Stochastic Hydrogeology: Chuck Newman Had a Good Idea About Where to Start

2019 ◽  
pp. 90-100
Author(s):  
Colin L. Clark ◽  
Larry Winter
Keyword(s):  
Stochastic Hydrogeology

scholarly journals Applied Stochastic Hydrogeology

Eos ◽  
2003 ◽  
Vol 84 (43) ◽  
pp. 463
Author(s):  
You-Kuan Zhang
Keyword(s):  
Stochastic Hydrogeology

Introduction

2003 ◽  
Author(s):  
Yoram Rubin
Keyword(s):  
Epistemic Uncertainty ◽  
Wide Spectrum ◽  
Real Life ◽  
Mass Conservation ◽  
Point Of View ◽  
The Other ◽  
Intrinsic Variability ◽  
Stochastic Hydrogeology ◽  
Fundamental Point ◽  
Continuum Of States

Stochastic hydrogeology is the study of hydrogeology using physical and probabilistic concepts. It is an applied science because it is oriented toward applications. Its goal is to develop tools for analyzing measurements and observations taken over a sample region in space, and extract information which can then be used for evaluating and modeling the properties of physical processes taking place in this domain, and make risk-qualified predictions of their outcome. By invoking probabilistic concepts to deal with problems of physics, stochastic hydrogeology joins a well-established tradition followed in mining (Matheron, 1965; David, 1977; Journel and Huijbregts, 1978), turbulence (Kolmogorov, 1941; Batchelor, 1949), acoustics (Tatarski, 1961), atmospheric science (Lumley and Panofsky, 1964), composite materials and electrical engineering (Beran, 1968; Batchelor, 1974), and of course statistical mechanics. Stochastic hydrogeology broadens the scope of the deterministic approach to hydrogeology by considering the last as an end member to a wide spectrum of states of knowledge, stretching from deterministic knowledge at one end all the way to maximum uncertainty at the other, with a continuum of states, representing varying degrees of uncertainty in the hydrogeological processes, in between. It provides a formalism for addressing this continuum of states systematically. The departure from the confines of determinism is an important and intuitively appealing paradigm shift, representing the maturing of hydrogeology from an exploratory into an applied discipline. Deterministic knowledge of a site’s hydrogeology is a state we rarely, if ever, find ourselves in, although from a fundamental point of view there is no inherent element of chance in the hydrogeological processes. For example, we know that mass conservation is a deterministic concept, and we are also confident that Darcy’s law works under conditions which are fairly well understood. However, the application of these principles involves a fair amount of conjecture and speculation, and hence when dealing with real-life applications, determinism exists only in the fact that uncertainty and ambiguity are unavoidable, and might as well be studied and understood. The other end of the spectrum is where uncertainty is the largest. Generally speaking, two types of uncertainty exist: intrinsic variability and epistemic uncertainty.

Three-Dimensional Non-Multi-Gaussian Simulation by Including Multiple Types of Information at Non-Colocated Locations

2021 ◽  
Author(s):  
Claus Haslauer ◽  
Bo Xiao ◽  
András Bárdossy ◽  
Olaf Cirpka ◽  
Geoffrey Bohling
Keyword(s):  
Hydraulic Conductivity ◽  
Data Fusion ◽  
Three Dimensional ◽  
Tracer Tests ◽  
Geostatistical Simulation ◽  
Dependence Structure ◽  
Large Set ◽  
Stochastic Hydrogeology ◽  
Spatially Distributed ◽  
Types Of Information

