scholarly journals Adaptive waveform inversion: Practice

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. R447-R461 ◽  
Author(s):  
Lluís Guasch ◽  
Michael Warner ◽  
Céline Ravaut
Keyword(s):  
Field Data ◽  
Waveform Inversion ◽  
Data Sets ◽  
Data Set ◽  
Depth Migration ◽  
The North ◽  
The Difference ◽  
Starting Model ◽  
Kirchhoff Depth Migration ◽  
Reflection Tomography

Adaptive waveform inversion (AWI) reformulates the misfit function used to perform full-waveform inversion (FWI), so that it no longer contains local minima related to cycle skipping. It does this by finding a model that drives the ratio of the predicted and observed data sets to unity rather than driving the difference between these two data sets to zero as is the case for conventional FWI. We apply AWI to a 3D field data set acquired over a pervasive gas cloud in the North Sea, comparing its performance with that of conventional FWI in a variety of circumstances. When starting inversion from 3 Hz, and using a good starting model obtained from reflection tomography, FWI and AWI generate similar models although the FWI result contains edge artifacts that are not produced by AWI. However, when the starting frequency is increased to approximately 6 Hz, or when the starting model is less accurate, FWI fails to recover a good model whereas AWI continues to converge. When both of these conditions apply, FWI fails comprehensively, leading to a model that is significantly worse than the starting model, whereas the AWI result remains largely unaffected. We applied Kirchhoff depth migration to the fully-processed data using the FWI result obtained following reflection tomography, and using the AWI result obtained from a simple one-dimensional starting model. We use the resulting migrated volumes, together with measures of residual moveout throughout the volume, to show that the AWI result from a simple starting model is at least as good as the FWI result obtained following tomography. We conclude that AWI is robust in the presence of cycle skipping on this 3D field data set, and can proceed successfully from a less-accurate starting model, and from a higher starting frequency, in circumstances in which FWI fails completely.

scholarly journals Mitigating elastic effects in marine 3-D full-waveform inversion

2019 ◽  
Vol 220 (3) ◽  
pp. 2089-2104
Author(s):  
Òscar Calderón Agudo ◽  
Nuno Vieira da Silva ◽  
George Stronge ◽  
Michael Warner
Keyword(s):  
Wave Equation ◽  
Field Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Data Sets ◽  
Acoustic Wave Equation ◽  
Data Set ◽  
Velocity Models ◽  
Full Waveform

SUMMARY The potential of full-waveform inversion (FWI) to recover high-resolution velocity models of the subsurface has been demonstrated in the last decades with its application to field data. But in certain geological scenarios, conventional FWI using the acoustic wave equation fails in recovering accurate models due to the presence of strong elastic effects, as the acoustic wave equation only accounts for compressional waves. This becomes more critical when dealing with land data sets, in which elastic effects are generated at the source and recorded directly by the receivers. In marine settings, in which sources and receivers are typically within the water layer, elastic effects are weaker but can be observed most easily as double mode conversions and through their effect on P-wave amplitudes. Ignoring these elastic effects can have a detrimental impact on the accuracy of the recovered velocity models, even in marine data sets. Ideally, the elastic wave equation should be used to model wave propagation, and FWI should aim to recover anisotropic models of velocity for P waves (vp) and S waves (vs). However, routine three-dimensional elastic FWI is still commercially impractical due to the elevated computational cost of modelling elastic wave propagation in regions with low S-wave velocity near the seabed. Moreover, elastic FWI using local optimization methods suffers from cross-talk between different inverted parameters. This generally leads to incorrect estimation of subsurface models, requiring an estimate of vp/vs that is rarely known beforehand. Here we illustrate how neglecting elasticity during FWI for a marine field data set that contains especially strong elastic heterogeneities can lead to an incorrect estimation of the P-wave velocity model. We then demonstrate a practical approach to mitigate elastic effects in 3-D yielding improved estimates, consisting of using a global inversion algorithm to estimate a model of vp/vs, employing matching filters to remove elastic effects from the field data, and performing acoustic FWI of the resulting data set. The quality of the recovered models is assessed by exploring the continuity of the events in the migrated sections and the fit of the latter with the recovered velocity model.

scholarly journals Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R1-R10 ◽  
Author(s):  
Zhendong Zhang ◽  
Tariq Alkhalifah ◽  
Zedong Wu ◽  
Yike Liu ◽  
Bin He ◽  
...  
Keyword(s):  
Field Data ◽  
Extreme Values ◽  
Time Lag ◽  
Waveform Inversion ◽  
Optimal Time ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Velocity Models ◽  
Full Waveform ◽  
The Earth

