Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.

To manage multidimensional point data more efficiently, this paper presents an improvement, called HD-tree, of a previous indexing method, called D-tree. Both structures combine quadtree-like partitioning (using integer shift operations without storing internal nodes, but only leaves) and hash tables (for searching for the nodes stored). However, the HD-tree follows a brand-new decomposition strategy, which is called half decomposition strategy. This improvement avoids the generation of nodes containing only a small amount of data and the sequential search of the hash table, so that it can save storage space while having faster I/O and better time performance when building the tree and querying data. The results demonstrate convincingly that the time and space performance of HD-tree is better than that of D-tree regardless of uniform or uneven data, which are less affected by data distribution.

Logging data play an important role in resource exploration, but in actual production, the logging attributes are often distorted or missing, which affects the geological work based on logging data. In this paper, a multivariate feature fusion model is proposed to address the problem of insufficient extraction of the intrinsic correlation between logging attributes in the traditional logging curve reconstruction method. Analogous to the method of extracting texture features in images, the Gray-Level Co-occurrence Matrices (GLCM) and Gradient Co-occurrence Matrices (GCM)are used to extract some kind of co-occurrence relationship that inherently exists between attribute values and trends, the fused features are fed into the BiLSTM network as a priori features in conjunction with the original attribute features, and the missing data are reconstructed using the trained model. The purpose of using this hybrid architecture is to extract the correlations between logging attributes of human interest in a previous step of network training, and to translate this type of important information into a form that can be learnt by the neural network, helping the network to learn more directly about the effective features in the logging data. In order to verify the effectiveness of the model, this paper uses some common evaluation metrics to assess the feature extraction results and model effectiveness. The experimental results of the test boreholes show that the reconstruction results of this paper's model compared with some common machine learning models (XGBoost, RNN, LSTM, BILSTM) are improved up to 33.18% and 44.12% in accuracy (MAE), and 21.67% and 19.85% in relevance (PCC), which has a practical engineering has some application value.