Research papers
2009 · Cambridge University Press eBooks · 2,318 citations
The Rock Physics Handbook addresses the relationships between geophysical observations and the underlying physical properties of rocks. It distills a vast quantity of background theory and laboratory results into a series of concise chapters that provide practical solutions to problems in geophysical data interpretation. This expanded second edition presents major new chapters on statistical rock physics and velocity-porosity-clay models for clastic sediments. Other new and expanded topics include anisotropic seismic signatures, borehole waves, models for fractured media, poroelastic models, and attenuation models. This new edition also provides an enhanced set of appendices with key empirical results, data tables, and an atlas of reservoir rock properties – extended to include carbonates, clays, gas hydrates, and heavy oils. Supported by a website hosting MATLAB routines for implementing the various rock physics formulas, this book is a vital resource for advanced students and university faculty, as well as petroleum industry geophysicists and engineers.
2020 · Cambridge University Press eBooks · 1,711 citations
Responding to the latest developments in rock physics research, this popular reference book has been thoroughly updated while retaining its comprehensive coverage of the fundamental theory, concepts, and laboratory results. It brings together the vast literature from the field to address the relationships between geophysical observations and the underlying physical properties of Earth materials - including water, hydrocarbons, gases, minerals, rocks, ice, magma and methane hydrates. This third edition includes expanded coverage of topics such as effective medium models, viscoelasticity, attenuation, anisotropy, electrical-elastic cross relations, and highlights applications in unconventional reservoirs. Appendices have been enhanced with new materials and properties, while worked examples (supplemented by online datasets and MATLAB® codes) enable readers to implement the workflows and models in practice. This significantly revised edition will continue to be the go-to reference for students and researchers interested in rock physics, near-surface geophysics, seismology, and professionals in the oil and gas industries.
2009 · South African Journal of Geology · 18 citations
Research Article| December 01, 2009 DETERMINISTIC SEISMIC GROUND MOTION MODELLING OF THE GREATER ACCRA METROPOLITAN AREA, SOUTHEASTERN GHANA P.E. AMPONSAH; P.E. AMPONSAH Geological Survey Department, Accra, Ghana, e-mail: pekua2@yahoo.com Search for other works by this author on: GSW Google Scholar B.K. BANOENG-YAKUBO; B.K. BANOENG-YAKUBO Geology Department, University of Ghana, Legon, Ghana, e-mail: bbruce@ug.edu.gh Search for other works by this author on: GSW Google Scholar G.F. PANZA; G.F. PANZA Department of Earth Sciences, University of Trieste, Italy, The Abdus Salam International Centre for Theoretical Physics- ESP SAND group, Trieste, Italy, e-mail: panza@dst.units.it Search for other works by this author on: GSW Google Scholar F. VACCARI F. VACCARI Department of Earth Sciences, University of Trieste, Italy, e-mail: vaccari@dst.units.it Search for other works by this author on: GSW Google Scholar Author and Article Information P.E. AMPONSAH Geological Survey Department, Accra, Ghana, e-mail: pekua2@yahoo.com B.K. BANOENG-YAKUBO Geology Department, University of Ghana, Legon, Ghana, e-mail: bbruce@ug.edu.gh G.F. PANZA Department of Earth Sciences, University of Trieste, Italy, The Abdus Salam International Centre for Theoretical Physics- ESP SAND group, Trieste, Italy, e-mail: panza@dst.units.it F. VACCARI Department of Earth Sciences, University of Trieste, Italy, e-mail: vaccari@dst.units.it Publisher: Geological Society of South Africa First Online: 09 Mar 2017 Online ISSN: 1996-8590 Print ISSN: 1012-0750 © 2009 Geological Society of South Africa South African Journal of Geology (2009) 112 (3-4): 317–328. https://doi.org/10.2113/gssajg.112.3-4.317 Article history First Online: 09 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation P.E. AMPONSAH, B.K. BANOENG-YAKUBO, G.F. PANZA, F. VACCARI; DETERMINISTIC SEISMIC GROUND MOTION MODELLING OF THE GREATER ACCRA METROPOLITAN AREA, SOUTHEASTERN GHANA. South African Journal of Geology 2009;; 112 (3-4): 317–328. doi: https://doi.org/10.2113/gssajg.112.3-4.317 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySouth African Journal of Geology Search Advanced Search Abstract The seismic ground motion of the greater Accra Metropolitan Area has been modelled for land use planning and disaster mitigation. The deterministic computation, a hybrid method based on the modal summation and finite difference method was used. Using these techniques, the seismic ground motion along four profiles located in the metropolis has been computed. The 1939 earthquake of magnitude 6.5 (ML ) was used as the scenario earthquake. Synthetic seismic waveforms from which parameters for engineering design such as peak ground acceleration, velocity and spectral amplifications have been produced along the geological cross sections. The peak ground acceleration and velocity computed for the metropolis ranges from 0.14 g to 0.57 g and 9.2 cms−1 to 37.1 cms−1, respectively. These correspond to intensity ranging from VII to IX on the Modified Mercalli Intensity scale. Areas in the metropolis underlain by unconsolidated sediments experience the largest shakings. The results illustrated represent a useful guide for civil engineers in the design of buildings in the metropolis for safe and sustainable development. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
2025 · AIP Advances · 15 citations
This paper aims to explore the nonlinear dynamics of the well-known nonlinear partial differential equations, namely, Estevez–Mansfield–Clarkson (EMC) and Sharma–Taso–Olver (STO) equations. The presented models have useful applications in various fields. The EMC equation clarifies the complex dynamics of waves in shallow water and fluid physics. In nuclear physics, the STO model is pertinent to particle fission and fusion processes. This work offers Riccati sub-equation neural networks to provide exact solutions for space–time partial differential equations. The proposed method incorporates the solutions of the Riccati problem into neural networks. Neural networks are multi-layer computer models with activation functions and weight functions that connect neurons across the input, hidden, and output layers. In this approach, each neuron in the first hidden layer is assigned to the solutions of the Riccati equation. Consequently, the new trial functions are established. The proposed method provides exact solutions to the studied models in the forms of bright, dark, singular, combined, and complex solitons. Moreover, generalized hyperbolic function solutions, trigonometric function solutions, and generalized rational solutions are also recovered. This study introduces innovative solutions as the proposed methodology is used in the neural network model. A variety of graphs have been sketched for the physical behavior of the obtained solutions. By establishing the dependability of the method used, this research’s outcomes could advance our grasp of nonlinear behavior in targeted systems.
2020 · Scientific Reports · 12 citations
Due to the lack of petroleum resources, stratigraphic reservoirs have become an important source of future discoveries. We describe a methodology for predicting reservoir sands from complex reservoir seismic data. Data analysis involves a bio-integrated framework called multi-modal machine learning fusion (MMMLF) based on neural networks. First, acoustic-related seismic attributes from post-stack seismic data were used to characterize the reservoirs. They enhanced the understanding of the structure and spatial distribution of petrophysical properties of lithostratigraphic reservoirs. The attributes were then classified as varied modal inputs into a central fusion engine for prediction. We applied the method to a dataset from Northeast China. Using seismic attributes and rock physics relationships as input data, MMMLF was performed to predict the spatial distribution of lithology in the Upper Guantao substrata. Despite the large scattering in the acoustic-related data properties, the proposed MMMLF methodology predicted the distribution of lithological properties through the gamma ray logs. Moreover, complex stratigraphic traps such as braided fluvial sandstones in the fluvio-deltaic deposits were delineated. These findings can have significant implications for future exploration and production in Northeast China and similar petroleum provinces around the world.