Full-waveform inversion (FWI) is an attractive technique due to its ability to build high-resolution velocity models. Conventional amplitude-matching FWI approaches remain challenging because the simplified computational physics used does not fully represent all wave phenomena in the earth. Because the earth is attenuating, a sample-by-sample fitting of the amplitude may not be feasible in practice. We have developed a normalized nonzero-lag crosscorrelataion-based elastic FWI algorithm to maximize the similarity of the calculated and observed data. We use the first-order elastic-wave equation to simulate the propagation of seismic waves in the earth. Our proposed objective function emphasizes the matching of the phases of the events in the calculated and observed data, and thus, it is more immune to inaccuracies in the initial model and the difference between the true and modeled physics. The normalization term can compensate the energy loss in the far offsets because of geometric spreading and avoid a bias in estimation toward extreme values in the observed data. We develop a polynomial-type weighting function and evaluate an approach to determine the optimal time lag. We use a synthetic elastic Marmousi model and the BigSky field data set to verify the effectiveness of the proposed method. To suppress the short-wavelength artifacts in the estimated S-wave velocity and noise in the field data, we apply a Laplacian regularization and a total variation constraint on the synthetic and field data examples, respectively.

scholarly journals Sources of light-absorbing aerosol in arctic snow and their seasonal variation

2010 ◽  
Vol 10 (6) ◽  
pp. 13755-13796 ◽  
Author(s):  
D. A. Hegg ◽  
S. G. Warren ◽  
T. C. Grenfell ◽  
S. J. Doherty ◽  
A. D. Clarke
Keyword(s):  
Seasonal Variation ◽  
Biomass Burning ◽  
Chemical Constituents ◽  
Dominant Role ◽  
Data Sets ◽  
Source Attribution ◽  
Data Set ◽  
Pmf Model ◽  
The North ◽  
Absorbing Aerosols

Abstract. Two data sets consisting of measurements of light absorbing aerosols (LAA) in arctic snow together with suites of other corresponding chemical constituents are presented; the first from Siberia, Greenland and near the North Pole obtained in 2008, and the second from the Canadian arctic obtained in 2009. A preliminary differentiation of the LAA into black carbon (BC) and non-BC LAA is done. Source attribution of the light absorbing aerosols was done using a positive matrix factorization (PMF) model. Four sources were found for each data set (crop and grass burning, boreal biomass burning, pollution and marine). For both data sets, the crops and grass biomass burning was the main source of both LAA species, suggesting the non-BC LAA was brown carbon. Depth profiles at most of the sites allowed assessment of the seasonal variation in the source strengths. The biomass burning sources dominated in the spring but pollution played a more significant (though rarely dominant) role in the fall, winter and, for Greenland, summer. The PMF analysis is consistent with trajectory analysis and satellite fire maps.

scholarly journals ‘B-M MODEL’ FOR FARMERS’ KNOWLEDGE MANAGEMENT IN INCREASING RICE PRODUCTION

2021 ◽  
Vol 2 (1) ◽  
pp. 30-45
Author(s):  
BK Mahalder ◽  
◽  
MB Ahmed ◽  
H Bhandari ◽  
MU Salam ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Field Data ◽  
Production Systems ◽  
Rice Yield ◽  
Rice Production ◽  
Practice Change ◽  
Clear Understanding ◽  
Data Set ◽  
Production Practices ◽  
The Difference

Quantifying knowledge on agriculture can have many benefits to stakeholders. While many knowledge-based systems exist in modern days for farmers’ decision support, specific models are lacking on how knowledge traits can impact on agricultural production systems. This study employed modelling technique, supported by field data, to provide a clear understanding and quantifying how knowledge management in production practices can contribute to rice productivity in the environmentally stressed southwest Bangladesh. This research accounted for ‘Boro’ rice as the target crop and ‘BRRI dhan28’ as the test variety. The ‘B-M Model’ was developed following the principle and procedure from published literature, ‘brainstorming’ and data from field surveys. Three knowledge management traits (KMT) were defined and quantified as the inputs of the model. Those are: self-experience and observation (SEO), extension advisory services (EAS) and accessed information sources (AIS). The yield influencing process (YIP), the intermediate state variable of the model, was deduced by accounting for the two dominant agronomic practices, seedling age for transplanting and triple superphosphate (TSP) application. ‘Knowledge drives farmers’ practice change which in turn influences yield’ was composed as the theoretical framework of the ‘B-M Model’. The model performed strongly against an independently collected field data set. Across the 180 farmers’ data, the average relative rice yield (RRY) predicted by the model (0.705) and observed in the field (0.716) was close (root mean squared deviation (RMSD) = 0.018). The difference between predicted and observed RRY was not statistically different (LSD = 0.03), indicating the model fully captured the field data. A regression of predicted and observed RRY explained 96% variance in observation, further proving the model’s strength in estimating RRY in a wider range of farmers’ rice yield. In a normative analysis, the practicality and usefulness of the model to stakeholders were simulated for the understanding of how much achievable yield could be expected by changing farmers’ knowledge pool (the sum of three KMT) on rice production practices, and at what combination(s) of KMT to be considered at strategic hierarchy to materialize a targeted achievable yield. To the best of the knowledge, a model quantifying rice yield in relation to knowledge management trait does not exist in literature. Upon successful testing under diverse yield scenarios using multiple and sophisticated statistical tools that enhanced the credibility of the model, it is concluded that the model has the potential to be used for identifying quantitative pathways of farmers’ knowledge acquisition for practice change leading to improved productivity of rice in the southwest region of Bangladesh.

