Computers & Geosciences最新文献

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.

Accurately identifying grain types in thin sections of sandy sediments or sandstones is crucial for understanding their provenance, depositional environments, and potential as natural resources. Although traditional computer vision methods and machine learning algorithms have been used for automatic grain identification, recent advancements in deep learning techniques have opened up new possibilities for achieving more reliable results with less manual labor. In this study, we present Trans-SedNet, a state-of-the-art dual-modal Vision-Transformer (ViT) model that uses both cross- (XPL) and plane-polarized light (PPL) images to achieve semantic segmentation of thin-section images. Our model classifies a total of ten grain types, including subtypes of quartz, feldspar, and lithic fragments, to emulate the manual identification process in sedimentary petrology. To optimize performance, we use SegFormer as the model backbone and add window- and mix-attention to the encoder to identify local information in the images and to best use XPL and PPL images. We also use a combination of focal and dice loss and a smoothing procedure to address imbalances and reduce over-segmentation. Our comparative analysis of several deep convolution neural networks and ViT models, including FCN, U-Net, DeepLabV3Plus, SegNeXT, and CMX, shows that Trans-SedNet outperforms the other models with a significant increase in evaluation metrics of mIoU and mPA. We also conduct an experiment to test the models' ability to handle dual-modal information, which reveals that the dual-modal models, including Trans-SedNet, achieve better results than single-modal models with the extra input of PPL images. Our study demonstrates the potential of ViT models in semantic segmentation of thin-section images and highlights the importance of dual-modal models for handling complex input in various geoscience disciplines. By improving data quality and quantity, our model has the potential to enhance the efficiency and reliability of grain identification in sedimentary petrology and relevant subjects.

In Naturally Fractured Reservoirs (NFR) diffuse fractures arrangement results from mechanical stratigraphy and tectonic history during failure. Thus, modelling Discrete Fracture Network (DFN) requires to understand and to account for fracture relationships at bed-interface (abutment or crosscutting) in 3D through time (loading path). However, sampling fractures data meaningfully in subsurface has always been a challenge for geologist due to data scarcity.

To better understand and forecast fracture networks in stratified rocks, we study outcrops with a focus on geometric relationships between stratigraphic interfaces and fractures. This paper presents an original python toolbox called FracAbut. It is composed of 1 main and 2 auxiliary codes that quantify the geometric relation between fractures and stratigraphic interfaces from 1D (wells, scan-line) and 2D (digital image, photographs data). We calculate the Interface Impedance (II) that accounts for fracture abutment (crossing or not), persistence (single- or multi-bed) and propagation polarity (upward or downward). For each stratigraphic interface FracAbut provides information on fractures (type, number) and interface sensitivity (coupling strength).

First, we apply FracAbut on synthetic case studies, then, on naturally fractured and stratified carbonates in Berat, Albania. Using both 1D scan-line and 2D outcrop photograph, we show that i) a mechanical interface can have different coupling above and below based on propagation polarity, ii) FracAbut results can give useful insight on fracture transmissivity, iii) FracAbut is fast and efficient to quantify fracture patterns and classify mechanical interface impact; iv) they are no relation between bed thickness and fracture propagation.

Machine learning (ML) and deep learning (DL) techniques have recently shown encouraging performance in recognizing metal-vectoring geochemical anomalies within complex Earth systems. However, the generalization of these techniques to detect subtle anomalies may be precluded due to overlooking non-stationary spatial structures and intra-pattern local dependencies contained in geochemical exploration data. Motivated by this, we conceptualize in this paper an innovative algorithm connecting a DL architecture to a spatial ML processor to account for local neighborhood information and spatial non-stationarities in support of spatially aware anomaly detection. A deep autoencoder network (DAN) is trained to abstract deep feature codings (DFCs) of multi-element input data. The encoded DFCs represent the typical performance of a nonlinear Earth system, i.e., multi-element signatures of geochemical background populations developed by different geo-processes. A local version of the random forest algorithm, geographical random forest (GRF), is then connected to the input and code layers of the DAN processor to establish nonlinear and spatially aware regressions between original geochemical signals (dependent variables) and DFCs (independent variables). After contributions of the latter on the former are determined, residuals of GRF regressions are quantified and interpreted as spatially aware anomaly scores related to mineralization. The proposed algorithm (i.e., DAN‒GRF) is implemented in the R language environment and examined in a case study with stream sediment geochemical data pertaining to the Takht-e-Soleyman district, Iran. The high-scored anomalies mapped by DAN‒GRF, compared to those by the stand-alone DAN technique, indicated a stronger spatial correlation with locations of known metal occurrences, which was statistically confirmed by success-rate curves, Student's $t$‒statistic method, and prediction-area plots. The findings suggested that the proposed algorithm has an enhanced capability to recognize subtle multi-element geochemical anomalies and extract reliable insights into metal exploration targeting.

Digital watermarking technology plays a crucial role in securing trajectory data. However, as trajectory data usage scenarios continue to expand, the security requirements for it have changed from a single copyright protection to one that takes into account data integrity. Existing digital watermarking algorithms for trajectory data can only choose between implementing copyright protection or ensuring integrity, unable to simultaneously achieve both functionalities. This limitation impedes the sharing and utilization of trajectory data. A dual watermarking algorithm that combines robust and fragile watermarking was innovatively proposed to solve this problem based on the geometric domain. Firstly, a set of feature points is extracted from the trajectory, and the farthest point pair of the minimum convex hull of the feature points is set as fixed points. The robust watermark is then embedded in the angles constructed by the feature points and the fixed points using quantization index modulation. Meanwhile, the trajectory points are grouped based on the angle and distance ratio constructed from the trajectory points to the fixed points. In each group, the spatiotemporal attributes of the trajectory points are mapped to the fragile watermark, which is then embedded into the distance ratios constructed by the trajectory points. Experimental results show that the proposed algorithm achieves both copyright protection and integrity verification for trajectory data and exhibits stronger robustness and tampering localization ability. This research can provide security and privacy protection for trajectory data and contribute positively to the application of trajectory data.