Computers & Geosciences最新文献

Well logs are essential tools for understanding the characteristics of subsurface formations and exploring petroleum resources. However, well logs are often missing randomly due to cost constraints, instrument failures, or other factors. Many methods have been developed for completing missing well logs, but these methods are all based on fixed types of known well-log inputs to predict specific types of missing logs. This fixed input–output mode severely limits the application of these methods in actual data, where the known and missing well-log types are often varying. To address this problem, we propose a hybrid deep learning method with two heads of heterogeneous graph neural network (HGNN) and fully connected network (FCN) to achieve mutual prediction among multiple types of well logs. It can adaptively use all known well logs to predict any missing well logs, achieving a very flexible and practical well log completion function of using what you have to complete what you are missing. Specifically, the HGNN head infers the inter-relationships among multiple well logs to predict normalized logs that contain detailed information, which achieved by using multiple independent kernels to extracting and aggregating the features of the multiple logs. The FCN head estimates the global statistics of the predicted logs, including means and standard deviations, for de-normalizing the well logs estimated by the HGNN head. Both the HGNN and FCN heads are trained simultaneously by a hybrid loss function to ensure the consistency of their predictions. Furthermore, we present an adaptive training strategy that leverages all well logs, including those with missing segments. We demonstrate the capability of our model using four well logs: gamma ray (GR), bulk density (RHOB), neutron porosity (NPHI), and compressional waves sonic (DTC). Theoretically, the model trained on other logs can also predict each other. Our approach yields high Pearson correlation coefficients and small root mean square error on a dataset obtained from an offshore North Sea field near Norway, demonstrating the efficacy of our proposed technique.

In this paper, we introduce pySimFrac , an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. pySimFrac allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, pySimFrac includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present pySimFrac along with integration examples and discuss the ability to extend pySimFrac from a single complex fracture to complex fracture networks.

The molecular ratio indices of biological markers (biomarkers), such as the Carbon Preference Index (CPI) or P_aq, are frequently used as proxies for paleoclimatic and palaeoecological conditions. These indices are regularly extracted from the relative abundances of target molecules detected by a Gas Chromatography analyzer with a Flame Ionization Detector (GC-FID). Despite their use in biogeochemical studies for over a half-century, it remains common procedure to quantify the abundance of individual compounds by manual integration of chromatogram peaks (i.e., interpret baselines visually and characterize peaks by hand), which is time consuming and can lead to inconsistent results. Here, we introduce a new MATLAB package (Chroma) for the automatic detection and integration of standard-referenced biomarker abundances and the calculation of a variety of established hydrocarbon indices commonly reported in the published literature. The algorithm identifies the detector response timing of specific target peaks in a sample chromatogram by cross-referencing to a standard (e.g., Mix-A6, Schimmelmann, Indiana University Bloomington), then calculates the peak areas for an approximation of molecular abundance. This new toolkit for automatic and rapid integration of GC-acquired data provides a consistent and reproducible approach for the calculation of hydrocarbon indices and offers a standardized inter-laboratory platform for data comparisons and exchange. We validate the utility of the Chroma package with the chromatograms of plant wax n-alkanes, a widely used proxy for ecology and hydrology, from six stratigraphic sections in the Tibetan Plateau. Chroma is an effective tool for efficient data processing and will continuously evolve to accommodate extended uses in related areas of biomarker research beyond n-alkanes.

When hindcasting wave fields of storm events with wave models, the quality of the results strongly depends on several factors such as the computational grid resolution and the accuracy of the atmospheric forcing. In an effort to minimize the uncertainties involved in this process, three ocean wave and surface wind ensemble hindcast systems were established using the Simulating WAves Nearshore (SWAN) model and Weather Research and Forecasting Model (WRF) with atmospheric data from ERA5 Ensemble of Data Assimilation (EDA) as well as deterministic high-resolution systems. We established three ensemble systems to tackle this: SWN-ERA5EDA using ERA5-EDA global reanalyses winds, SWN-WRFERA5 employing WRF downscaling of ERA5-EDA, and SWN-WRFPPar incorporating WRF multi-physics runs for dynamical downscaling. This study focuses on extreme events in southern Brazil during an austral winter, highlighting the importance of increasing the resolution of ocean wave and surface wind data to provide more accurate and reliable forecasts for coastal and marine activities. Our analyses revealed that atmospheric downscaling performed with WRF not only increased the ensemble spread by significant amounts but also enhanced the sharpness of the wave ensemble hindcast compared with those based solely on the ERA5 EDA. Specifically, for the Rio Grande buoy location, the significant wave height ($H_s$) from the SWN-WRFERA5 system showed an increase of 0.5 over SWN-ERA5, and $H_s$ from the SWN-WRFPPar system increased by 0.6. Additionally, the wave peak period ($T_p$) for both SWN-WRFERA5 and SWN-WRFPPar systems experienced an increase of 1.2 compared to SWN-ERA5. Additionally, the ensemble produced with the WRF multi-physics approach captured peaks in the significant wave height registered by the buoy that were not reproduced by other ensemble systems, demonstrating an improvement in predictive accuracy, despite presenting a smaller correlation between spread and strong localized wave variations. Besides quantifying the hindcast error, the methodology presented in this work also offers a way to generate alternative and improved representations of past extreme events. This approach significantly contributes to our ability to sample recent climatic conditions and expand the dataset for statistical analyses, which is especially valuable for ocean and coastal engineering projects. This study underscores the critical role of enhancing computational and methodological approaches in wave modeling for better understanding and mitigating the impacts of extreme weather events on coastal and oceanic regions.

