Earth Science Informatics最新文献

A drought is a complex event characterized by low rainfall and has negative implications for agricultural and hydrological systems, as well as for community life. A common meteorological drought index used for drought monitoring and water resource management is the Standardized Precipitation Evapotranspiration Index (SPEI). Using SPEI can assist in predicting drought onset and estimating drought severity. The objective of this research is to assess the accuracy of machine learning models in estimating the SPEI-1 (one-month) index in semi-arid climates. To achieve this goal, the data will be analyzed using remote sensing parameters, a worldwide database, and meteorological station information. SPEI-1 was predicted in Tabriz, Iran, between 1990 and 2022 using multilayer perceptron (MLP) and random forest (RF) techniques combined with genetic algorithm (GA) methods. The parameters used are average air temperature, average relative humidity, monthly precipitation, wind speed, sunny hours, as well as the one-month standard precipitation index (SPI-1) (from ground data), daily precipitation products from satellites named PERSIANN (PRC-PR) (from remote sensing), and SPEIbase data (from global databases). The results suggest that the use of satellite remote sensing characteristics and global databases has significantly enhanced the precision and efficiency of prediction models. Based on the GA-RF model with an R² of 0.992 and an RMSE of 0.124, it exhibits the best performance among all models in Scenario 1. By combining remote sensing parameters, this study presents an innovative approach to predicting the SPEI index and demonstrates their capabilities in drought management and mitigation.

Satellite cloud image sequences contain rich spatial and temporal information, and forecasting future cloud image sequences is of great significance for meteorological research. Traditional satellite cloud image prediction methods usually ignore nonlinear variations in cloud masses, leading to large errors in prediction results and low prediction efficiency. The use of existing video prediction methods for satellite cloud image sequence prediction also suffers from problems of blurred prediction images and the accumulation of sequence errors. To address these issues, we propose a Multi-feature Extraction and High-resolution Generative Network (MEHGNet) for the prediction of satellite cloud image sequences, which consists of an encoder, a translator, a decoder, and a generator. To learn the spatial features and spatiotemporal dependencies of cloud images, 2D convolution multi-head attention mechanisms and local residue connections are introduced to the encoder and decoder. The generator preserves detailed features and improves the resolution of the predicted images using the generative ability of generative adversarial networks. In addition, a motion-aware loss function is proposed to learn high-level features of motion variations among cloud image sequences. Experiments on satellite datasets demonstrate that the proposed method is superior compared to other prediction methods.

Accurate well log data is critical for subsurface characterisation and decision-making in the petroleum exploration. We explore and compare the effectiveness of three distinct deep leaning (DL) approaches—Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Convolutional Long Short-Term Memory networks—in predicting missing well log data, a common challenge in the data acquired by Energy and Production (E&P) companies. Our analysis revealed the complex, nonlinear relationships present in geophysical logs through correlation matrix and determining the rank of predictor features through Minimum Redundancy Maximum Relevance (MRMR) analysis. To weigh these models, we used real-field wireline log datasets from the Bhogpara oil field of Upper Assam basin. The performance of each model is evaluated through root mean square error, correlation coefficients, mean absolute error and variance between actual and predicted values. The uncertainty of the models was facilitated by Monte Carlo simulation. Deep learning models accurately predicted neutron porosity logs from gamma-ray, resistivity, density, and photoelectric factor logs. The high correlation coefficients during the training (exceeding 0.90) and test (exceeding 0.97) phases illustrated the predictive precision of the DL models. Conv-LSTM consistently outperforms LSTM and Bi-LSTM, indicating the integration of convolutional layers in feature extraction offers a significant advantage in capturing intricate patterns in log data. The research showcases the effectiveness of deep learning architectures in predicting missing logs, a crucial aspect for E&P companies, as log data is vital for decision-making. The study presents a novel method for preserving data integrity and facilitating informed decision-making.

