摘要：In the geosciences, data are acquired, processed, analysed, modelled andinterpreted in order to generate knowledge. Such a complex procedure isaffected by uncertainties related to the objective (e.g. the data,technologies and techniques employed) as well as the subjective (knowledge,skills and biases of the geoscientist) aspects of the knowledge generationworkflow. Unlike in other scientific disciplines, uncertainty and its impacton the validity of geoscientific outputs have often been overlooked or onlydiscussed superficially. However, for geological outputs to providemeaningful insights, the uncertainties, errors and assumptions madethroughout the data acquisition, processing, modelling and interpretationprocedures need to be carefully considered. This special issue illustratesand brings attention to why and how uncertainty handling (i.e. analysis,mitigation and communication) is a critical aspect within the geosciences.In this introductory paper, we (1) outline the terminology and describe therelationships between a number of descriptors often used to characterise andclassify uncertainty and error, (2) present the collection of research papersthat together form the special issue, the idea for which stems from a 2018European Geosciences Union's General Assembly session entitled“Understanding the unknowns: recognition, quantification, influence and minimisation of uncertainty in the geosciences”, and (3) discuss the limitations of the “traditional” treatment ofuncertainty in the geosciences.“The efforts of many researchers have already cast much darkness on the subject, and it is likely that, if they continue, we will soon know nothing about it at all.” – Mark Twain Downloadandlinks Article (PDF, 653 KB) How to cite Back to top top How to cite. Pérez-Díaz, L., Alcalde, J., and Bond, C. E.: Introduction: Handling uncertainty in the geosciences: identification, mitigation and communication, Solid Earth, 11, 889–897, https://doi.org/10.5194/se-11-889-2020, 2020. 1 Introduction to this special issue Back to toptop Over the past 50 years, development of new acquisition, analytical andexperimental techniques in the geosciences, alongside the associated rise inavailable data has led to major breakthroughs in our understanding of theEarth, such as the development of plate tectonic theory or theacknowledgement of anthropogenic global warming. With ever-more powerfulinformation technology, many aspects of geoscience now rely oncomputer-assisted models and simulations. Computers are not only extremelypowerful tools for the integration and analysis of big data, which otherwisewould simply be unmanageable, but they are also instrumental in the testingof hypotheses and visualisation of processes acting over the full range ofterrestrial spatial and temporal scales. However, for geological outputs toprovide meaningful insights, the uncertainties, errors and assumptions madethroughout the data acquisition, processing, analysis, modelling andinterpretation procedures need to be carefully considered. Unlike in otherscientific disciplines in which uncertainty analysis is a key component ofresearch, geological outputs (maps, interpretations, models, simulations)are frequently presented unaccompanied by uncertainty estimates, perhaps dueto the disciplinary expectation of a single (flawless) deterministic modelor unequivocal interpretation and corresponding outputs. This is a bar toeffective interdisciplinarity in the geosciences, because it maintains asituation in which there can be no explicit understanding of how differingdatasets and modelling approaches conflict with and complement one another.Routine handling of uncertainty (analysis, mitigation and communication) isthus an urgently pending need in the geosciences.With this concern in mind, a multidisciplinary session was organised duringthe 2018 European Geosciences Union (EGU)'s General Assembly, with the title“Understanding the unknowns: recognition, quantification, influence and minimisation of uncertainty in the geosciences”. The session was conceived as a forum in which geoscientists from differentfields could share their different views and approaches on how to handleuncertainty. The session was well attended and included contributions frommost of the classical geoscience fields, including sedimentology,palaeoclimate, structural geology, tectonics, geochemistry and geophysics.This special issue on uncertainty encompasses 12 articles covering thequantification and management of uncertainty in a broad range of geologicaldisciplines, including seismic interpretation (Alcalde etal., 2019; Schaaf and Bond, 2019), mantle dynamic models(Bodur and Rey, 2019;Mather and Fullea, 2019), field geology (Andrewset al., 2019; Bárbara et al., 2019; Pakyuz-Charrier et al., 2019; Stammet al., 2019; Wilson et al., 2019), plate kinematic modelling(Causer et al., 2020) and subsurface resource evaluation(Miocic et al., 2019; Wilkinson and Polson, 2019). This collection of original research contributions, some ofwhich were presented at the aforementioned EGU 2018 session, highlights theimportance of understanding uncertainty, often neglected by interpreters,geomodellers and experimentalists. 2 Definitions of error and uncertainty Back to toptop Although they are often used interchangeably when they do appear ingeoscientific literature, “error” and “uncertainty” are not synonymousterms. Effectively quantifying and discussing these two concepts whenpresenting results (which in the geosciences may be statements,measurements, calculations or models) therefore starts by understanding thedifference between them. The concept “error” describes the estimateddifference between a single measured value and some assumed or knownreference “true value”, usually comprises both systematic and randomcomponents. Errors are often dealt with and quantified in all fields ofscience, and their effects addressed purely with statistical approaches.However, when the “true value” is accepted to be practically or absolutelyunknowable and/or unmeasurable, as is often the case in geosciences with itsinstances of deep burial and deep time, we must instead deal with“uncertainty”. Uncertainty can be described as a consequence of themismatches between the quantity and quality of the knowledge available andthose of the knowledge required for rational decision (model) making(Tannert et al., 2007) or, in other words, as “afunction of our ignorance”. Describing uncertainty requires recognition thatour knowledge is flawed and limited, identifying the “known unknowns” andacknowledging that there may also be “unknown unknowns” (things we do noteven know we do not know). Quantifying uncertainties is thus the process ofanalysing how far away our ideas might stray from any “describable truth”.In this way, “error” is a difference, a measure of precision;“uncertainty” is a range, or estimate of accuracy. Figure 1 illustratesthe differences between error and uncertainty, and the relationship of theformer with other quality descriptors and performance characteristicscommonly used in the geosciences (accuracy, precision, trueness and bias).In summary: Error is the quantified difference between a knowable parameter anda measured variable. It is quantifiable as a combination of both systematicerror and random error.Trueness, precision and accuracy relate to error and require some knowledgeor expectation of a “true value” for comparison. Trueness is the closeness of agreement between the average valueobtained from a large series of test results and the expected true value. Trueness is largely affected by systematic error. Precision is the closeness of agreement between independentmeasurements. Precision is largely affected by random error. Accuracy is the agreement between a measurement and the expectedtrue value. It is an expression of the relative size of error.However, the following are also applicable: Uncertainty characterises the range of values within which apractically unmeasurable or unknowable parameter is estimated to lie at somelevel of confidence. Any repetition of the estimation process at the sameconfidence level should be expected to produce a result within the limits ofthe uncertainty range. The final uncertainty budget of an output mayincorporate several “systematic uncertainties” that have to be quantified.For example, in geochronology, the decay constant of a given isotope systemcarries with it an uncertainty, which does not change, but which is anadditional component that has to be propagated onto the final quoteduncertainty of an age.In some geoscientific disciplines, for example, in the fields of geochemistryand geochronology, terms such as measurement uncertainty and error havefixed meanings, although misuse is still common. Potts (2012) provide auseful translation of the International Vocabulary of Metrology 2008 (VIM3, Bureau Internationale des Poids et Mesures) for analysts in geoscience. However, outside of these fields focusedon chemical data, the terminology becomes looser, which is largely areflection of the complex use of language.Figure 1Visual representation of (a) the difference between errorand uncertainty and (b) the relationships between commonly used qualitydescriptors and performance characteristics.
