Earth Science Informatics最新文献

Combining the advantages of cryptography and digital watermarking, commutative encryption and watermarking (CEW) addresses the limitations of traditional information security technologies by simultaneously ensuring security and confirming copyright ownership. Existing CEW algorithms for vector geographic data cannot simultaneously meet the requirements of lossless and applicability to all types of vector geographic data. This investigation proposes a lossless CEW algorithm for all types vector geographic data. In the encryption scheme, all coordinate points are stored in a one-dimensional set for permutation encryption. This procedure is applicable to all types of vector geographic data. Then, the original coordinates are replaced with the encrypted coordinates according to the original spatial structure. Since encryption preserves the size of coordinate values, they can be gridded after normalization to ensure compatibility between encryption and watermarking. Subsequently, a characteristic matrix is generated by conducting singular value decomposition on the coordinate values within the grid. Finally, the XOR operation is executed between the encrypted watermark information and this matrix to complete the construction of the zero watermark. Experiments demonstrate that the encryption scheme can yield favorable encryption outcomes with just one scrambling, and the efficiency is greatly improved. The watermarking scheme is robust against most attacks on vector geographic data.

This paper aims to improve porosity estimation in complex carbonate reservoirs by proposing a hybrid CRNN deep learning model. The objectives include addressing the challenges associated with porosity estimation in heterogeneous carbonate reservoirs and evaluating the performance of the CRNN model in accurately predicting porosity based on well-log data. The overall approach involves integrating CNN and RNN architectures within the CRNN model to effectively extract and combine relevant information from well logs. The model is trained using a dataset consisting of well-log and core analysis data from an Iranian carbonate oil field. Well-log data is used as the input including GR, DT, RHOB, LLD, and NPHI for model training, while core data is utilized for model validation. The model's performance is compared with the traditional MLP model in terms of accuracy and generalization. The proposed hybrid CRNN model demonstrates superior performance in predicting porosity values at new locations where only well-log data are available. It outperforms conventional neural network models, as evidenced by the significant improvement in the correlation coefficient between the model predictions and core data (from 0.67 for the MLP model to 0.98 for the CRNN model). The CRNN model's ability to capture complex spatial dependencies within heterogeneous carbonate reservoirs leads to more accurate porosity estimations and valuable insights into reservoir characterization. This paper presents novel and additive information to the existing body of literature in the petroleum industry. The hybrid CRNN model, combining CNN and RNN architectures, offers a unique approach to porosity estimation in complex carbonate reservoirs. By effectively integrating spatial and temporal patterns from well-log data, the model demonstrates higher accuracy rates and improved generalization capabilities. The findings contribute to the state of knowledge by providing a robust and efficient tool for accurate porosity prediction, which can assist in reservoir characterization and enhance decision-making in the petroleum industry.

Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6 months. An appropriate set of predictors was selected based on numerous climate indices and neighbor station data for the period 1980–2020. We developed an adapted deep learning pipeline for both short- and long-term analysis. The predicted rainfall was verified against the observed data using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficients. The results showed that our model generally captured well observed data reflected by low error (MAE and RMSE < 0.2) and high correlation (at 0.8–0.9) for all climatic sub-regions. For the leadtimes of 1–3 months, the rainfall predictionsmade using climate indices as predictors were outperformed by those using neighbor stations data; while for longer leadtimes (4–6 months), the climate indices themselve were able to improve the performance. The rainfall predictions of our methods on all three lead times climatological predictions by factoring additional values. However, there is room for improvement in predicting extreme and abrupt shifts in time series patterns.

Water classification in Synthetic Aperture Radar (SAR) images is an ongoing area of research, which has implications in environmental monitoring and water resource management. Adaptive threshold algorithms provide a fast, reliable and efficient way to perform automated water classification, but users often lack awareness on selecting the best algorithm for their specific application. This paper presents a comprehensive assessment of adaptive threshold algorithms for water delineation applied to L- and C-band SAR backscatter images. We introduce a novel approach for dynamic selection of windows within a SAR image to determine optimum thresholds on sigma naught values. A comparison of five threshold-determination techniques is performed which include Otsu, Kittler and Illingworth (KI), Gaussian Mixture Model (GMM), Quality Index (QI) and Gamma Maximum Likelihood Estimation (GMLE) algorithms. We observed that, for L-band SAR data, convex hull approach produced better kappa coefficient value with GMM, KI and GMLE algorithms. However, for C-band SAR, kappa coefficients were highest for convex hull method with GMM, KI, QI and GMLE approaches and noticeably higher (> 0.89) when compared to split window approach. Our analysis indicates that the proposed convex hull method for window selection performs better in both L- and C-band SAR images. The results of our analysis will help users in identifying the best adaptive algorithm for water delineation in L- and C-band SAR images.

Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models.

