Quantitative Geoscience and Geological Big Data Development： A Review

After long-term development, mathematical geology has today become an independent discipline. Big Data science, which has become a new scientific paradigm in the 21st century, gives rise to the geological Big Data, i.e. mathematical geology and quantitative geoscience. Thanks to a robust macro strategy for big data, China＇s quantitative geoscience and geological big data＇s rapid development meets present requirements and has kept up with international levels. This paper presents China＇s decade-long achievements in quantitative prediction and assessment of mineral resources, geoscience information and software systems, geological information platform development, etc., with an emphasis on application of geological big data in informatics, quantitative mineral prediction, geological environment and disaster management, digital land survey, digital city, etc. Looking ahead, mathematical geology is moving towards ＂Digital Geology＂, ＂Digital Land＂ and ＂Geological Cloud＂, eventually realizing China＇s grand ＂Digital China＂ blueprint, and these valuable results will be showcased on the international academic arena.

Alternative 3D Modeling Approaches Based on Complex Multi-Source Geological Data Interpretation

Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.

Fractal Method for Statistical Analysis Geological Data

This paper establishes the phase space in the light of spacial series data , discusses the fractal structure of geological data in terms of correlated functions and studies the chaos of these data . In addition , it introduces the R/S analysis for time series analysis into spacial series to calculate the structural fractal dimensions of ranges and standard deviation for spacial series data -and to establish the fractal dimension matrix and the procedures in plotting the fractal dimension anomaly diagram with vector distances of fractal dimension . At last , it has examples of its application .

Accessing Multi-Source Geological Data through Network in MORPAS Software System

MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical, geochemical and remote sensing data. It overlays geological database management, geological background and geological abnormality analysis, image processing of remote sensing and comprehensive abnormality analysis, etc.. It puts forward an integrative solution for the application of GIS in basic-level units and the construction of information engineering in the geological field. As the popularization of computer networks and the request of data sharing, it is necessary to extend its functions in data management so that all its data files can be accessed in the network server. This paper utilizes some MAPGIS functions for the second development and ADO (access data object) technique to access multi-source geological data in SQL Server databases. Then remote visiting and congruous management will be realized in the MORPAS system.

Development of Geological Data Warehouse

Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in different levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis always needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.

Progress of Geological Survey Using Airborne Hyperspectral Remote Sensing Data in the Gansu and Qinghai Regions

Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.

Image Processing on Geological Data in Vector Format and Multi-Source Spatial Data Fusion

The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.

STUDY ON EXTRACTING METHODS OF BURIED GEOLOGICAL INFORMATION IN HUAIBEI COAL FIELD

It is discussed features and tbe producing mechanism of buried geological information in geological, geophysical and remote sensing data in Huaibei coal field, and studied the methods extracting buried tectonic and igneous rock information from various geologicaI data using digital image processing techoiques.

The movement age of hidden fault and analysis on width of its effect zone from shallow seismic sounding and drilling data

Field experimental seismic sounding permitted us to obtain optimal shallow seismic reflection sounding parameters.In process of data processing,we obtained a high-qualitative shallow seismic reflection sounding profile by using the techniques such as filtering,edition surgical blanking,prediction deconvolution,fitting static correlation of first arrival time,and velocity analysis.Comprehensive analysis on the information of reflection wave groups along the seismic sounding profile and the stratigraphic and neogeochronological data obtained from many drills near the sounding line reveals that the upper termination of the detected fault zone is located at depth of 75~80 m,in the Middle Pleistocene deposits dated to be about 220 ka BP.The continuity,discontinuity,increasing and decreasing amount of reflection wave groups and change of their configurations,in combination with geological columns of drills,permitted us to know that the width of upper termination of the fault zone is 100 m.It can be inferred from the variation of number of reflection wave groups along the profile that the scarp of hidden fault is 200 m wide and the fault is a synsedimentary active fault in the Early Pleistocene and the early stage of Middle Pleistocene.No tectonic movement,which offset the covering deposits,had occurred since the late stage of Middle Pleistocene.

