Handling gaps in acquisition geometries - improving Marchenko-based imaging using sparsity-promoting inversion and joint inversion of time-lapse data

Geophysics ◽  
2020 ◽  
pp. 1-49
Author(s):  
Claudia Haindl ◽  
Matteo Ravasi ◽  
Filippo Broggini
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Time Lapse ◽  
Velocity Model ◽  
Inversion Problem ◽  
Scattered Energy ◽  
Regular Sampling ◽  
Sparsity Constraint ◽  
Inversion Techniques ◽  
Source Geometry

Marchenko focusing and imaging are novel methods for correctly handling multiple scattered energy while processing seismic data. However, strict requirements in the acquisition geometry, specifically co-location of sources and receivers as well as dense and regular sampling, currently constrain their practical applicability. We reformulate the Marchenko equations to handle the case where there are gaps in the source geometry while receiver sampling remains regular (or the opposite, by means of reciprocity). Using synthetic data based on a velocity model that produces strong interbed multiples, we test different solvers for the newly formulated inversion problem and we compare these results to results obtained by applying standard Marchenko inversion to a previously reconstructed dataset. When using the unreconstructed dataset, the ability of the Marchenko equations to retrace multiple reflected energy deteriorates. Sparsity-promoting Marchenko inversion, while improving the appearance of focusing functions, barely decreases multiple leakage in gathers and does not visibly improve the final image when compared to standard least-squares inversion. On the other hand, reconstructing the wavefield in advance restores the proper functioning of the Marchenko methods. Further, we test a joint inversion technique designed for time-lapse data with non-repeated geometries and originally intended to be solved using sparsity-promoting inversion. Motivated by our previous results, we compare images produced by this method to images produced by solving the same joint inversion problem without sparsity constraint. We find that the joint inversion alone hardly improves the resulting images but, when combined with the sparsity constraint, it leads to better noise and multiple suppression and produces a clean time-lapse image. Overall, none of the results from sparsity-promoting inversion techniques match the results obtained when reconstructing the wavefield in advance. We show that this can be explained by the comparatively slow convergence rate of sparsity-promoting Marchenko inversion.

Quasi-2D inversion of DCR and TDEM data for shallow investigations

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F239-F250 ◽  
Author(s):  
Fernando A. Monteiro Santos ◽  
Hesham M. El-Kaliouby
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Complex Environments ◽  
Inversion Algorithm ◽  
2D Inversion ◽  
New Approach ◽  
Earth Models ◽  
Time Domain Electromagnetic ◽  
Inversion Techniques

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

scholarly journals Comparing different approaches of time-lapse seismic inversion

2020 ◽  
Vol 17 (6) ◽  
pp. 929-939
Author(s):  
Daiane R Rosa ◽  
Juliana M C Santos ◽  
Rafael M Souza ◽  
Dario Grana ◽  
Denis J Schiozer ◽  
...  
Keyword(s):  
Joint Inversion ◽  
Time Lapse ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Hydrocarbon Production ◽  
True Solution ◽  
Seismic Amplitude ◽  
Deterministic Methods ◽  
4D Seismic ◽  
Inversion Techniques

Abstract Time-lapse (4D) seismic inversion aims to predict changes in elastic rock properties, such as acoustic impedance, from measured seismic amplitude variations due to hydrocarbon production. Possible approaches for 4D seismic inversion include two classes of method: sequential independent 3D inversions and joint inversion of 4D seismic differences. We compare the standard deterministic methods, such as coloured and model-based inversions, and the probabilistic inversion techniques based on a Bayesian approach. The goal is to compare the sequential independent 3D seismic inversions and the joint 4D inversion using the same type of algorithm (Bayesian method) and to benchmark the results to commonly applied algorithms in time-lapse studies. The model property of interest is the ratio of the acoustic impedances, estimated for the monitor, and base surveys at each location in the model. We apply the methods to a synthetic dataset generated based on the Namorado field (offshore southeast Brazil). Using this controlled dataset, we can evaluate properly the results as the true solution is known. The results show that the Bayesian 4D joint inversion, based on the amplitude difference between seismic surveys, provides more accurate results than sequential independent 3D inversion approaches, and these results are consistent with deterministic methods. The Bayesian 4D joint inversion is relatively easy to apply and provides a confidence interval of the predictions.

scholarly journals Visual4DTracker: a tool to interact with 3D + t image stacks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ermanno Cordelli ◽  
Paolo Soda ◽  
Giulio Iannello
Keyword(s):  
Synthetic Data ◽  
Time Lapse ◽  
Temporal Data ◽  
Image Stack ◽  
Biological Phenomena ◽  
Insulin Granules ◽  
Matlab Package ◽  
User Friendly ◽  
Drosophila Cells ◽  
Over Time