The escalating issue of land subsidence poses a critical threat to the economic prosperity of Indonesia’s North Coast in Central Java. This recurring phenomenon intensifies annual tidal floods, posing a severe threat to the infrastructure, buildings, coastal zones, land quality, and the livelihoods of local communities. Effective monitoring of land subsidence rates is essential to mitigate these impacts and implement pre-emptive measures. This study addresses this challenge by employing a two-pronged approach: measuring subsidence rates and assessing susceptibility. Over six years (2016–2021), the research utilizes SAR Sentinel-1 data coupled with machine learning algorithms to achieve these goals. The subsidence rates are generated by the time series InSAR SBAS method. Land subsidence susceptibility assessment uses algorithms such as Random Forest (RF), Gradient Boosted Trees (GTB), Classification and Regression Trees (CART), Support Vector Machine (SVM), Decision Trees with Bagging Method (MD), and K-Nearest Neighbours (KNN). An exhaustive assessment utilizing K-fold cross-validation, incorporating five folds with an 80% training and 20% validation split, effectively facilitates the identification of the model exhibiting the highest accuracy. The findings reveal significant spatial variations in land subsidence rates. Semarang, Pekalongan, and Jepara experienced the highest rates (ranging from − 13 cm/year to -5 cm/year) based on SAR Sentinel-1 data. Machine learning model evaluation yielded Overall Accuracy values of 0.761 (RF), 0.766 (GTB), 0.65 (CART), 0.456 (SVM), 0.359 (KNN), and 0.541 (MD). Based on this analysis, the RF and GTB algorithms are recommended for mapping land subsidence susceptibility. Additionally, the study identified influential factors, with distance from boreholes being the most significant influence. Other notable variables are distance to rivers, rainfall, wetness index, proximity to faults, and distance from residential areas. These valuable insights offer significant benefits to decision-makers and stakeholders, including local governments, urban planners, and disaster management agencies. These findings serve as a foundation for developing a comprehensive policy framework and strategic measures to address land subsidence in this critical region.

Predicting facies and petrophysical properties along and between wells is challenging in carbonate reservoir modeling. In the Nullipore carbonate reservoir, Ras Fanar field, depositional and long-term diagenetic processes result in a high degree of heterogeneity and complex distribution of facies, which in turn affect the reservoir quality. This provides a significant obstacle to building accurate geological models. This study integrates thin sections, routine core analyses, and well logging data to overcome such difficulties and model the Nullipore carbonate facies and permeability. The detailed petrographic analysis revealed the existence of seven microfacies in the reservoir, which are summed up into three facies associations (FAs), each of which represents a specific reservoir rock type (RRT): (1) supratidal FA, (2) intertidal FA, and (3) shallow subtidal FA. The three FAs were correlated with the gamma-ray logs to create facies logs for the studied wells, which were further populated via the Truncated Gaussian Simulation method. Cross-validation was used to evaluate the model's accuracy. The analysis of the available core data infers that the three RRTs are prospective and have a wide permeability distribution. However, RRT3 constitutes the best reservoir quality. The sedimentological analysis revealed that the long-term diagenetic events, involving the dolomitization of limestone and the dissolution of allochems have a major role in improving the pore connectivity and permeability of the reservoir. Fracture characterization discloses that fractures play a significant role in fluid storage and migration. Three Machine Learning (ML) models, including Adaptive boosting (AdaBoost), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were developed to integrate the RRTs, porosity, and permeability to improve permeability prediction. Statistical analysis revealed that the XGB model outperforms other models and exhibits the highest prediction performance. The present study provides further insights into the characterization and modeling of facies and permeability of complex carbonate reservoirs. It can be applied in similar geological settings to better interpretation of depositional and diagenetic controls on reservoir quality assessment and aid in the field development plan.

Advances in the Internet of Things (IoT) and underwater communications have led to extensive research in academia and industry in recent years to implement underwater applications. The successful operation of these applications depends on the use of efficient communication protocols and solutions. Given the dynamic and bandwidth-limited nature of underwater communications, the use of condition-aware routing techniques can mitigate some of these limitations. In this paper, we propose a multi-objective path computation approach for underwater IoT networks that aims to balance energy consumption and maximize network throughput. The proposed mechanism leverages the benefits of Software-Defined Networking (SDN) architecture to collect information about the coordinates of underwater nodes and then calculates the optimal paths to the destination node using a multi-objective mathematical model. Once the paths are calculated, the underwater nodes send data packets towards the destination based on the calculated paths. Simulation results demonstrate that the proposed solution increases the throughput of the network while balancing energy consumption compared to baseline methods.

Currently, the Internet of Things (IoT) is in a premature phase. Although it is growing at a steady pace, there is still a need for further research in the field of security. In this work, the Fujian Province was selected as the study area. The climate, parent material and topographic information of the area were obtained, and the soil-landscape quantitative model was used to quantitatively obtain the relationship between the attributes of coastal sand and gravel soil. On the basis of soil type map, according to the difference of soil type elevation distribution, further predict the soil type distribution and make a map. The results show that the method can achieve more than 80% coincidence with the survey results on the scale of soil digital mapping and can make up for the missing areas of the survey.

Geographical data acquisition in Antarctic regions is challenging due to the lack of human habitation, harsh environmental conditions, and limited accessibility. This research explores and evaluates the capability of two crowdsourcing platforms in mapping facilities across Antarctic regions. The study presents crowdsourcing projects related to polar regions in the literature. The methodology section outlines the data acquisition techniques employed by Flickr and Happywhale, and the spatial evaluation methods applied to the collected data. In the implementation and results section, the spatiotemporal potential of the data obtained from the two identified crowdsourced platforms is assessed, and the results based on spatial statistical methods are compared. In the discussion and conclusion section, the contribution of the two identified crowdsourced platforms to mapping activities is evaluated in terms of spatial, temporal, and content differences. This study reveals that Happywhale offers data with higher spatial consistency, considering seasonal representation and spatial autocorrelation. Additionally, content restrictions and reliance on GPS enhance spatial accuracy in Happywhale. At the same time, the liberation of data production leads to lower quality but increased quantity, diversity, and spatial coverage, as observed in Flickr. By comparing two crowdsourced platforms, this study enhances data acquisition and evaluation potential in Antarctica.