Time-lapse full-waveform inversion (TLFWI) can provide high-resolution information about changes in the reservoir properties during hydrocarbon production and CO2 injection. However, the accuracy of the estimated source wavelet, which is critically important for TLFWI, is often insufficient for fielddata applications. The so-called "source-independent" FWI is designed to reduce the influence of the source wavelet on the inversion results. Here, we incorporate the convolutionbased source-independent technique into a time-lapse FWI algorithm for VTI (transversely isotropic with a vertical symmetry axis) media. The gradient of the modified FWI objective function is obtained from the adjoint-state method. The algorithm is tested on a model with a graben structure and the modified VTI Marmousi model using three time-lapse strategies (the parallel-difference, sequential-difference, and double-difference methods). The results confirm the ability of the developed methodology to reconstruct the localized timelapse parameter variations even for a strongly distorted source wavelet. The algorithm remains robust in the presence of moderate noise in the input data but the accuracy of the estimated time-lapse changes depends on the model complexity.
Abstract Although ultrahigh‐pressure (UHP) metamorphic rocks are present in many collisional orogenic belts, almost all exposed UHP metamorphic rocks are subducted upper or felsic lower continental crust with minor mafic boudins. Eclogites formed by subduction of mafic lower continental crust have not been identified yet. Here an eclogite occurrence that formed during subduction of the mafic lower continental crust in the Dabie orogen, east‐central China is reported. At least four generations of metamorphic mineral assemblages can be discerned: (i) hypersthene + plagioclase ± garnet; (ii) omphacite + garnet + rutile + quartz; (iii) symplectite stage of garnet + diopside + hypersthene + ilmenite + plagioclase; (iv) amphibole + plagioclase + magnetite, which correspond to four metamorphic stages: (a) an early granulite facies, (b) eclogite facies, (c) retrograde metamorphism of high‐pressure granulite facies and (d) retrograde metamorphism of amphibolite facies. Mineral inclusion assemblages and cathodoluminescence images show that zircon is characterized by distinctive domains of core and a thin overgrowth rim. The zircon core domains are classified into two types: the first is igneous with clear oscillatory zonation ± apatite and quartz inclusions; and the second is metamorphic containing a granulite facies mineral assemblage of garnet, hypersthene and plagioclase (andesine). The zircon rims contain garnet, omphacite and rutile inclusions, indicating a metamorphic overgrowth at eclogite facies. The almost identical ages of the two types of core domains (magmatic = 791 ± 9 Ma and granulite facies metamorphic zircon = 794 ± 10 Ma), and the Triassic age (212 ± 10 Ma) of eclogitic facies metamorphic overgrowth zircon rim are interpreted as indicating that the protolith of the eclogite is mafic granulite that originated from underplating of mantle‐derived magma onto the base of continental crust during the Neoproterozoic ( c . 800 Ma) and then subducted during the Triassic, experiencing UHP eclogite facies metamorphism at mantle depths. The new finding has two‐fold significance: (i) voluminous mafic lower continental crust can increase the average density of subducted continental lithosphere, thus promoting its deep subduction; (ii) because of the current absence of mafic lower continental crust in the Dabie orogen, delamination or recycling of subducted mafic lower continental crust can be inferred as the geochemical cause for the mantle heterogeneity and the unusually evolved crustal composition.
Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficiency. However, most existing applications of ML yield only one prediction (i.e., the expectation) for a given input, which may not properly reflect the distribution of the testing data, if it has a shift with respect to that of the training data. The Simultaneous Quantile Regression (SQR) method can estimate the entire conditional distribution of the target variable of a neural network via pinball loss. Here, we incorporate this technique into seismic inversion for purposes of CO2 monitoring. The uncertainty map is then calculated pixel by pixel from a particular prediction interval around the median. We also propose a novel data-augmentation method by sampling the uncertainty to further improve prediction accuracy. The developed methodology is tested on synthetic Kimberlina data, which are created by the Department of Energy and based on a CO2 capture and sequestration (CCS) project in California. The results prove that the proposed network can estimate the subsurface velocity rapidly and with sufficient resolution. Furthermore, the computed uncertainty quantifies the prediction accuracy. The method remains robust even if the testing data are distorted due to problems in the field data acquisition. Another test demonstrates the effectiveness of the developed data-augmentation method in increasing the spatial resolution of the estimated velocity field and in reducing the prediction error.
Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/
In carbon capture and sequestration (CCS), developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO2 propagation during/after injection. We propose an efficient "hybrid" time-lapse workflow that combines physics-based full-waveform inversion (FWI) and data-driven machine- learning (ML) inversion. The developed hybrid methodology simultaneously predicts the variations in velocity and satura- tion and achieves a high spatial resolution in the presence of realistic noise in the data. The method is validated on a syn- thetic CO2 -sequestration model based on the Kimberlina stor- age reservoir in California. The scarcity of the training data with the ML algorithm is addressed by developing a new data- generation technique with physics constraints. The proposed approach synthesizes a large volume of high-quality, physi- cally realistic training data, which is critically important in ac- curately characterizing the CO2 movement in the reservoir.
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