Delineation of Prospecting Prospect Area Based on Maximum Entropy Model

Taking the Dapingzhang copper-polymetallic deposit in Yunnan Province, China as the research object, the maximum entropy model was used to extract the mining information, and the mineral resource prediction model was established by using the exploration data of the deposit and related regions in this area, so as to determine the prospecting prospect area in the study area. In this paper, the Jacknife analysis module of maximum entropy model is used to quantitatively rank the importance of 39 geochemical element variables, and finally obtain the prospecting prospect map of the study area. The research results show that the Dapingzhang mining area has the potential to find hidden ore in the deep and surrounding areas, and the northern and southern ends and western sides of the rock ore control structural belt in the eastern region of the mining area have good prospecting prospects. The research results provide an important basis for the deployment of follow-up exploration work in the study area, and the maximum entropy model has a good application effect in mineral resources exploration.

A study on geological structure prediction based on random forest method

The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt,which is one of the key areas to study the formation and evolution of the Central Asian orogenic belt.At present,there is a huge controversy over the closure time of the Paleo-Asian Ocean in the Xingmeng orogenic belt.One of the reasons is that the genetic tectonic setting of the Carboniferous volcanic rocks is not clear.Due to the diversity of volcanic rock geochemical characteristics and its related interpretations,there are two different views on the tectonic setting of Carboniferous volcanic rocks in the Xingmeng orogenic belt:island arc and continental rift.In recent years,it is one of the important development directions in the application of geological big data technology to analyze geochemical data based on machine learning methods and further infer the tectonic background of basalt.This paper systematically collects Carboniferous basic rock data from Dongwuqi area of Inner Mongolia,Keyouzhongqi area of Inner Mongolia and Beishan area in the southern section of the Central Asian Orogenic Belt.Random forest algorithm is used for training sets of major elements and trace elements in global island arc basalt and rift basalt,and then the trained model is used to predict the tectonic setting of the Carboniferous magmatic rock samples in the Xingmeng orogenic belt.The prediction results shows that the island arc probability of most of the research samples is between 0.65 and 1,which indicates that the island arc tectonic setting is more credible.In this paper,it is concluded that magmatism in the Beishan area of the southern part of the Central Asian Orogenic belt in the Early Carboniferous may have formed in the heyday of subduction,while the Xingmeng orogenic belt in the Late Carboniferous may have been in the late subduction stage to the collision or even the early rifting stage.This temporal and spatial evolution shows that the subduction of the Paleo-Asian Ocean is different from west to east.Therefore,the research results of this paper show that the subduction of the Xingmeng orogenic belt in the Carboniferous has not ended yet.

Explainable artificial intelligence models for mineral prospectivity mapping

Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.

Comparative study of big data of global adakites and mineralization-related granite in the Geza arc metallogenic belt, northwest Yunnan, Southwest China

The Geza arc is an important part of the Sanjiang tectono-magmatic belt and is a newly discovered copper polymetallic ore concentration area in northwest Yunnan province,Southwest China.The area comprises numerous metal ore deposits,including one super-largedeposit,three large deposits,etc.The formation of these deposits was closely related to intermediate–acidic magmatic intrusions.Based on previous studies,the“big data”analysis technique was used for a comparative study of large geochemical datasets of granite related to ore-formation in the Geza porphyry copper deposit and global adakites.As a result,1313 element combinations and 127,765 overlap ratios were obtained.The results show that the Geza porphyry has similar geochemical characteristics to global adakites(the ratios of REE and Ga to major elements are in the range of global adakites).However,theCu,Mo,and Zn contents of the porphyry are significantly higher than those of global adakites,and the porphyry may,therefore,represent an end-member of the global range of adakite composition.In addition,the geochemistry of adakites associated with the porphyry copper deposits overlaps in part with that of global adakites,although most of the data lie outside of the range of global adakites(i.e.lowMn/Cu,Sr/Cu,Na/Cu,and Zr/Cu values,and high Th/Cu,Ba/Cu,Na/Mo,Rb/Mo,Th/Mo,Ta/Mo,Ba/Mo,Mn/Zn,and Ba/Znvalues).The samples with characteristics that deviate significantly from the geochemistry of global adakites show more advanced mineralization and alteration,and a stronger relationship with Cu and Momineralization.The results of geochemical data mining can be used as a prospecting indicator,and provide a new scientific basis for geological prospecting of the deep levels and per-iphery of the Geza Cu polymetallic ore belt.