Cloud computing as a powerful technology of performing massive-scale and complex computing plays an important role in implementing geological information services. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing based cloud platform, and technology applications.
In the era of big data, the data-driven modeling method enables us to exploit the potential of massive amount of geological data easily [
The application of geological cloud makes it possible to fully utilize structured and unstructured data, including geology, minerals, geophysics, geochemistry, remote sensing, terrain, topography, vegetation, architecture, hydrology, disasters, and other digitally geological data distributed in every place on the surface of the earth [
Big data technologies are bringing unprecedented opportunities and challenges to various application areas, especially to geological information processing [
The trend of the number of documents in which “geological data” is in the “Title” from 2007 to 2016.
The trend of the number of documents in which “geological data” is in the “Topic” from 2007 to 2016.
Considering the development status of cloud-enabled geological information services (CEGIS) and the application requirements of big data management analysis, this article describes the significant impact and revolution on GIS brought by the advancement of big data technologies. Furthermore, this article outlines the future application development and technology development trend of big data management analysis in CEGIS.
The remainder of this article is organized as follows. In Section
The construction of geological cloud differs from the current big data analysis based on Internet and Internet of Things (IoT). Having a deep understanding of data characteristics is necessary to collect, process, analyze, and interpret data in different fields, because the nature and types of data vary in different fields and in different problems. Geology is a data intensive science and geological data are characterized with multisource heterogeneity, spatiotemporal variation, correlation, uncertainty, fuzziness, and nonlinearity. Therefore, the geological cloud has a certain degree of confidentiality and it is highly domain-specific; meanwhile, it is developed on the basis of a large amount of geological data accumulated over a long period of time [
Geological big data are multidimensional, and they consist of both structured and unstructured data [
Generally, the construction of geological cloud is a long-term systematic project. This means that it is required to follow the basic principles of “standing on the reality, focusing on the future” and “focusing on the long-term and overall situation, embarking on the current and local situation,” in order to achieve the analysis and application of geological cloud public data and core data gradually in accordance with the technical route of big data analysis; thus the construction of geological cloud will be implemented eventually. For the earth, the land and resources management should cover many respects, including human behavior, climate change, development and utilization of various resources, natural disasters, environmental pollution, and the ecosystem cycle. Then, the introduction of big data technologies can integrate this type of resource information to provide the ability of uniformly dealing with the problems related to the entire earth information resources, which has a significant effect on the strategic planning of land and resources management [
The geological cloud is an important part in the science system for geological data research. The ultimate goal for developing geological cloud is to better describe and understand the complex earth system and geological framework, provide the scientific basis for the description of the land surface and the biodiversity characteristics of the earth, and improve the ability to deal with complex social problems.
Because the business service functions of each country are different, the system architecture of the geological cloud would vary. In the following, we present a system architecture in Figure
The system architecture of geological cloud.
The geological cloud combines the geological survey Intranet and the geological survey Extranet. It enables the sharing and management of computing resources, storage resources, network resources, software resources, and geological data resources [
Geological cloud can be summarized with the following characteristics [ “ “ “
On the Extranet, it deploys a system for geological survey business management and auxiliary decision-making. The system provides a real-time tracking and management function for geological survey projects and various resources.
Main users of the geological cloud include institutional users, geological survey project users, and the general public users. The institutional users can store the current geological database and newly collected data in the geological cloud through the geological survey business network and can obtain the geological data of other institutions from the cloud as needed. The geological survey project users can access the cloud geological background data through 4G or satellite lines and can collect data through data the collection system.
The construction of geological cloud must meet customer demand. Big data technologies are then used as the means to implement the geological cloud.
The types and quantity of geological data have been continuously growing over the years. Geological data include all kinds of electronic documents, structured, semistructured, and unstructured data, such as various databases (map database, spatial database, and attribute database), pictures, tables, video, and audio. Generally speaking, those important data may be buried in the massive data without the guidance for requirements. Hence, the first step is to understand the user requirements and then gain the capability of large-scale data processing. This is followed by data mining, algorithm, and analysis, which will ultimately generate value. Big data technologies in the field of geography must meet different needs from people at different levels, including the public demand of the geologic data services and professional data demand for geological research institutions, as well as related enterprises and government departments [
On the basis of big data analysis technologies, a complete data link is formed connecting data, information, knowledge, and service, through the use of advanced cloud computing system, IoT, and big data processing flow. It is shown in Figure
Schematic diagram of big data analysis.
