scholarly journals Mass conservative three-dimensional water tracer distribution from Markov chain Monte Carlo inversion of time-lapse ground-penetrating radar data

2012 ◽  
Vol 48 (7) ◽  
Author(s):  
Eric Laloy ◽  
Niklas Linde ◽  
Jasper A. Vrugt
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Time Lapse ◽  
Radar Data ◽  
Tracer Distribution ◽  
Ground Penetrating

Analysis of ground penetrating radar data using hierarchical Markov Chain Monte Carlo simulation

2014 ◽  
Vol 41 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Leslie Odartey Mills ◽  
Nii Attoh-Okine
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Ground Penetrating Radar ◽  
Missing Values ◽  
Short Pulse ◽  
Radar Data ◽  
Statistical Estimates ◽  
Original Dataset ◽  
Ground Penetrating

Ground penetrating radar (GPR) is a geophysical method used in highway maintenance to determine subsurface conditions within the right-of-way. GPR operates by using short-pulse radiation of radio-frequency electromagnetic energy to record dissimilarities in electrical properties of subsurface materials. As such, GPR results are susceptible to the transmission frequency used and the inherent properties of different subsurface materials. Uncertainty due to these susceptibilities can lead to ambiguity in the interpretation of GPR data. To distinguish heterogeneity from uncertainty, this paper modeled GPR data on pavement layer thickness using Markov Chain Monte Carlo (MCMC) simulation. MCMC is able to model heterogeneity within a given dataset and was employed to estimate and predict layer thicknesses obtained from GPR data. Simulated results were consistent with field data and provided statistical estimates of missing values in the original dataset. This analysis will aid relevant stakeholders to verify and determine consistency in field GPR data.

Ground-penetrating radar surveys for the detection of preferential flow into soils

2020 ◽  
Author(s):  
Simone Di Prima ◽  
Thierry Winiarski ◽  
Rafael Angulo-Jaramillo ◽  
Ryan D. Stewart ◽  
Mirko Castellini ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Preferential Flow ◽  
Three Dimensional ◽  
Time Lapse ◽  
Water Infiltration ◽  
Single Ring ◽  
Before And After ◽  
Infiltration Tests ◽  
Ground Penetrating ◽  
Preferential Flows

<p>Preferential flow is more the rule than the exception, in particular during water infiltration experiments. In this study, we demonstrate the potential of GPR monitoring to detect preferential flows during water infiltration. We monitored time-lapse ground penetrating radar (GPR) surveys in the vicinity of single-ring infiltration experiments and created a three-dimensional (3D) representation of infiltrated water below the devices. For that purpose, radargrams were constructed from GPR transects conducted over two grids (1 m × 1 m) before and after the infiltration tests. The obtained signal was represented in 3D and a threshold was chosen to part the domain into wetted and non-wetted zones, allowing the determination of the infiltration bulb. That methodology was used to detect the infiltration below the devices and clearly pointed at nonuniform flows in correspondence with the heterogeneous soil structures. The protocol presented in this study represents a practical and valuable tool for detecting preferential flows at the scale of a single ring infiltration experiment.</p>

scholarly journals Basis-constrained Bayesian Markov-chain Monte Carlo difference inversion for geoelectrical monitoring of hydrogeologic processes

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. A37-A42 ◽  
Author(s):  
Erasmus Kofi Oware ◽  
James Irving ◽  
Thomas Hermans
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Computational Cost ◽  
Model Space ◽  
Time Lapse ◽  
Tracer Experiment ◽  
Hydrologic Process ◽  
Spatially Distributed ◽  
Geophysical Imaging

Bayesian Markov-chain Monte Carlo (McMC) techniques are increasingly being used in geophysical estimation of hydrogeologic processes due to their ability to produce multiple estimates that enable comprehensive assessment of uncertainty. Standard McMC sampling methods can, however, become computationally intractable for spatially distributed, high-dimensional problems. We have developed a novel basis-constrained Bayesian McMC difference inversion framework for time-lapse geophysical imaging. The strategy parameterizes the Bayesian inversion model space in terms of sparse, hydrologic-process-tuned bases, leading to dimensionality reduction while accounting for the physics of the target hydrologic process. We evaluate the algorithm on cross-borehole electrical resistivity tomography (ERT) field data acquired during a heat-tracer experiment. We validate the ERT-estimated temperatures with direct temperature measurements at two locations on the ERT plane. We also perform the inversions using the conventional smoothness-constrained inversion (SCI). Our approach estimates the heat plumes without excessive smoothing in contrast with the SCI thermograms. We capture most of the validation temperatures within the 90% confidence interval of the mean. Accounting for the physics of the target process allows the detection of small temperature changes that are undetectable by the SCI. Performing the inversion in the reduced-dimensional model space results in significant gains in computational cost.

Planform and stratigraphic signature of proximal braided streams: remote-sensing and ground-penetrating-radar analysis of the Kicking Horse River, Canadian Rocky Mountains

2020 ◽  
Vol 90 (1) ◽  
pp. 131-149
Author(s):  
Natasha N. Cyples ◽  
Alessandro Ielpi ◽  
Randy W. Dirszowsky
Keyword(s):  
Remote Sensing ◽  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Time Lapse ◽  
Braided Rivers ◽  
Continental Divide ◽  
Channel Belt ◽  
Braided Streams ◽  
And Migration ◽  
Ground Penetrating

