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The geothermal gradient in the eastern area of Liaoning Province is very low, but hot springs resources are variable. The reason is not clear till now but leads to the fact that a few strong influence factors can cause imbalances in the results of many prediction algorithms. It can be found as a black-box algorithm, deep learning will obtain a more unbalanced result with the fault influence factors. To tackle this issue, the role of preprocessing during the process of profound learning was enhanced and four comparative experiments were carried out. The results show that compared with the unprocessed experiment, the accuracy rate of the experiment with fully processed data increased by 11.9 p.p., and the area under the curve increased by 0.086 (0.796–0.882). This inspires us that even though the deep learning method can achieve high accuracy in the prediction of geological resources, we still need to pay attention to the analysis and pretreatment of data with expertise according to local conditions.

Over-exploitation of open-pit mines has caused extensive development of geological hazards, posing serious threats to people and buildings in the area. Based on the UAV tilt-tilt photography technology, this paper generates a real-world 3D model of the study area, taking the molybdenum mine of Songshumao in Yangjiajiaozi, Xingcheng City, Liaoning Province as an example, and combines orthophotography, high-precision DEM data and field geological survey research to establish a geohazard interpretation marker for the study area and interpret various types of geohazards such as landslides, landslides, dangerous rocks, ground cracks and unstable slopes in the study area. Based on the hierarchical analysis method combined with the geological environment characteristics, the geological hazard risk evaluation model and index system were established, and five indexes were selected, including geological hazard range, stratigraphic lithology, slope, elevation, vegetation coverage, etc. The study area was divided into high, medium and low risk zones, and the geological hazard risk zoning map was obtained to complete the geological hazard risk evaluation of the study area.

High-resolution geological mapping is an important supporting condition for mineral and energy exploration. However, high-resolution geological mapping work still faces many problems. At present, high-resolution geological mapping is still generated by expert interpretation of survey lines, compasses, and field data. The work in the field is constrained by the weather, terrain, and personnel, and the working methods need to be improved. This paper proposes a new method for high-resolution mapping using Unmanned Aerial Vehicle (UAV) and deep learning algorithms. This method uses the UAV to collect high-resolution remote sensing images, cooperates with some groundwork to anchor the lithology, and then completes most of the mapping work on high-resolution remote sensing images. This method transfers a large amount of field work into the room and provides an automatic mapping process based on the Simple Linear Iterative Clustering-Convolutional Neural Network (SLIC-CNN) algorithm. It uses the convolutional neural network (CNN) to identify the image content and confirms the lithologic distribution, the simple linear iterative cluster (SLIC) algorithm can be used to outline the boundary of the rock mass and determine the contact interface of the rock mass, and the mode and expert decision method is used to clarify the results of the fusion and mapping. The mapping method was applied to the Taili waterfront in Xingcheng City, Liaoning Province, China. In this study, the Area Under the Curve (AUC) of the mapping method was 0.937. The Kappa test result was k = 0.8523, and a high-resolution geological map was obtained.

Geological faults are highly developed in the eastern Liaoning Province in China, where Mesozoic granitic intrusions and Archean and Paleoproterozoic metamorphic rocks are widely distributed. Although the heat flow value in eastern Liaoning Province is generally low, the hot springs are very developed. It is obvious that the faults have significant control over the distribution of hot springs, and traditional methods of spatial data analysis such as WofE (weight of evidence) usually do not take into account the direction of the distribution of geothermal resources in the geothermal forecast process, which seriously affects the accuracy of the prediction results. To overcome the deficiency of the traditional evidence weight method, wherein it does not take the direction of evidence factor into account, this study put forward a combination of the Fry and WofE methods, Fry-WofE, based on geological observation, gravity, remote sensing, and DEM (digital elevation model) multivariate data. This study takes eastern Liaoning Province in China as an example, and the geothermal prospect was predicted respectively by the Fry-WofE and WofE methods from the statistical data on the spatial distribution of the exposed space of geothermal anomalies the surface. The result shows that the Fry-WofE method can achieve better prediction results when comparing the accuracy of these two methods. Based on the results of Fry-WofE prediction and water system extraction, 13 favorable geothermal prospect areas are delineated in eastern Liaoning Province. The Fry-WofE method is effective in study areas where the geothermal distribution area is obviously controlled by the fault. We provide not only a new method for solving the similar issue of geothermal exploration, but also a new insight into the distribution of geothermal resources in Liaoning Province.

The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.

Decision-making departments need more detailed and timely data in order to meet the needs of emergency response. Sichuan province is an area that frequently suffers natural disasters, and many disasters are caused by rainfall. This study establishes a decision support system (DSS) based on geoprocessing (GP) services, which can locate the region that overran the rainfall threshold and provide the population or property analysis, query, map plot, and path analysis functions. Most of the functions of the system are developed on the basis of geoprocessing services. This paper uses the warning–judgment module as an example to introduce the structure and function of the DSS system. The system satisfies the demands of real-time data acquisition, calculation, analysis, and presentation.