow and Curie-point depths inverted from magnetic anomalies. This gives better constraints on regional thermal lithospheric thicknesses, which are found to be small (mostly between 55 and 95km), conformable to other geophysical results, and supportive of lithospheric de-rooting. From regional geotherms and Bouguer gravity anomalies, we assess depths, temperatures and heat flow of the Moho, and find that mantle contributes a little over 70% of the total surface heat flow. Large differences in thermal lithospheric thicknesses and geothermal activities are found between the Sulu and the Dabie segments of the Sulu–Dabie orogen. These differences result, at least partially, from large vertical differential movement at the lithospheric scale across the Tanlu fault, as seen from both reflection seismic sections and gravity anomalies. Within the Sulu–Dabie orogenic belt, reduction to the pole and 3D analytic signal analysis on magnetic anomalies reveal that positive magnetic anomalies associated with this belt are most due to gneiss, migmatite and Mesozoic granites, whereas ultrahigh-pressure metamorphic zones show weak or negative magnetic anomalies. This interesting magnetic contrast between ultrahigh-pressure metamorphic rocks and surrounding rocks suggests that ultrahigh-pressure metamorphic minerals are either only weakly magnetized, or possibly retrograded and remagnetized over a long time span or in a period of reversed magnetization. High-pressure metamorphic minerals of blueschist facies appear to be less susceptive than ultrahigh-pressure metamorphic minerals. [Copyright &y& Elsevier]