The physics that describes the seismic response of an interval saturated porous rocks with known petrophysical properties is relatively well understood and includes rock physics, petrophysics, wave propagation models. main goal reservoir characterization to predict fluid given a set measurements by combining geophysical models mathematical methods. This modeling challenge generally formulated as inverse problem. most common problem (or elastic) inversion, i.e., estimation elastic properties,...

Among the large variety of mathematical and computational methods for estimating reservoir properties such as facies petrophysical variables from geophysical data, deep machine-learning algorithms have gained significant popularity their ability to obtain accurate solutions inverse problems in which physical models are partially unknown. Solutions classification inversion generally not unique, uncertainty quantification studies required quantify model predictions determine precision results....

Floating plastic debris represent an environmental threat to the maritime environment as they drift oceans. Developing tools detect and remove them from our oceans is critical. We present approach distinguish suspect other floating materials (i.e., driftwood, seaweed, sea snot, foam, pumice) using Sentinel-2 data. use extreme gradient boosting trained with data compiled published works complemented by manual interpretation of satellite images. The method two spectral bands seven indices...

We have developed a new iterative geostatistical seismic amplitude variation with angle (AVA) inversion algorithm that inverts prestack data, sorted by gathers, directly for high-resolution density, P-wave velocity, S-wave and facies models. This novel inverse procedure is based on two key main principles: the use of stochastic sequential simulation cosimulation as perturbation technique model parameter space global optimizer crossover genetic to converge simulated earth models toward an...

The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination factors, including social ones, which lead to significant differences in evolution spatiotemporal pattern between within countries. Usually, spatial smoothing techniques are used map health outcomes, rarely uncertainty predictions assessed. As an alternative, we propose apply direct block sequential simulation model distribution COVID-19 infection risk...

Abstract Missing data is a frequent problem in meteorological and hydrological temporal observation sets. Finding effective solutions to this essential because complete time series are required conduct reliable analyses. This study used daily rainfall from 60 rain gauges spatially distributed within Portugal's Guadiana River basin over 30-year reference period (1976–2005). Gap-filling approaches using kriging-based interpolation methods (i.e. ordinary kriging simple cokriging) presented...

Abstract Predicting the subsurface spatial distribution of geological facies from fullstack geophysical data is a main step in geo-modeling workflow for energy exploration and environmental tasks requires solving an inverse problem. Generative adversarial networks (GANs) have shown great potential geologically accurate probabilistic modeling, but existing methods require multiple sequential steps do not account uncertainty facies-dependent continuous properties, linking to observed data....

A Bottom Simulating Reflector (BSR) is a seismic feature closely related to marine gas hydrate as it usually regarded the response of base stability zone in profiles. BSRs are widely distributed Makran accretionary wedge, and double observed at some locations. Double appear on profiles two layers located distinct depths but with large lateral amplitude variations. Based multi-channel reflection data acquired over this work studies origin BSR wedge its association fluid escape events. Our...

Autonomous Underwater Vehicle (AUV) trajectory planning for oceanographic surveys should ensure comprehensive data collection enhanced mission success. By strategically navigating and targeting high-value points, the AUV can operate longer gather more essential information ocean modelling. Here, we propose a geostatistical modelling workflow to predict temperature with spatial uncertainty maps, representing regions limited knowledge about properties from where navigation paths be devised.A...

The prediction of rock properties in the subsurface from geophysical data generally requires solution a mathematical inverse problem. Because large size (seismic) sets and models, it is common to reduce dimension problem by applying reduction methods considering reparameterization model and/or data. Especially for high-dimensional nonlinear problems, which analytical not available closed form iterative sampling or optimization must be applied approximate solution, computational cost...

Due to the nature of seismic inversion problems, there are multiple possible solutions that can equally fit observed data while diverging from real subsurface model. Consequently, it is important assess how inverse-impedance models converging toward For this purpose, we evaluated a new methodology combine multidimensional scaling (MDS) technique with an iterative geostatistical elastic algorithm. The algorithm inverted partial angle stacks directly for acoustic and impedance (AI EI) models....

ABSTRACT Seismic inversion plays an important role in reservoir modelling and characterisation due to its potential for assessing the spatial distribution of sub‐surface petro‐elastic properties. amplitude‐versus‐angle methodologies allow retrieve P‐wave S‐wave velocities density individually allowing a better existing litho‐fluid facies. We present iterative geostatistical seismic algorithm that inverts pre‐stack data, sorted by angle gather, directly for: density; P‐wave; velocity models....

Reservoir models are numerical representations of the subsurface petrophysical properties such as porosity, volume minerals and fluid saturations. These often derived from elastic inferred seismic inversion in a two-step approach: first, reflection data inverted for interest (such density, P-wave S-wave velocities); these then used constraining to model variables. The sequential approach does not ensure proper propagation uncertainty throughout entire geo-modelling workflow it describe...

Summary Geophysical logging is widely used in lithofacies identification, reservoir parameter prediction, and geological modeling. However, it common to have well-log sections with low-quality and/or missing segments. Repeating the measurements not only expensive but might also be impossible depending on condition of borehole walls. In these situations, reliable accurate prediction is, therefore, necessary different stages geomodeling workflow. this study, we propose a time series regression...

Accurate prediction of the spatial distribution subsurface permeability is a fundamental task in reservoir characterization and monitoring studies for hydrocarbon production CO 2 geologic storage. Predicting over large areas challenging, due to their high variability anisotropy. Common approaches modeling generally involve deterministic calculations from porosity using precalibrated rock-physics models (RPMs) or geostatistical cosimulation methods that reproduce observed experimental...

The simultaneous prediction of the subsurface distribution facies and acoustic impedance () from fullstack seismic data requires solving an inverse problem is fundamental in natural resources exploration, carbon capture storage, environmental risk management. In recent years, deep generative models (DGM), such as variational autoencoders (VAE) adversarial networks (GAN), were proposed to reproduce complex patterns honoring prior geological information. Variational Bayesian inference using...

Abstract Modeling inclined fine‐scale mud drapes inside point bars, deposited on accretion surfaces during stages of low energy or slack water, is critical to modeling fluid flow in complex sedimentary environments (e.g., fluvial and turbidity flows). These features have been modeled using deterministic geostatistical tools object‐, event‐, pixel‐based). However, this a non‐trivial task due the need preserve geological realism connectivity within facies hierarchy), while being able condition...

One of the main challenge problems in geophysics is getting reliable seismic inverse models while uncertainty assessed. Seismic may be tackled a probabilistic framework resulting set equiprobable acoustic and elastic impedance models. Here we show new geostatistical AVO method from where density, Vp Vs are retrieved. With Earth also compute correspondent synthetic pre-stack data zero-reflectivity R(0) Gradient (G) We successfully applied this workflow to 3D dataset were known. The final best...