We present future fire danger scenarios for the countries bordering the Mediterranean areas of Europe and north Africa building on a multi-model ensemble of state-of-the-art regional climate projections from the EU-funded project ENSEMBLES. Fire danger is estimated using the Canadian Forest Fire Weather Index (FWI) System and a related set of indices. To overcome some of the limitations of ENSE...

Carbon capture and storage (CCS) is a key technology that directly removes industry driven CO2 to achieve carbon neutrality. In the process of CCS, it necessary monitor whether injected properly stored not leaking. The behavior can be investigated using 4D seismic survey compares data before after injection. We proposed two-step monitoring with diffraction-angle filtering (DAF) effectively loca...

Wavelet estimation plays an important role in many seismic processes like impedance inversion, amplitude versus offset (AVO) and full waveform inversion (FWI). Statistical methods of wavelet estimation away from well control are a desirable tool to support seismic signal processing. One of these methods based on Homomorphic analysis has long intrigued as a potentially elegant solution to the wa...

In this abstract, we describe how to improve time domain full waveform inversion using source wavelet convolution, windowed back propagation and source side illumination. Instead of estimating the source wavelet from field data, a user defined source wavelet can be convolved to field data. This convolution makes waveform matching between modeled and field data easier. Increasing time window app...

Much of the computational cost involved in full-waveform inversion comes from the solution of the wave equation in a large domain. These computations must be done for the entire domain through which we expect waves to pass for a particular survey, despite the fact that our region of interest is often significantly smaller. In addition to the wasted time spent propagating waves through less impo...

Full-waveform inversion (FWI) is considered one of the most promising interpretation tools for hydrogeological applications using ground-penetrating radar. However, FWI has had limited practical uptake several reasons: large computational requirements, an inability to reconstruct loss mechanisms soil, and need a good initial starting model. We aim address these issues via novel subject fractall...

Abstract Seismic velocity modeling is a crucial step in seismic processing that enables the use of information from both and wells to map depth thickness subsurface layers interpreted images. The can be obtained form normal moveout (NMO) or by an inversion (optimization) process such as full-waveform (FWI). These methods have several limitations. limitations include enormous time consumption ca...

The anisotropic full-waveform inversion (FWI) is a seismic inverse problem for multiple parameters, which aims to simultaneously reconstruct the vertical velocity and parameters of earth’s subsurface. This multiparameter suffers from two issues. First, objective function data fitting less sensitive parameters. Second, crosstalk effect among different worsens model update in iterative inversion....

Seismic full-waveform inversion (FWI) uses full seismic records to estimate the subsurface velocity structure. This requires a highly nonlinear and nonunique inverse problem be solved; therefore, Bayesian methods have been used quantify uncertainties in solution. Variational inference optimization efficiently provide solutions. However, previously method has only applied transmission FWI with s...