Abstract Full waveform inversion (FWI) in the time domain has limitations due to large computing and memory requirements. Some studies have addressed this problem by using machine learning techniques. Most FWI directly estimate subsurface velocity structure training seismic data generated through various synthetic models obtain structure. In study, we propose a method convert common midpoint (C...

Full-waveform inversion (FWI) can resolve subsurface physical properties to high resolutions, yet high-performance computing resources have only recently made it practical invert for frequencies. A benefit of high-frequency FWI is that recovered velocity models be differentiated in space produce high-quality depth images (FWI images) a comparable resolution conventional reflection images. Here,...

Abstract Full-waveform inversion (FWI) is among the most effective methods of velocity modelling in seismic exploration. However, because strong nonlinearity FWI, if target geobody not sharply different from that its surroundings, total variation (TV) model will be sufficiently sparse. To alleviate this issue, we propose a novel TV-regularised FWI method can consider sparsity high-order regular...

Abstract A new fire danger index is proposed to overcome one of the most important limitations current metrics. The occurrence probability (FOPI) combines Canadian weather (FWI) with remote observations vegetation characteristics better predict landscape flammability. FOPI designed improve predictions in all fuel-limited environments where driven by short-term drying intermittently-available fu...

Abstract. A sparse 3D seismic survey was acquired over the Blötberget iron oxide deposits of Ludvika Mines in south-central Sweden. The main aim to delineate deeper extension mineralisation and better understand its nature associated fault systems for mine planning purposes. To obtain a high-quality image depth, we applied time-domain acoustic full-waveform inversion (FWI) build high-resolution...

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due increasing size and resolution seismic data. Moreover, it non-convex problem can encounter local minima limited accuracy initial ve...

By taking advantage of the information carried by entire seismic wavefield, Full Waveform Inversion (FWI) is able to yield higher resolution subsurface velocity models than traveltime tomography. However, FWI heavily relies on knowledge source and good initial models, could be easily trapped into local minima caused cycle skipping issue because its high nonlinearity. To mitigate these issues in...

Full waveform inversion (FWI) is a non-linear optimization problem based on full-wavefield modeling to obtain quantitative information of subsurface structure by minimizing the difference between observed seismic data and predicted wavefield. The limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method an effective quasi-Newton in FWI due its high efficiency with low calculation storage ...

Building realistic and reliable models of the subsurface is primary goal seismic imaging. We have constructed an ensemble convolutional neural networks (CNNs) to build velocity directly from data. Most other approaches attempt map full data into 2D labels. exploit regularity acquisition train CNNs gathers neighboring common midpoints (CMPs) vertical 1D logs. This allows us integrate well-log in...