Most of the complex research problems can be formulated as optimization problems. Emergence of big data technologies have also commenced the generation of complex optimization problems with large size. The high computational cost of these problems has rendered the development of optimization algorithms with parallelization. Particle swarm optimization (PSO) algorithm is one of the most popular swarm intelligence-based algorithm, which is enriched with robustness, simplicity and global search capabilities. However, one of the major hindrance with PSO is its susceptibility of getting entrapped in local optima and; alike other evolutionary algorithms the performance of PSO gets deteriorated as soon as the dimension of the problem increases. Hence, several efforts are made to enhance its performance that includes the parallelization of PSO. The basic architecture of PSO inherits a natural parallelism, and receptiveness of fast processing machines has made this task pretty convenient. Therefore, parallelized PSO (PPSO) has emerged as a well-accepted algorithm by the research community. Several studies have been performed on parallelizing PSO algorithm so far. Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications.

A Survey on Parallel Particle Swarm Optimization Algorithms

A spectral element method for the approximate solution of linear elastodynamic equations, set in a weak form, is shown to provide an efficient tool for simulating elastic wave propagation in realistic geological structures in two- and three-dimensional geometries. The computational domain is discretized into quadrangles, or hexahedra, defined with respect to a reference unit domain by an invertible local mapping. Inside each reference element, the numerical integration is based on the tensor-product of a Gauss–Lobatto–Legendre 1-D quadrature and the solution is expanded onto a discrete polynomial basis using Lagrange interpolants. As a result, the mass matrix is always diagonal, which drastically reduces the computational cost and allows an efficient parallel implementation. Absorbing boundary conditions are introduced in variational form to simulate unbounded physical domains. The time discretization is based on an energy-momentum conserving scheme that can be put into a classical explicit-implicit predictor/multicorrector format. Long term energy conservation and stability properties are illustrated as well as the efficiency of the absorbing conditions. The accuracy of the method is shown by comparing the spectral element results to numerical solutions of some classical two-dimensional problems obtained by other methods. The potentiality of the method is then illustrated by studying a simple three-dimensional model. Very accurate modelling of Rayleigh wave propagation and surface diffraction is obtained at a low computational cost. The method is shown to provide an efficient tool to study the diffraction of elastic waves and the large amplification of ground motion caused by three-dimensional surface topographies. Copyright © 1999 John Wiley & Sons, Ltd.

Castillo-covarrubias jm, sanchez-sesma fj. the spectral element method for elastic wave equations-application to 2-d and 3-d seismic problems

Due to the broadband nature of distributed acoustic sensing (DAS) measurement, a roadside section of the Stanford DAS-2 array can record seismic signals from various sources. For example, ...

Near-surface characterization using a roadside distributed acoustic sensing array

This paper describes a new method based on the particle swarm optimisation (PSO) technique for interpreting the second moving average (SMA) residual gravity anomalies. The SMA anomalies are deduced from the measured gravity data to eradicate the regional anomaly by utilising filters of consecutive window lengths (s-value). The buried structural parameters are the amplitude factor (A), depth (z), location (d) and shape (q) that are estimated from the PSO method. The discrepancy between the measured and the predictable gravity anomaly is estimated by the root mean square error. The PSO method is applied to two different theoretical and three real data sets from Cuba, Canada and India. The model parameters inferred from the method developed here are compared with the available geological and geophysical information.

/pdf/gravity-data-interpretation-using-the-particle-swarm-16oqrsr472.pdf

Gravity data interpretation using the particle swarm optimisation method with application to mineral exploration

https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/met.1817

Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models

A parallel competitive Particle Swarm Optimization for non-linear first arrival traveltime tomography and uncertainty quantification

Injection of CO2 for geologic carbon sequestration into deep sedimentary formations involves fluid pressure increases that engage hydromechanical processes that can cause seismicity by activation of existing faults. In this work, we use a coupled multiphase fluid flow and geomechanical simulator to model spatiotemporal fluid pressure and stress changes in order to study the poroelastic effect of CO2 injection on faults in crystalline basement rock below the injection zone. The seismicity rate along features interpreted to be basement faults is modeled using Dieterich's rate-and-state earthquake nucleation model. The methodology is applied to microseismicity detected during CO2 injection into the Mount Simon formation during the Illinois Basin—Decatur Project. The modeling accurately captures an observed reduction in seismicity rate when the injection in the second well was into a slightly shallower zone above the base of the Mount Simon formation. Moreover, the modeling shows that it is important to consider poroelastic stress changes, in addition to fluid pressure changes for accurately modeling of the observed seismicity rate. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2021JB023496

Coupled Hydromechanical Modeling of Induced Seismicity From CO 2 Injection in the Illinois Basin

When setting up a model, the most time-consuming part is preparing the input data required to run a TOUGH simulation due to its error-prone text-based fixed-format input files and the lack of proper built-in meshing tool to model complex geological structures. In addition, a TOUGH mesh is optimally represented by a Voronoi graph as TOUGH uses an integral finitedifference formulation (Narasimhan & Witherspoon, 1976) to solve the coupled fluid and heat flow equations. A TOUGH mesh is only represented as a set of elements and connections without any reference to a coordinate system usually required for post-processing with common visualization softwares (e.g. ParaView, Tecplot and VisIt).

/pdf/toughio-pre-and-post-processing-python-library-for-tough-41jk4wtd4n.pdf

toughio: Pre- and post-processing Python library for TOUGH

A competitive particle swarm optimization for nonlinear first arrival traveltime tomography

Injection of CO2 for geologic carbon sequestration (GCS) into deep sedimentary formations involves fluid pressure increases that engage hydromechanical processes that can cause seismicity by activa...

Coupled Hydromechanical Modeling of Induced Seismicity from CO2 Injection in the Illinois Basin

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of {limit} columns in the table. Remove at least 1 column to add or create another one.

Keurfon Luu

Author Tools

Chat about Author

Papers

A parallel competitive Particle Swarm Optimization for non-linear first arrival traveltime tomography and uncertainty quantification

Coupled Hydromechanical Modeling of Induced Seismicity From CO <sub>2</sub> Injection in the Illinois Basin

toughio: Pre- and post-processing Python library for TOUGH

A competitive particle swarm optimization for nonlinear first arrival traveltime tomography

Coupled Hydromechanical Modeling of Induced Seismicity from CO2 Injection in the Illinois Basin