With the advancement of technology and the maturity of various aerial imaging techniques, data proprietors have awareness of the importance of secure protection for remote sensing images. In order to protect sensitive data of images, we propose a secure encoding scheme for compressing remote sensing images to decrease potential risks of data disclosure associated with such images. First, we designed the Sin chaos paradigm for constructing chaotic systems in various dimensions. As a result through relevant experiments, this chaos paradigm demonstrated effective scalability and stability. In addition, DNA transposition methods have been introduced to extend DNA encoding, expanding the range of DNA encoding from 1 to 4 and achieving dynamic selection of DNA transposition methods. This method reduces potential threats that conflict with fixed DNA encoding methods. In addition, in order to ensure the security of symmetric encryption and the efficiency of asymmetric encryption during key transmission, an elliptical curve “ring” key hiding strategy is adopted. Although the key embedding occupies 1.2% of the space in the ciphertext image, data redundancy realizes the implicit transmission of the key, improving the decryption efficiency of remote sensing images. In response to the above research, we propose a secure compression encoding scheme based on Sin chaotic paradigm and DNA transposition to ensure the security of remote sensing images. After cropping the original remote sensing image to a size of 1/16, the original image can still be decrypted. In addition, when the noise attack reaches 0.3, the ciphertext image can also be restored. Performance analysis and experimental data results show that our proposed secure compression encoding scheme has excellent robustness and security.

This comprehensive review paper examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) tools in earthquake engineering, specifically focusing on damage assessment and retrofitting strategies. The paper begins with an introduction to AI and its significance in structural engineering, highlighting the need for advanced methodologies to address seismic challenges. A detailed review of recent applications of ML, Pattern Recognition (PR), and Deep Learning (DL) in earthquake engineering is provided, showcasing their capabilities in surpassing the limitations of traditional models. The advantages of employing these algorithmic methods in damage assessment, retrofitting designs, risk prediction, and structural optimization are discussed extensively. Furthermore, the paper identifies potential research avenues and emerging trends in AI/ML applications for earthquake resilience, while also addressing the challenges and limitations associated with these technologies. Overall, this review paper offers a comprehensive overview of the current state-of-the-art in AI and ML tools for earthquake damage assessment and retrofitting strategies, paving the way for future advancements in seismic resilience engineering.

The current research employed the least absolute shrinkage and selection operator (Lasso) and Elastic-net algorithms to examine their potential utilization in MVT Pb-Zn prospectivity modeling. In training the model, both Elastic-net and Lasso regularization approaches include a penalty term to the loss function. Since this penalty term limits the feature coefficients, the model is motivated to prioritize the most informative features and penalize the less relevant ones. The Varcheh district in western Iran was the source of the geological, geochemical, tectonic, and alteration dataset. We applied stratified 5-fold cross-validation to train the dataset, ensuring consistent and comprehensive performance evaluation across different data subsets. This method improved data utilization and provided more reliable performance estimates by averaging metrics over multiple folds, thereby enhancing the model’s generalization assessment. The hyperparameters were adjusted using random search, quickly finding near-optimal solutions. Our investigation revealed that Elastic-net exhibited superior prediction accuracy and model robustness compared to Lasso. The combination of L1 and L2 regularization in Elastic-net, offers a more adaptable technique than Lasso, which just utilizes L1 regularization. This feature enables Elastic-net to handle scenarios in which there have been correlated predictors successfully.

The dynamics of Sea Surface Temperature (SST) are crucial for maintaining the balance of marine ecosystems. While existing artificial intelligence methods offer powerful tools for SST prediction, they struggle with data sparsity issues. To enhance SST prediction accuracy under sparse data conditions, this study proposes an innovative prediction model: TL-iTransformer. This model is based on the iTransformer architecture and incorporates transfer learning techniques specifically tailored for SST prediction. We begin by extracting SST features from data-rich sea areas (source sea areas) using a transfer learning strategy, integrating these features into the iTransformer model for pre-training. This process imparts the model with a priori knowledge and basic prediction capabilities, enabling it to adapt to data-sparse sea areas (target sea areas). The model is then fine-tuned using domain adaptive techniques to accurately capture the data characteristics and distribution patterns of the target sea area. We conducted a series of experiments using a real SST dataset from the sea area of British Columbia, Canada. The results demonstrate that TL-iTransformer maintains the Mean Absolute Error (MAE) and Mean Squared Error (MSE) within 0.144 and 0.356, respectively, under data sparsity conditions. Additionally, it outperforms four mainstream time-series prediction baseline models as the prediction time span increases. The proposed model can effectively address the issue of SST prediction in situations with sparse data.