Geological big data are generated regarding various layers of the earth, the history of the conformation and evolution of the earth, and the material composition of the earth and its changes. It also involves the exploration and utilization of mineral resources. In the current geological work, the collection, mining, processing, analysis, and utilization of various complex type data are closely related to those general big data. The “4V” characteristics of big data—namely, Volume, Velocity, Variety, and Veracity—also apply to geological big data.
Currently, there is no consensus on the size of geological data. Geological big data are a collection of data, including geology, minerals, remote sensing, geophysical exploration, geochemical exploration, surveying, and mapping, which are interconnected and integrated. In terms of the number of mines, there are at least 70000 in China, and some official documents and popular science books indicate that there are more than 200000 deposits and minerals that have been found. The information is huge and cannot be processed using conventional tools. For example, an Excel spreadsheet cannot contain all the information of 70000 mining areas. Then, it is difficult to classify and rank the 200000 mines, so it is necessary to rely on the concepts and technologies of big data [
Especially in recent years, images, video, and other types of data have emerged on a large scale. With the application of 3D scanning and other devices, the data volume has been increasing exponentially. The ability to describe the data is more and more powerful, and the data are gradually approximated to the real world. In addition, the large amount of data is also reflected in the aspect that the methods and ideas used by people to deal with data have undergone a fundamental change. In the early days, people used the sampling method to process and analyze data in order to approximate the objective with a small number of subsample data. With the development of technologies, the number of samples gradually approaches the overall data. Using all the data can lead to a higher accuracy, which can explain things in more detail [
Recently, the China geological survey system has built databases including regional geological database (covering the 1 : 2500000, 1 : 1000000, 1 : 500000, 1 : 250000, and 1 : 200000 regional geological map; the national 1 : 200000 natural sand; the isotope geological dating; and the lithostratigraphic unit database), basic geological database (covering the national rock property database and national geological working degree database), mineral resources database (covering the national mineral resources, the national mineral resources utilization survey mining resources reserves verification results, the national survey of large and medium-sized mines, the prospect of mineral resources, the survey of the resources potential of major solid mineral resources in China, and the geological and mineral resources database), oil and gas energy database (covering the oil and gas basins in China, the geological survey results of the national oil and gas resources, the national petroleum and geophysical exploration, national shale gas, national coal bed methane, national natural gas hydrate, and other databases), geophysical database (covering 1 : 1 million, 1 : 500000, 1 : 250000, 1 : 200000, and 1 : 50000 gravity, national regional gravity, national aeromagnetism, national ground magnetism, national electrical survey, seismic survey, national aviation radioactivity, and national logging database), geochemical database (covering the databases of national 1 : 250000 and 1 : 20 geochemical exploration, national multiobjective geochemical and national land quality evaluation results), remote sensing survey database (covering national aeronautical remote sensing image, China resources satellite data, space remote sensing image, national mine environmental remote sensing monitoring, national high score satellite, and other databases), drilling database (covering the national geological borehole information, the national important geological borehole, the Chinese mainland scientific drilling core scanning image library, and so on), hydraulic cycle hazards database, data literature database, special subject database (covering the national mineral resources potential evaluation database, the important mineral “three-rate” investigation and evaluation database), work management database (covering the national exploration right, mining right, mining right verification, geological information metadata database, and many others) [
For those databases, they are still expanding and consummating, and their practical values have not yet been fully reflected. However, the vast majority of researchers are virtually impossible to have all of the above data, at most, using their own accumulated data. Anyway, even if their accumulated data, both on the quantity and on type, is incomparable by 10 years and 20 years ago, they are, in fact, in the era of the “relatively big data.” From 1999 to 2004, for example, in “the Chinese mineralization system and regional metalorganic evaluation” project, although there are 202 national academic experts that participated in it, they only master data of 4500 properties (all kinds of minerals). From 2006 to 2013, the study of “national important mineral and regional mineralization laws” was conducted; meanwhile, the mining area covered only by the mineral resources research institute was 30600. Therefore, the increase of information and the amount of data are unprecedented in the last ten years.
