Yıldırım, İsa Eren
In Seismic prospecting, huge amounts of data are collected and processed to infer the structural and lithological composition of the subsurface. The key step in this procedure is velocity model building. First arrival traveltime inversion is one of the velocity model building tools commonly used for predicting near-surface velocity structures in seismic exploration. Conventionally, the inversion is carried out using ray-based methods or gradient-based algorithms. Though the gradient-based algorithms find the gradient that is needed to update the model parameters without requiring ray tracing, it can be computationally demanding. On the other hand, despite its robustness and efficiency ray-based methods suffer from complex regions as the ray theory relies on the high-frequency approximation. Instead of using these approaches for a traveltime inversion problem, we propose a machine learning based approach, specifically harnessing the physics informed neural networks exploiting the mathematical model represented by the eikonal equation to estimate the near-surface subsurface velocities. Through synthetic tests and the application of real data, we show the reliability of the physics informed machine learning based traveltime inversion which can be a potential alternative tool to the traditional tomography frameworks.


Singularly perturbed diffusion-advection-reaction processes on extremely large three-dimensional curvilinear networks with a periodic microstructure -- efficient solution strategies based on homogenization theory
Kropat, Erik; Meyer-Nieberg, Silja; Weber, Gerhard-Wilhelm (American Institute of Mathematical Sciences (AIMS), 2016-8)
Boundary value problems on large periodic networks arise in many applications such as soil mechanics in geophysics or the analysis of photonic crystals in nanotechnology. As a model example, singularly perturbed elliptic differential equations of second order are addressed. Typically, the length of periodicity is very small compared to the size of the covered region. The overall complexity of the networks raises serious problems on the computational side. The high density of the graph, the huge number of ed...
Flow dynamics at the origin of thin clayey sand lacustrine turbidites: Examples from Lake Hazar, Turkey
Hage, Sophie; Hubert-Ferrari, Aurelia; Lamaır, Laura; Avşar, Ulaş; El Ouahabı, Meriam; Van Daele, Maarten; Boulvaın, Frederic; Bahrı, Mohamed Ali; Seret, Alain; Plenevaux, Alain (2017-12-01)
Turbidity currents and their deposits can be investigated using several methods, i.e. direct monitoring, physical and numerical modelling, sediment cores and outcrops. The present study focused on thin clayey sand turbidites found in Lake Hazar (Turkey) occurring in eleven clusters of closely spaced thin beds. Depositional processes and sources for three of those eleven clusters are studied at three coring sites. Bathymetrical data and seismic reflection profiles are used to understand the specific geomorph...
Thermally stimulated currents in n-InS single crystals
Hasanlı, Nızamı; Yuksek, NS (Elsevier BV, 2003-03-24)
Thermally stimulated current measurements are carried out on as-grown n-InS single crystals in the temperature range of 10-125 K. Experimental evidence is found for four trapping centers present in InS. They are located at 20, 35, 60 and 130 meV. The trap parameters have been determined by various methods of analysis, and they agree well with each other.
Coupled physical and biochemical data driven simulations of Black Sea in spring-summer: real-time forecast and data assimilation
Besiktepe, ST (2002-12-06)
Data driven simulations in the Black Sea based upon observations during May-June 2001 in the SW part of the basin and coupled 3D physical and biochemical models have been carried out. The model was initialised with the data obtained during 22-28 May, 2001 and ran until 15 June, 2001. The data obtained in the second leg during 12-18 June, 2001 was assimilated into the model. At the time of the assimilation, the model forecast and the data were also compared. Quantitative and qualitative comparisons of the co...
Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites
Kockan, Umit; Ozturk, Fahrettin; Evis, Zafer (2014-01-01)
In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted ...
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