Accurate measurements of soil thickness are crucial for assessing landslide susceptibility, slope stability, and soil conservation. However, there is a relative scarcity of research on the spatial distribution of soil thickness in areas with complex terrains, such as alpine canyon regions. Given this research gap, the aim of this study is to develop a reliable method for predicting soil thickness in these regions. In this study, the Baihetan Reservoir area (China), characterized by typical alpine canyon regions, was selected as the research site. The slope index (SI) and slope (S) factor, in addition to other factors, were used to predict soil thickness. Subsequently, the random forest (RF) model and its version based on the whale optimization algorithm (WOA) were used to model soil thickness. The results showed that compared to the other models, the WOA-RF model, which considers the slope index factor, performed best in 100 tests, achieving the highest coefficient of determination (R² = 0.93) and the lowest root mean square error (RMSE = 5.6 m). Furthermore, the soil thickness data from the WOA-RF (SI) model displayed the highest congruence with the soil thickness data obtained from environmental noise measurements. Therefore, predicting soil thickness in alpine canyon regions by comprehensively considering environmental variables and using the WOA-RF model is feasible. The resulting soil thickness maps can serve as key fundamental inputs for further analysis.

The fractional viscoacoustic/viscoelastic wave equation, which accurately quantifies the frequency-independent anelastic effects, has been the focus of seismic industry in recent years. The pseudo-spectral (PS) method stands as one of the most widely used numerical methods for solving the fractional wave equation. However, the PS method often suffers from low accuracy and efficiency, particularly when modeling wave propagation in heterogeneous media. To address these issues, we propose a novel and efficient fractional finite-difference (FD) method for solving the wave equation with fractional Laplacian operators. This method develops an arbitrary high-order FD operator via the generating function of our fractional FD (F-FD) scheme, enhancing accuracy with L2-optimal FD coefficients. Similar to classic FD methods, our F-FD method is characterized by straightforward programming and excellent 3D extensibility. It surpasses the PS method by eliminating the need for Fast Fourier Transform (FFT) and inverse-FFT (IFFT) operations at each time step, offering significant benefits for 3D applications. Consequently, the F-FD method proves more adept for wave-equation-based seismic data processes like imaging and inversion. Compared with existing F-FD methods, our approach uniquely approximates the entire fractional Laplacian operator and stands as a local numerical algorithm, with an adjustable F-FD operator order based on model parameters for enhanced practicality. Accuracy analyses confirm that our method matches the precision of the PS method with a correctly ordered F-FD operator. Numerical examples show that the proposed method has good applicability for complex models. Finally, we have carried out reverse time migration on the Marmousi-2 model, and the imaging profiles indicate that the proposed method can be effectively applied to seismic imaging, demonstrating good practicability.

In regions experiencing ongoing aseismic deformation, fault’s Activity Rate (AR) calculations often lead to an overestimation of hazard potential. This study proposes a novel methodology that integrates the Seismic Coupling Coefficient (SCC) into the fault Seismic Activity Rate (SAR) calculation process to discriminate seismic moment rates. We introduce FaultQuake, an open-source Python tool equipped with a Graphical User Interface (GUI), designed to implement this methodology and accurately estimate SAR for faults. These activity rates can be included in Probabilistic Seismic Hazard Assessment (PSHA) frameworks and assist in differentiating the seismic and aseismic deformation. FaultQuake also presents an innovative embedded workflow, the Optimal Value Computation Workflow (OVCW), based on Conflation of Probabilities (CoP), for calculating the Maximum Magnitude ($M_{max}$) from the empirical relationships and the observed magnitudes ($M_{obs}$) assigned to a single fault. This enhancement improves the estimation of seismic moment rates and the SAR calculation process. FaultQuake outputs are provided in the format of OpenQuake engine input files to facilitate the PSHA process. We present a sample case study focusing on the PSHA of a region in southern Iran characterized by a substantial aseismic deformation to illustrate the practical application of FaultQuake in seismic hazard analysis. Peak Ground Acceleration (PGA) maps for 10% and 2% Probabilities of Exceedance (PoE) are plotted to compare PGAs with and without applying the FaultQuake algorithm. The results provide an enhanced view of the area’s hazard with mitigation of the overestimation, resulting in more representative hazard maps. The source codes of FaultQuake are available at the FaultQuake GitHub repository, contributing to the computer and geoscience community.