From the formal point of view, the geological big data have many characteristics, including multidimensionality, multiscale, and multitenses. And they contain structured, semistructured, and unstructured data and usually are stored in forms of text, graphics, images, databases (including image database, spatial database, and attribute database), tables, videos, and audios in a fragmented state. For example, a large number of field outcrop description data, borehole core description data, and all kinds of geological survey, exploration report, and a large number of geological maps, drawings, and photos were stored and managed in the form of paper for a long time; even the numerous relational databases and spatial databases were primarily used to store and manage structured data that are tabulated and vectorized, while the text descriptions, records, and summaries were directly stored. Very few standardized processing and structural transformations were performed. Furthermore, there is no tool available to effectively integrate storage and manage structured, semistructured, and unstructured data.
The increase of geological data is very fast, especially in remote sensing geology, aviation geophysical exploration, regional geochemical exploration, and other fields, due to the introduction of new technologies and new methods. Meanwhile, high speed processing is also a characteristic of big data. In addition to the need of analyzing data in real time, people also need to describe the results of data mining and processing through the use of several data processing techniques, such as image and video, while requiring effective and efficient handling skills. For example, the detection of the deep earth information not only needs to obtain parameters of the seismic wave reflection and refraction but also needs to conduct quick processing, so as to timely predict whether earthquake will occur and forecast the time, location, and intensity. In this way, we can avoid the disaster effectively. When applying a variety of data to a particular mountain, one should learn which ones have spatial limitations and which are not related to spatiality, so that one deduces the metallogenic law and guides the prospecting better [
For the understanding of the value of big data, most people consider it low value density. It means that the real useful information in the vast amount of data is very little. Taking video as an example, the useful data may be only a second or two in the continuous monitoring process. While big data is high value, it does not need to be invested too much; just collecting information from the Internet can bring business value. Therefore, big data has the characteristics of low value density and high business value. The same is true for geological big data. So far, there has been a lot of information about geophysical prospecting, but only a few have been confirmed, and the discovered mines were less. But once a breakthrough was made, its socioeconomic value was enormous, such as the lithium polymetallic deposit in Tibet and the newly discovered Jima copper polymetallic deposit in the outskirts of Sichuan [
In addition, the spatial attribute and temporal attribute of geological data also bring a big challenge to data accuracy. Any geological data have spatial attribute, and their values are reflected in the spatial law of distribution of mineral resources. For this reason, in the process of establishing the metallogenic series, exploring the metallogenic law, and constructing the mathematical model, the spatial attribute of the metallogenic model should be considered. Obviously, every metallogenic series has its spatial attribute. Geological data also has the time attribute, which is very different from physical, chemical, and other natural sciences. One of the fundamental pillars of geology is the geological time scale. The rocks, strata, and deposits of different geological periods have different distribution characteristics and regularity, so those data have their own time attribute.
It is obvious that those characteristics of geological big data mentioned above impose very challenging obstacles to the data management in CEGIS. The challenges related to geological big data management can be summarized as follows: It is quite difficult to describe and model geological big data, since there are few effective characteristics description mechanisms and object modeling approaches under the cloud computing environment. There remain many technical issues that must be addressed to fully manage, mine, analyze, integrate, and share those geological big data, in consideration of those complex characteristics, including multisource heterogeneous data, highly spatiotemporal variation, high-volume and high-correlation data, and many others. Many issues appear in achieving decision support, such as data incompleteness, data uncertainty, and high-dimensionality of data.
The broad range of challenges described here make good topics for research within the field of big data management in CEGIS. They are analyzed in the next section.
With the rapid advancement of big data technologies, some key technologies are accordingly developed for big data management in CEGIS. Specifically, a schematic diagram of those key technologies is shown in Figure
Schematic diagram of key technologies for big data management in CEGIS.
Geological big data collection and preprocessing aim to categorize those geological big data obtained from geological data, geological information, and geological literature.
In addition to the traditional collection ways, it is also required to carry out large-scale network information access and provide real-time, high concurrency, and fast web content acquisition, combining with the application characteristics in the cloud environment. Currently, considering that the growth rate of geological information generated from the network is very fast, the big data analysis system should obtain relevant data quickly.
It needs to distinguish and identify valuable information through intelligent discovery and management technologies. Because the information value density contained in different data sources differs from each other, filtering out the useless or low-value data source can effectively reduce the data storage and processing costs. Then, it can also further improve the efficiency and accuracy of analysis.
