A short review of data processing in the earth sciences in Czechoslovakia

Summary The archival files of geochemical, metallogenic, petrologic, paleontologic, hydrogeochemic, and other data have their special problems—mostly at the Geological Surveys (Prague) or at the Geological Department of Charles University (Prague)- and it is hoped they will be solved with the same enthusiasm and support as the environmental-geologic data center.Geofond's activity in the field of data processing and computer-based information service has demonstrated that traditionally developed geologic surveys on a small scale must also proceed to computer-based information techniques to avoid the lack of relevant and formalized data for future demands of geoscientists and the public and official need for quick and decision-oriented information.Published with permission of the Director, Geological Survey of Czechoslovakia.     

A model for earth science data structures designed to promote generalization of earth science data processing systems

During the past 15 years earth science survey data compilation has gradually changed from a manual process to one almost entirely computer automated. Computer methods were at first an optional means for improvement of the speed and efficiency of certain phases of the work. They eventually became mandatory as in-flight digital acquisition devices were introduced and rates of data acquisition increased to the point where manual methods became totally inadequate. As a consequence of the phased replacement of manual systems with digital ones, and the pressing need to avoid interruptions to the work, the resulting software is highly specialized. Software systems are incompatible between different organizations compiling the same kind of data and even between systems for different kinds of data within one organization. The result is wastage of manpower in maintaining, learning to use, and improving the great variety of existing systems, and wastage of man and computer time in converting data to a form compatible with the peculiarities of each particular system. Comparative analysis of highly specialized compilation systems form several different earth science disciplines reveals that processes do exist at the fundamental level which are generally applicable to a range of disciplines. Hence a generalized compilation system capable of eradicating the problems inherent in multiple specialized systems is at least conceptually feasible. The most serious obstacle to its realization however, is the diversity of data content and structure among the various disciplines. To surmount this obstacle, a system with a great degree of physical and logical independence of data from software will be necessary. Existing methods of achieving such independence within data base management systems are found to be largely inapplicable to earth science survey compilation, the principle reasons being the very large quantities of data involved and significant differences between the data retrieval requirements of compilation systems and data base management systems. It is found that, although the data contents and structures vary greatly between the various compilation systems examined, a general abstract model can be constructed which adequately represents all of them and which incorporates the means for achievement of data independence. The logical data structures of current data base management systems are based variously on relational calculus, set theory, and graph theory. The compilation data structure model is based on simple algebra with the addition of some components of vector algebra. It is further found that simple algebraic manipulation of the model expressions faith-fully simulates the actual data manipulation processes which are applied by the various compilation systems. Furthermore, unlike the logical models of the data base management systems, the logical structure of the compilation data model also represents the actual physical structure exhibited by the data in its most common, sequential form.     

A review on missing hydrological data processing

Like almost all fields of science, hydrology has benefited to a large extent from the tremendous improvements in scientific instruments that are able to collect long-time data series and an increase in available computational power and storage capabilities over the last decades. Many model applications and statistical analyses (e.g., extreme value analysis) are based on these time series. Consequently, the quality and the completeness of these time series are essential. Preprocessing of raw data sets by filling data gaps is thus a necessary procedure. Several interpolation techniques with different complexity are available ranging from rather simple to extremely challenging approaches. In this paper, various imputation methods available to the hydrological researchers are reviewed with regard to their suitability for filling gaps in the context of solving hydrological questions. The methodological approaches include arithmetic mean imputation, principal component analysis, regression-based methods and multiple imputation methods. In particular, autoregressive conditional heteroscedasticity (ARCH) models which originate from finance and econometrics will be discussed regarding their applicability to data series characterized by non-constant volatility and heteroscedasticity in hydrological contexts. The review shows that methodological advances driven by other fields of research bear relevance for a more intensive use of these methods in hydrology. Up to now, the hydrological community has paid little attention to the imputation ability of time series models in general and ARCH models in particular.     

Influence of the improbable in earth sciences

The special significance of unanticipated extreme data (in space and time), especially for both genetic and economic predictions, is emphasised with reference to numerous dissimilar geological examples. However, commonly, data for measured variables falling near, or beyond, the tails of unimodal distribution curves have not been incorporated in inverse or forward models used previously in the earth sciences.     