Abstract Background Biological phenomena usually evolves over time and recent advances in high-throughput microscopy have made possible to collect multiple 3D images over time, generating $$3D+t$$ 3 D + t (or 4D) datasets. To extract useful information there is the need to extract spatial and temporal data on the particles that are in the images, but particle tracking and feature extraction need some kind of assistance. Results This manuscript introduces our new freely downloadable toolbox, the Visual4DTracker. It is a MATLAB package implementing several useful functionalities to navigate, analyse and proof-read the track of each particle detected in any $$3D+t$$ 3 D + t stack. Furthermore, it allows users to proof-read and to evaluate the traces with respect to a given gold standard. The Visual4DTracker toolbox permits the users to visualize and save all the generated results through a user-friendly graphical user interface. This tool has been successfully used in three applicative examples. The first processes synthetic data to show all the software functionalities. The second shows how to process a 4D image stack showing the time-lapse growth of Drosophila cells in an embryo. The third example presents the quantitative analysis of insulin granules in living beta-cells, showing that such particles have two main dynamics that coexist inside the cells. Conclusions Visual4DTracker is a software package for MATLAB to visualize, handle and manually track $$3D+t$$ 3 D + t stacks of microscopy images containing objects such cells, granules, etc.. With its unique set of functions, it remarkably permits the user to analyze and proof-read 4D data in a friendly 3D fashion. The tool is freely available at https://drive.google.com/drive/folders/19AEn0TqP-2B8Z10kOavEAopTUxsKUV73?usp=sharing

scholarly journals Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging

2021 ◽  
Author(s):  
Lorenzo Chicchi ◽  
Gloria Cecchini ◽  
Ihusan Adam ◽  
Giuseppe de Vito ◽  
Roberto Livi ◽  
...  
Keyword(s):  
Structural Information ◽  
Activity Patterns ◽  
Synthetic Data ◽  
Light Sheet ◽  
Time Lapse ◽  
Inhibitory Neurons ◽  
Mean Field Approximation ◽  
Directed Network ◽  
Dynamical Regime ◽  
Power Law Distribution

AbstractAn inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. C81-C92 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Hilde Grude Borgos ◽  
Martin Landrø
Keyword(s):  
Stochastic Model ◽  
Seismic Data ◽  
Synthetic Data ◽  
Time Lapse ◽  
Bayesian Framework ◽  
Reservoir Modeling ◽  
Data Set ◽  
The North ◽  
Fluid Saturation ◽  
Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Target-oriented inversion using the Patched Green′s function method

Geophysics ◽  
2021 ◽  
pp. 1-77
Author(s):  
Danyelle da Silva ◽  
Edwin Fagua Duarte ◽  
Wagner Almeida ◽  
Mauro Ferreira ◽  
Francisco Alirio Moura ◽  
...  
Keyword(s):  
Condensed Matter ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Velocity Model ◽  
Dyson Equation ◽  
Computational Time ◽  
Green Functions ◽  
Target Area ◽  
Computational Domain ◽  
Full Waveform

We have designed a target-oriented methodology to perform Full Waveform Inversion using a frequency-domain wave propagator based on the so-called Patched Green’s Function (PGF) technique. Originally developed in condensed matter physics to describe electronic waves in materials, the PGF technique is easily adaptable to the case of wave propagation in a spatially variable media in general. By dividing the entire computational domain into two sections, namely the target area and the outside target area, we calculate the Green Functions related to each section separately. The calculations related to the section outside the target are performed only once at the beginning of inversion, whereas the calculations in the target area are performed repeatedly for each iteration of the inversion process. With the Green Functions of the separate areas, we calculate the Green Functions of the two systems patched together through the application of a Recursive Dyson equation. By performing 2D and time-lapse experiments on the Marmousi model and a Brazilian Pre-salt velocity model, we demonstrate that the target-oriented PGF reduces the computational time of the inversion without compromising accuracy. In fact, when compared with conventional FWI results, the PGF-based calculations are identical but done in a fraction of the time.

scholarly journals Rapid estimation of earthquake locations using waveform traveltimes

2019 ◽  
Vol 217 (3) ◽  
pp. 1727-1741 ◽  
Author(s):  
D W Vasco ◽  
Seiji Nakagawa ◽  
Petr Petrov ◽  
Greg Newman
Keyword(s):  
Standard Error ◽  
Random Noise ◽  
Grid Point ◽  
Synthetic Data ◽  
Velocity Model ◽  
Source Location ◽  
Model Errors ◽  
Average Deviation ◽  
Origin Time ◽  
Data Set

SUMMARY We introduce a new approach for locating earthquakes using arrival times derived from waveforms. The most costly computational step of the algorithm scales as the number of stations in the active seismographic network. In this approach, a variation on existing grid search methods, a series of full waveform simulations are conducted for all receiver locations, with sources positioned successively at each station. The traveltime field over the region of interest is calculated by applying a phase picking algorithm to the numerical wavefields produced from each simulation. An event is located by subtracting the stored traveltime field from the arrival time at each station. This provides a shifted and time-reversed traveltime field for each station. The shifted and time-reversed fields all approach the origin time of the event at the source location. The mean or median value at the source location thus approximates the event origin time. Measures of dispersion about this mean or median time at each grid point, such as the sample standard error and the average deviation, are minimized at the correct source position. Uncertainty in the event position is provided by the contours of standard error defined over the grid. An application of this technique to a synthetic data set indicates that the approach provides stable locations even when the traveltimes are contaminated by additive random noise containing a significant number of outliers and velocity model errors. It is found that the waveform-based method out-performs one based upon the eikonal equation for a velocity model with rapid spatial variations in properties due to layering. A comparison with conventional location algorithms in both a laboratory and field setting demonstrates that the technique performs at least as well as existing techniques.

scholarly journals Joint Inversion of Time-Lapse Seismic Data

2019 ◽  
Author(s):  
Cesar Barajas-Olalde ◽  
Donald Adams ◽  
Lu Jin ◽  
Jun He ◽  
Nicholas Kalenze ◽  
...  
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse
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