According to the subject domain of geology, the distributed data are extracted to form a data warehouse, after conducting the operation of processing and integration. When extracting data in the field of geology, it needs to use entity modeling method to abstract entities from the vast numbers of data, so as to find out the relationship between those entities. This approach ensures that the data used in warehouse data can be consistent and relevant in accordance with the data model [
Generally, different data sources and even the same data source may generate data with different formats. As mentioned above, because these structural, semistructured, and unstructured multimodal geological big data are integrated together, the data heterogeneity is obvious in big data analysis. Then, data aggregation as the key technology in achieving data extraction and transformation [
In order to effectively utilize geological big data, it needs to track the evolution of big data during the whole life cycle of GIS, with the purpose of achieving the traceable big data management.
Here, we provide an example of aggregating and collecting geological big data in CEGIS. Figure
An example of aggregating and collecting geological big data in CEGIS.
From the data collection perspective, geological data can be divided into field survey data, drilling and engineering exploration data, remote detection data, analytical test data, and comprehensive study data. From the angle of comprehensive application fields, they can be also divided into regionally geological survey data, energy and mineral resources evaluation and exploration survey results data, geological disaster monitoring and early warning data, geological environment survey and evaluation results data, and marine geological survey and evaluation data. From the data formality point of view, they can be divided into picture data, text report data, tabular data, and image data. These data are collected by different units.
Facing these complex geological big data mentioned above, the traditional relational database will be difficult to handle them, while the distributed storage system can be used to store such huge amounts of data and manage them. Then, the data system places the massive data in many machines, which avoids such limitation of storage capacity, and also brings many problems that have not occurred before in stand-alone systems. Hence, some distributed data storage solutions have accordingly emerged, including Hadoop, Spark, and other nonrelational database systems (like HBase, MongoDB, and many others) [
Here, the hot research topics include the following: For geological applications, the load optimization storage should be implemented to achieve the coupling for data storage and application and the coupling for distributed file system and the new storage system. Based on the application characteristics of distributed databases, more studies could be conducted on the application of new databases NoSQL and NewSQL in geological survey work.
With the development of big data technologies, more and more mature distributed data storage solutions will emerge and will be applied to big data analysis [
Specifically, in the management of geological big data, the implementation of data query—for example, spatial query—has been a long-term focus. Generally, considering those advantages with unified modeling language (UML) and computer-aided software engineering (CASE) methodology, the spatial database could be accordingly designed and implemented to characterize and realize the object-oriented spatial vector big data firstly [
In terms of geological data analysis and mining, it needs to combine geological data, geological information, and geological literatures, through the analysis of geological application demand of real-time mining, to explore geological big data environment analysis and mining algorithm, in an effort to fully achieve the goal of intelligent mining for geological big data.
Figure
Schematic diagram of discovering geological knowledge through analyzing and mining geological big data.
Considering the special applications, geological big data technologies would apply big data concepts to analyze the metallogenic rules by making full use of various data related to ore, to recognize deposit metallogenic series, to summarize the metallogenic regularities and express in an appropriate way (like voice, image, and many others), and to establish the scientifically mathematical model. The model then uses new exploration data to predict future data and to guide geological prospecting.
In addition, it is necessary to pay special attention to the analysis of new geological big data information collected from social medium and networks [
Data mining is to extract the unknown and useful knowledge and information from the massive multilevel spatiotemporal data and attribute data, using statistics, pattern recognition, artificial intelligence, set theory, fuzzy mathematics, cloud computing, machine learning, visualization, and relevant techniques and methods. Data mining could reveal the relationship and evolution trend behind the geological big data, achieve the automatic or semiautomatic acquisition of the new knowledge, and provide the decision basis for resource prediction, prospecting, environmental assessment, and disaster prevention and mitigation [
Effective data mining also could reduce manual intervention during information processing and make use of methods and tools of big data intelligent analysis [
Highly performable big data cloud computing platform is the foundation for big data analysis. It enables parallel computing for large-scale incremental real-time data and large-scale heterogeneous data [
With the advent of massive data storage solution, many big data distributed computing frameworks have been proposed. Among them, Hadoop, MapReduce, Spark, and Storm are the most important distributed computing frameworks. These frameworks have different characteristics and solve different problems in applications [
An application example is geological big data aggregation mining framework based on Hadoop [
MapReduce has been used to perform morphological correlation analysis, which involves the analysis of geochemical data processing and the study of the correlation between multielements. Figure
Correlation among three element morphologies.