Frequency–time conversion is a crucial step in grounded electrical-source transient electromagnetic response calculation, and the performance of the algorithm is directly related to the overall accuracy and speed of forward modeling. In mainstream algorithms, algorithms with high accuracy often have slow computation speed while algorithms with high efficiency have unsatisfactory accuracy, especially when facing inversion problems that are difficult to meet requirements. This paper introduces three inverse Laplace transform algorithms for this problem: the Gaver–Stehfest algorithm, the Euler algorithm, and the Talbot algorithm. The performance of each algorithm in forward modeling was analyzed using half-space and layered models, and the optimal selection schemes for algorithm weight coefficients were provided. The numerical calculation results show that the Gaver–Stehfest algorithm has a unique advantage in computational efficiency, while the Talbot algorithm and Euler algorithm meet the accuracy requirements. After considering both accuracy and efficiency, the Talbot algorithm is selected to replace conventional algorithms for calculation of grounded electrical-source transient electromagnetic forward modeling. In addition, this paper combines the characteristics of the Gaver–Stehfest algorithm and the Talbot algorithm to implement an adaptive hybrid algorithm. This algorithm uses the Gaver–Stehfest algorithm for forward modeling in the early times and the Talbot algorithm to compensate for the decrease in accuracy in the later times. Through the comparison of forward modeling calculations, it can be seen that the hybrid algorithm proposed in this paper fully utilizes the advantages of both algorithms. The hybrid algorithm greatly improves computational speed while meeting accuracy requirements, and has significant advantages over conventional algorithms.

Many geophysical imaging applications, such as full-waveform inversion, often rely on high-performance computing to meet their demanding computational requirements. The failure of a subset of computer nodes during the execution of such applications can have a significant impact, as it may take several days or even weeks to recover the lost computation. To mitigate the consequences of these failures, it is crucial to employ effective fault tolerance techniques that do not introduce substantial overhead or hinder code optimization efforts. This paper addresses the primary research challenge of developing fault tolerance techniques with minimal impact on execution and optimization. To achieve this, we propose DeLIA, a Dependability Library for Iterative Applications designed for parallel programs that require data synchronization among all processes to maintain a globally consistent state after each iteration. DeLIA efficiently performs checkpointing and rollback of both the application’s global state and each process’s local state. Furthermore, DeLIA incorporates interruption detection mechanisms. One of the key advantages of DeLIA is its flexibility, allowing users to configure various parameters such as checkpointing frequency, selection of data to be saved, and the specific fault tolerance techniques to be applied. To validate the effectiveness of DeLIA, we applied it to a 3D full-waveform inversion code and conducted experiments to measure its overhead under different configurations using two workload schedulers. We also analyzed its behavior in preemptive circumstances. Our experiments revealed a maximum overhead of 8.8%, and DeLIA demonstrated its capability to detect termination signals and save the state of nodes in preemptive scenarios. Overall, the results of our study demonstrate the suitability of DeLIA to provide fault tolerance for iterative parallel applications.

Passive surface-wave methods using dense seismic arrays have gained growing attention in near-surface high-resolution imaging in urban environments. Deep learning (DL) in the extraction of dispersion curves and inversion can release a tremendous workload brought by dense seismic arrays. We presented a case study of imaging shear-wave velocity (Vs) structure and detecting low-velocity layer (LVL) in the Hangzhou urban area (eastern China). We used traffic-induced passive surface-wave data recorded by dense linear arrays. We extracted phase-velocity dispersion curves from noise recordings using seismic interferometry and multichannel analysis of surface waves. We adopted a convolutional neural network to estimate near-surface Vs models by inverting Rayleigh-wave fundamental-mode phase velocities. To improve the accuracy of the inversion, we utilized the sensitivities to weight the loss function. The average root mean square error from the weighted inversion is 46% lower than that from the unweighted DL inversion. The estimated pseudo-2D Vs profiles correspond to the velocities obtained from downhole seismic measurements. Compared with an investigation on the same survey area, our inversion results are more consistent with the Vs provided by downhole seismic measurements within 50–60 m where the LVL exists. The trained neural network successfully identified that the LVL is located at 50–60 m deep. To check the applicability of the trained neural network, we applied it to a nearby passive surface-wave survey and the inversion results agree with the existing investigation results. The two applications demonstrate the accuracy and efficiency of delineating near-surface Vs structures with the LVL from traffic-induced noise using the DL technique. The DL inversion has great potential for monitoring subsurface medium changes in urban areas.