scholarly journals Constrained optimization framework for joint inversion of geophysical data sets

2013 ◽  
Vol 195 (3) ◽  
pp. 1745-1762 ◽  
Author(s):  
A. Sosa ◽  
A. A. Velasco ◽  
L. Velazquez ◽  
M. Argaez ◽  
R. Romero
Keyword(s):  
Constrained Optimization ◽  
Joint Inversion ◽  
Geophysical Data ◽  
Data Sets ◽  
Optimization Framework

scholarly journals Structure-coupled joint inversion of geophysical and hydrological data

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. ID1-ID14 ◽  
Author(s):  
Tobias Lochbühler ◽  
Joseph Doetsch ◽  
Ralf Brauchler ◽  
Niklas Linde
Keyword(s):  
Hydraulic Conductivity ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Geophysical Data ◽  
Data Sets ◽  
Northern Switzerland ◽  
Hydrological Data ◽  
Spatial Coverage ◽  
Geophysical Imaging ◽  
Constraints Model

In groundwater hydrology, geophysical imaging holds considerable promise for improving parameter estimation, due to the generally high resolution and spatial coverage of geophysical data. However, inversion of geophysical data alone cannot unveil the distribution of hydraulic conductivity. Jointly inverting geophysical and hydrological data allows us to benefit from the advantages of geophysical imaging and, at the same time, recover the hydrological parameters of interest. We have applied a coupling strategy between geophysical and hydrological models that is based on structural similarity constraints. Model combinations, for which the spatial gradients of the inferred parameter fields are not aligned in parallel, are penalized in the inversion. This structural coupling does not require introducing a potentially weak, unknown, and nonstationary petrophysical relation to link the models. The method was first tested on synthetic data sets and then applied to two combinations of geophysical/hydrological data sets from a saturated gravel aquifer in northern Switzerland. Crosshole ground-penetrating radar (GPR) traveltimes were jointly inverted with hydraulic tomography data, as well as with tracer mean arrival times, to retrieve the 2D distribution of GPR velocities and hydraulic conductivities. In the synthetic case, incorporating the GPR data through a joint inversion framework improved the resolution and localization properties of the estimated hydraulic conductivity field. For the field study, recovered hydraulic conductivities were in general agreement with flowmeter data.

scholarly journals A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021 ◽  
Author(s):  
Francesca Pace ◽  
Alessandro Santilano ◽  
Alberto Godio
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Modeling ◽  
Critical Evaluation ◽  
Particle Swarm ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Data Sets ◽  
Swarm Optimization ◽  
Geophysical Modeling ◽  
Original Works

AbstractThis paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

scholarly journals Dissecting a Zombie: Shallow Volcanic Structure Revealed by Multiple Geophysical Data Sets at Uturuncu Volcano, Bolivia

2021 ◽  
Author(s):  
Patricia MacQueen ◽  
Joachim Gottsmann ◽  
Matthew Pritchard ◽  
Nicola Young ◽  
Faustino Ticona J. ◽  
...  
Keyword(s):  
Geophysical Data ◽  
Data Sets ◽  
Volcanic Structure

scholarly journals Adjoint Slip Inversion under a Constrained Optimization Framework: Revisiting the 2006 Guerrero Slow Slip Event

2020 ◽  
Author(s):  
Josué Tago ◽  
Víctor M. Cruz-Atienza ◽  
Carlos Villafuerte ◽  
Takuya Nishimura ◽  
Vladimir Kostoglodov ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Slow Slip ◽  
Slow Slip Event ◽  
Optimization Framework ◽  
Slip Event

Quasi-2D inversion of DCR and TDEM data for shallow investigations

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F239-F250 ◽  
Author(s):  
Fernando A. Monteiro Santos ◽  
Hesham M. El-Kaliouby
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Complex Environments ◽  
Inversion Algorithm ◽  
2D Inversion ◽  
New Approach ◽  
Earth Models ◽  
Time Domain Electromagnetic ◽  
Inversion Techniques

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

EPOS-Norway Portal

2021 ◽  
Author(s):  
Jan Michalek ◽  
Kuvvet Atakan ◽  
Christian Rønnevik ◽  
Helga Indrøy ◽  
Lars Ottemøller ◽  
...  
Keyword(s):  
Web Services ◽  
Visual Analysis ◽  
Earth Science ◽  
National Research Council ◽  
Geophysical Data ◽  
Multidimensional Data ◽  
Data Sets ◽  
Complex Data ◽  
Submarine Landslides ◽  
Data Repositories

<p>The European Plate Observing System (EPOS) is a European project about building a pan-European infrastructure for accessing solid Earth science data, governed now by EPOS ERIC (European Research Infrastructure Consortium). The EPOS-Norway project (EPOS-N; RCN-Infrastructure Programme - Project no. 245763) is a Norwegian project funded by National Research Council. The aim of the Norwegian EPOS e‑infrastructure is to integrate data from the seismological and geodetic networks, as well as the data from the geological and geophysical data repositories. Among the six EPOS-N project partners, four institutions are providing data – University of Bergen (UIB), - Norwegian Mapping Authority (NMA), Geological Survey of Norway (NGU) and NORSAR.</p><p>In this contribution, we present the EPOS-Norway Portal as an online, open access, interactive tool, allowing visual analysis of multidimensional data. It supports maps and 2D plots with linked visualizations. Currently access is provided to more than 300 datasets (18 web services, 288 map layers and 14 static datasets) from four subdomains of Earth science in Norway. New datasets are planned to be integrated in the future. EPOS-N Portal can access remote datasets via web services like FDSNWS for seismological data and OGC services for geological and geophysical data (e.g. WMS). Standalone datasets are available through preloaded data files. Users can also simply add another WMS server or upload their own dataset for visualization and comparison with other datasets. This portal provides unique way (first of its kind in Norway) for exploration of various geoscientific datasets in one common interface. One of the key aspects is quick simultaneous visual inspection of data from various disciplines and test of scientific or geohazard related hypothesis. One of such examples can be spatio-temporal correlation of earthquakes (1980 until now) with existing critical infrastructures (e.g. pipelines), geological structures, submarine landslides or unstable slopes.  </p><p>The EPOS-N Portal is implemented by adapting Enlighten-web, a server-client program developed by NORCE. Enlighten-web facilitates interactive visual analysis of large multidimensional data sets, and supports interactive mapping of millions of points. The Enlighten-web client runs inside a web browser. An important element in the Enlighten-web functionality is brushing and linking, which is useful for exploring complex data sets to discover correlations and interesting properties hidden in the data. The views are linked to each other, so that highlighting a subset in one view automatically leads to the corresponding subsets being highlighted in all other linked views.</p>

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