Reference samples for the earth sciences

A revised list of reference samples of interest to geoscientists has been extended to include samples for the agronomist, the archaeologist and the environmentalist. In addition to the source from which standard samples may be obtained, references or pertinent notes for some samples are included.The number of rock reference samples is now almost adequate, and the variety of ore samples will soon be sufficient. There are very few samples for microprobe work. Oil shales will become more important because of the outlook for world petroleum resources. The dryland equivalent of a submarine basalt might be useful in studies of sea-floor spreading and of the geochemistry of basalts.The Na- and K-feldspars of BCS (British Chemical Standards—Bureau of Analysed Samples), NBS (National Bureau of Standards), and ANRT (Association Kationale de la Recherche Technique) could serve as trace-element standards if such data were available. Similarly, the present NBS flint and plastic clays, as well as their predecessors, might be useful for archaeological pottery studies. The International Decade for Ocean Exploration may stimulate the preparation of ocean-water standards for trace elements or pollutants and a standard for manganese nodules.     

Recursive parameter estimation with applications in earth sciences

Parameter estimation has become increasingly interesting the last few decades for a variety of engineering topics. In such situations, one may face problems like (a) how to estimate parameters for which erroneous measurements are available (direct estimation), or (b) how to estimate coefficients of some process model governing a geological phenomenon when these coefficients are inaccessible or difficult to access by direct investigation (inverse estimation or identification). Both these problems are examined in this presentation from a modern stochastic viewpoint, where parameters sought are interpretated mathematically as random functions, generated and estimated in space or time with the aid of recursive models. Advantages of this methodology are remarkable, from both theoretical and physical points of view, as compared to conventional statistics or nonrecursive estimators. Particularly it may offer more accurate estimators, better representation of spatial variation, and a means of overcoming difficulties such as excessive computational time or computer storage. To test effectiveness of this type of estimation, a series of representative case studies from geotechnical practice have been computed in detail.     

Big data and the historical sciences: A critique

Social media, government, industry and science use data in the same way, through the pursuit of correlations in large data sets. As this critique shows, however, there is greater dialogue about the potential pitfalls of Big Data and the Big Data Cycle in non-historical science fields, such as medicine and advertising. Pitfalls, such as the Big Data Hubris, the Filter Bubble and correlation superseding causation, are discussed in relation to the historical sciences.     

Employment generation in earth sciences through broad-basing of geoscience

Geoscience instruction in India is facing a serious crisis-instances are known of well-established university geology departments with more than a score of senior teachers having just a couple of M.Sc. students. This profound lack of demand for geoscience studies is a direct consequence of the paucity of employment opportunities for tradition-ally-trained geoscience graduates which     

Geostatistics: Models and tools for the earth sciences

The probability construct underlying geostatistical methodology is recalled, stressing that stationary is a property of the model rather than of the phenomenon being represented. Geostatistics is more than interpolation and kriging(s) is more than linear interpolation through ordinary kriging. A few common misconceptions are addressed.     

Some thoughts on the automated generation and selection of hypotheses in the earth sciences

Many major scientific problems in the earth sciences can be expressed in terms of finding a sequence of past events which can explain an observed configuration. The configuration might be anything from distribution of continents to a particular landform pattern. Because the past is unobservable, explanations take the form of one or more subjective hypotheses for a specific configuration origin. The subjective approach allows free rein of the human scientific spirit, but the way also is open for personal bias and construction of needlessly elaborate hypotheses. As an alternative, an objective process of hypothesis generation should be achievable by way of a generalized expert system incorporating all geological environments. The generality is required in order to allow for the possibility of producing surprising hypotheses, which might not have been anticipated in the context of any specific geological environment under study. In selecting from many available hypotheses, the criterion of simplicity is a useful means by which a hypothesis subset can be created and listed. However, no guarantee exists that subset members will better approximate the truth. The rationale is rather in terms of a working rule for avoiding unnecessary complexity in explanations. Creation of a generalized expert system would be a major project involving a team effort. Such a project would have the advantage of raising the scientific profile of mathematical geology, a subdiscipline which at present has something of an image of solving little problems elegantly.