ABSTRACT Braided rivers have accumulated a dominant fraction of the terrestrial sedimentary record, and yet their morphodynamics in proximal intermountain reaches are still not fully documented—a shortcoming that hampers a full understanding of sediment fluxes and stratigraphic preservation in proximal-basin tracts. Located in the eastern Canadian Cordillera near the continental divide, the Kicking Horse River is an iconic stream that has served as a model for proximal-braided rivers since the 1970s. Legacy work on the river was based solely on ground observations of small, in-channel bars; here we integrate field data at the scale of individual bars to the entire channel belt with time-lapse remote sensing and ground-penetrating-radar (GPR) imaging, in order to produce a more sophisticated morphodynamic model for the river. Cyclical discharge fluctuations related to both diurnal and seasonal variations in melt-water influx control the planform evolution and corresponding stratigraphic signature of trunk channels, intermittently active anabranch channels, and both bank-attached and mid-channel bars. Three-dimensional GPR fence diagrams of compound-bar complexes are built based on the identification of distinct radar facies related to: i) accretion and migration of unit bars, ii) both downstream and lateral outbuilding of bar-slip foresets; iii) buildup of bedload sheets, iv) channel avulsion, and v) accretion of mounded bars around logs or outsized clasts. Trends observed downstream-ward include decreases in gradient and grain size decreases, trunk-channel shrinkage, intensified avulsion (with increase in abundance for anabranch channels), and a shift from high-relief to low-relief bar topography. The integration of ground sedimentology, time-lapse remote sensing, and GPR imaging demonstrates that proximal-braided streams such as the Kicking Horse River can be critically compared to larger systems located farther away from their source uplands despite obvious scale differences.

scholarly journals Solving for the Orbital Elements of Binary Systems using MCMC Simulations.

2013 ◽  
Vol 8 (S299) ◽  
pp. 52-53
Author(s):  
Kyle Mede ◽  
Timothy D. Brandt
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Observational Data ◽  
Confidence Intervals ◽  
Formation Mechanism ◽  
Binary Systems ◽  
Three Dimensional ◽  
Orbital Elements ◽  
Orbital Parameters

AbstractRecent simulation and observational data have been used to investigate the ability of Kozai oscillations to explain the formation of “hot Jupiter” planetary systems. One of the first exoplanets discovered, τ Boo Ab, orbits a star with a binary companion, making it an excellent testbed for this scenario. We have written a three-dimensional Markov Chain Monte Carlo (MCMC) simulator to constrain the orbit of the distant stellar companion τ Boo B, and are currently deriving orbital parameters and confidence intervals. These orbital parameters will confirm or reject Kozai oscillations as a plausible formation mechanism for τ Boo Ab.

A geophysical investigation of the active Hockley Fault System near Houston, Texas

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. B177-B185 ◽  
Author(s):  
Shuhab D. Khan ◽  
Robert R. Stewart ◽  
Maisam Otoum ◽  
Li Chang
Keyword(s):  
Ground Penetrating Radar ◽  
Fault Location ◽  
Gravity Data ◽  
Time Lapse ◽  
Radar Data ◽  
Airborne Lidar ◽  
Fault System ◽  
Geophysical Investigation ◽  
Geophysical Surveys ◽  
Ground Penetrating

Sedimentation and deformation toward the Gulf of Mexico Basin cause faulting in the coastal regions. In particular, many active (but non-seismic) faults underlie the Houston metropolitan area. Using geophysical data, we have examined the Hockley Fault System in northwest Harris County. Airborne LiDAR is an effective tool to identify fault scarps and we have used it to identify several new faults and assemble an updated map for the faults in Houston and surrounding areas. Two different LiDAR data sets (from 2001 to 2008) provide time-lapse images and suggest elevation changes across the Hockley Fault System at the rate of 10.9 mm/yr. This rate is further supported by GPS data from a station located on the downthrown side of the Hockley Fault System indicating movement at 13.8 mm/yr. To help illuminate the subsurface character of the faults, we undertook geophysical surveys (ground-penetrating radar, seismic reflection, and gravity) across two strands of the Hockley Fault System. Ground-penetrating radar data show discontinuous events to a depth of 10 m at the main fault location. Seismic data, from a vibroseis survey along a 1-km line perpendicular to the fault strike, indicate faulting to at least 300-m depth. The faults have a dip of about 70°. Gravity data show distinct changes across the fault. However, there are two contrasting Bouguer anomalies depending on the location of the transects and their underlying geology. Our geophysical surveys were challenged by urban features (especially traffic and access). However, the survey results consistently locate the fault and hold significant potential to understand its deformational features as well as assist in associated building zoning.

Attribute-based analysis of time-lapse ground-penetrating radar data

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. H1-H8 ◽  
Author(s):  
Niklas Allroggen ◽  
Jens Tronicke
Keyword(s):  
Ground Penetrating Radar ◽  
Preferential Flow ◽  
Synthetic Data ◽  
Structural Similarity ◽  
Time Lapse ◽  
Radar Data ◽  
Hydrological Processes ◽  
Time Shift ◽  
Noisy Input ◽  
Ground Penetrating

Analysis of time-lapse ground-penetrating radar (GPR) data can provide information regarding subsurface hydrological processes, such as preferential flow. However, the analysis of time-lapse data is often limited by data quality; for example, for noisy input data, the interpretation of difference images is often difficult. Motivated by modern image-processing tools, we have developed two robust GPR attributes, which allow us to distinguish amplitude (contrast similarity) and time-shift (structural similarity) variations related to differences between individual time-lapse GPR data sets. We tested and evaluated our attributes using synthetic data of different complexity. Afterward, we applied them to a field data example, in which subsurface flow was induced by an artificial rainfall event. For all examples, we identified our structural similarity attribute to be a robust measure for highlighting time-lapse changes also in data with low signal-to-noise ratios. We determined that our new attribute-based workflow is a promising tool to analyze time-lapse GPR data, especially for imaging subsurface hydrological processes.

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