<div> <p><span>The incentive of this presentation is the age-old quest of stochastic hydrogeology: Are we able to better match observed long-tailed breakthrough curves by an improved description of the spatial dependence of saturated hydraulic conductivity (<em>K</em>)?</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>This contribution considers two innovations: We include more information than usual by incorporating multiple types of observations at non-collocated locations (<em>data fusion</em>), and we extract more information than usual from the available measurements by analysing statistical properties that go further than typical second-order moments-based analyses (<em>non-Gaussian geostatistics</em>).</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>The evaluation of these innovations in geostatistical simulation methodologies of spatially distributed fields of <em>K</em> is performed against real-world tracer-tests that were performed at the site of the <em>K</em> measurements. The hypothesis is that fields that contain the most information match the observed solute spreading best.</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>The spatially distributed <em>K</em>- fields were geostatistically simulated using the multi-objective phase annealing (<em>PA</em>) method. To accelerate the asymmetry updating during the PA iterations, a Fourier transform based algorithm is integrated into the three-dimensional PA method. Multiple types of objective functions are included to match the value and/or the order of observations as well as the degree of the “non-Gausianness” (asymmetry). Additionally, “censored measurements” (e.g., high-K measurements above the sensitivity of the device that measures <em>K</em>) are considered.</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>The MAcroDispersion Experiment (MADE) site is considered the holy grail of stochastic hydrogeology as among the well instrumented sites in the world, the variance of the hydraulic conductivity measurements at the MADE site is fairly large and detailed observations of solute spreading are available. In addition to the classic <em>K</em>-measurements obtained via 2611 flowmeter measurements, recently a large set of 31123 <em>K</em>‑measurements obtained via direct push injection logging (DPIL), are available, although not at the same locations where the flowmeter measurements were taken.</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>The influence of including different types of information on the simulated spatially-distributed fields of <em>K</em> are evaluated by analyzing the ensemble spatial moments and the dispersivity of numerical conservative solute tracer tests performed using particle tracking. The improved dependence structure of <em>K</em> with all of the above knowledge contains more information than fields simulated by traditional geostatistical algorithms and expected as a more realistic realization of <em>K</em> at the MADE site and at many other sites where such data-fusion approaches are necessary.</span></p> </div>

scholarly journals The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling

2014 ◽  
Vol 18 (8) ◽  
pp. 2943-2954 ◽  
Author(s):  
X. L. He ◽  
T. O. Sonnenborg ◽  
F. Jørgensen ◽  
K. H. Jensen
Keyword(s):  
High Resolution ◽  
Three Dimensional ◽  
Secondary Data ◽  
Training Image ◽  
Geophysical Data ◽  
Geostatistical Simulation ◽  
Multiple Point ◽  
Multivariate Distributions ◽  
Stochastic Hydrogeology ◽  
Capture Zones

Abstract. Multiple-point geostatistical simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS simulation was applied to different scenarios regarding the TI and soft conditioning. By comparing their output from simulations of groundwater flow and probabilistic capture zone, TI from both sources (directly converted from high-resolution geophysical data and generated by TiGenerator) yields comparable results, even for the probabilistic capture zones, which are highly sensitive to the geological architecture. This study also suggests that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided.

scholarly journals Stochastic Hydrogeology: What Professionals Really Need?

Ground Water ◽  
2007 ◽  
Vol 45 (5) ◽  
pp. 531-541 ◽  
Author(s):  
Philippe Renard
Keyword(s):  
Stochastic Hydrogeology

Applied Stochastic Hydrogeology.

2005 ◽  
Vol 4 (4) ◽  
pp. 984-985
Author(s):  
Tian-Chyi J. Yeh
Keyword(s):  
Stochastic Hydrogeology

In Honour of Distinguished Scientist and Seminal Hydrogeologist Professor Ghislain de Marsily

2020 ◽  
Author(s):  
Craig T. Simmons
Keyword(s):  
Water Management ◽  
Groundwater Hydrology ◽  
Stochastic Hydrogeology ◽  
Renowned Scientist

<p>Professor Ghislain de Marsily is an internationally renowned scientist famed for his contributions to groundwater hydrology and water management. He is a pioneer in the development of stochastic hydrogeology. This presentation will outline and explore the significant contributions made by de Marsily to hydrogeology as a whole and to stochastic hydrogeology in particular. It will examine the effect of his work on defining the discipline of hydrogeology as we know it today, and will go on to show the significant impact his students and colleagues continue to have, inspired by his passion, ideas and enthusiasm for a more sustainable, equitable future for all.</p>

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