The metallogenic law is the human regular knowledge of the temporal and spatial distribution of mineral resources, and its cognitive level, ability, and scope are all related to the size of data, the type of data, and the way of data processing. Therefore, to deduce the metallogenic law, it is necessary to fully understand the massive data about spatial distribution, reserves and production in mineral origin, the geological structure of the mineral origin, and related geological survey data. Then, it is to conduct the regular speculation and objectivity expression of these geological big data, so that one can identify the essential reasons for the distribution of mineral origin. Actually, using geological big data technologies could help to translate data into new understanding or knowledge and help to guide the future geological prospecting work.
The types of deposits vary, and the formation of them is related to certain geological backgrounds and geological effects, respectively. The geological backgrounds include tectonic unit and stratigraphic unit, deep upper mantle and lithosphere conditions, and paleogeography and palaeoclimate environment on the surface of the earth. Geological effects include tectonism, magmatism, sedimentation, metamorphism, and weathering. Theses geologic backgrounds and effects in the wide range of space, and in the long geologic history, are a dynamic change and repeated stack, and large deposits can be formed only in a variety of favourable conditions. Long-term scientific research and experience accumulation formed mineral deposit and mineralization prediction subject. Professionals are guided by certain theories and methods to adopt quantitative or qualitative methods to predict prospecting with the existing knowledge and experience.
However, in view of the difficulties of geological data sharing and the limitations of calculation tools and calculation methods, most of the known deposits in the past are independent of each other. In the future, we can use geological data to connect several adjacent deposit exploration data, conduct unified analysis and specialized processing, determine the “digital” characteristics of the distribution of metallogenic materials, find out metallogenic potential, delineate the abnormal area and prospective area, and promote geological prospecting. Furthermore, geological data informatization and standardization could be improved [
After entering the 21st century, geological work is more closely related to economic development, and geological work plays an important role in every aspect of social and economic life. Agricultural geology, urban geology, environmental geology, tourism geology, disaster geology, and other works have been strengthened, and the service area has also been expanded [
In order to meet the social demand for geological data, China Geological Survey carried out the construction of geological cloud, which built cluster geologic data service system with the National Geological Information Center and the Provincial Geological Information Center as the backbone nodes, conducted the integration of data resources, and applied the GIS cloud technology, in order to obtain large-scale computing ability and solve those key problems, such as the distributed storage, processing, query, interoperability, and virtualization of massive spatial data [
With the continuous development of web technology, human beings have experienced the “Web 1.0” era, which is characterized by document interconnection, and “Web 2.0”, which is characterized by data interconnection, and are moving towards the new “Web 3.0” era based on the interconnected knowledge of the entity. Due to the continuous release of user-generated content and linking open data on the Internet, people need to explore knowledge interconnection methods which both conform to the development of the network information resources and meet users’ requirements from a new perspective according to the knowledge organization principles in the large data environment, to reveal human cognition on a deeper level [
In this context, knowledge graph (KG) was formally put forward by Google in May 2012, and its goal is to improve the search results and describe the various entities and concepts that exist in the real world and the relationship between these entities and concepts. KG is a great choice to select the essence and discard the dross, as well as the sublimation of the present semantic web technology. In recent years, the applications of KG have been increasing rapidly, and there is now a mature method used to draw a KG and conduct intelligent searching research based on KG [
Big data technologies make it possible to process massive amount of unstructured and semistructured geological data. And the geological cloud enables us to explore the application of demand-driven geological core data and to extract new information from unstructured data, while supporting the decision-making in land resources management. Thus, the geological cloud could effectively organize and use geological big data, to mine the data scientifically, with the purpose of producing higher value and achieving the corresponding service.
In the architecture of geological cloud, this article describes the application background of CEGIS and the demands from big data management. Furthermore, we elaborate the application requirements and challenges faced in big data management technologies. Then, more analyses are provided from four aspects, including data size, data type, data processing speed, and data processing accuracy, respectively. In addition, this article outlines the research status and technology development opportunities of big data related in CEGIS, from the perspectives of big data acquisition and preprocessing, big data storage and management, big data analysis and mining, highly performable big data cloud computing platform, and big data technology applications. With the continuous development of big data technologies in addressing those challenges related to geological big data, such as the difficulties of describing and modeling geological big data with some complex characteristics, CEGIS will move towards a more mature and more intelligent direction in the future.
The authors declare that there are no conflicts of interest regarding the publication of this article.
This work was supported in part by the Key Laboratory of Geological Information Technology of Ministry of Land and Resources under Grant 2017320, the National Key Technologies R&D Program of China under Grant 2015BAK38B01, and the National Key R&D Program of China under Grant 2016YFC0600510.