Data Sets

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Model Building ◽  
Careful Analysis ◽  
Logical Structure ◽  
Fortran Program ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Wind Speeds ◽  
The North ◽  
The One

Hurricane data originate from careful analysis of past storms by operational meteorologists. The data include estimates of the hurricane position and intensity at 6-hourly intervals. Information related to landfall time, local wind speeds, damages, and deaths, as well as cyclone size, are included. The data are archived by season. Some effort is needed to make the data useful for hurricane climate studies. In this chapter, we describe the data sets used throughout this book. We show you a work flow that includes importing, interpolating, smoothing, and adding attributes. We also show you how to create subsets of the data. Code in this chapter is more complicated and it can take longer to run. You can skip this material on first reading and continue with model building in Chapter 7. You can return here when you have an updated version of the data that includes the most recent years. Most statistical models in this book use the best-track data. Here we describe these data and provide original source material. We also explain how to smooth and interpolate them. Interpolations are needed for regional hurricane analyses. The best-track data set contains the 6-hourly center locations and intensities of all known tropical cyclones across the North Atlantic basin, including the Gulf of Mexico and Caribbean Sea. The data set is called HURDAT for HURricane DATa. It is maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA) at the National Hurricane Center (NHC). Center locations are given in geographic coordinates (in tenths of degrees) and the intensities, representing the one-minute near-surface (∼10 m) wind speeds, are given in knots (1 kt = .5144 m s−1) and the minimum central pressures are given in millibars (1 mb = 1 hPa). The data are provided in 6-hourly intervals starting at 00 UTC (Universal Time Coordinate). The version of HURDAT file used here contains cyclones over the period 1851 through 2010 inclusive. Information on the history and origin of these data is found in Jarvinen et al (1984). The file has a logical structure that makes it easy to read with a FORTRAN program. Each cyclone contains a header record, a series of data records, and a trailer record.

Marlim R3D: A realistic model for controlled-source electromagnetic simulations — Phase 2: The controlled-source electromagnetic data set

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. E293-E299
Author(s):  
Jorlivan L. Correa ◽  
Paulo T. L. Menezes
Keyword(s):  
A Priori ◽  
Synthetic Data ◽  
Realistic Model ◽  
Earth Model ◽  
Data Sets ◽  
Data Set ◽  
Geoelectric Model ◽  
Controlled Source ◽  
The North ◽  
Electromagnetic Simulations

Synthetic data provided by geoelectric earth models are a powerful tool to evaluate a priori a controlled-source electromagnetic (CSEM) workflow effectiveness. Marlim R3D (MR3D) is an open-source complex and realistic geoelectric model for CSEM simulations of the postsalt turbiditic reservoirs at the Brazilian offshore margin. We have developed a 3D CSEM finite-difference time-domain forward study to generate the full-azimuth CSEM data set for the MR3D earth model. To that end, we fabricated a full-azimuth survey with 45 towlines striking the north–south and east–west directions over a total of 500 receivers evenly spaced at 1 km intervals along the rugged seafloor of the MR3D model. To correctly represent the thin, disconnected, and complex geometries of the studied reservoirs, we have built a finely discretized mesh of [Formula: see text] cells leading to a large mesh with a total of approximately 90 million cells. We computed the six electromagnetic field components (Ex, Ey, Ez, Hx, Hy, and Hz) at six frequencies in the range of 0.125–1.25 Hz. In our efforts to mimic noise in real CSEM data, we summed to the data a multiplicative noise with a 1% standard deviation. Both CSEM data sets (noise free and noise added), with inline and broadside geometries, are distributed for research or commercial use, under the Creative Common License, at the Zenodo platform.

scholarly journals Anisotropic Kirchhoff pre-stack depth migration at the COSC-1 borehole, central Sweden

2019 ◽  
Vol 219 (1) ◽  
pp. 66-79 ◽  
Author(s):  
H Simon ◽  
S Buske ◽  
P Hedin ◽  
C Juhlin ◽  
F Krauß ◽  
...  
Keyword(s):  
Regional Scale ◽  
Seismic Profile ◽  
Black Shales ◽  
Data Sets ◽  
Data Set ◽  
Depth Migration ◽  
Scandinavian Caledonides ◽  
Long Offset ◽  
Nappe Complex ◽  
Seve Nappe Complex

SUMMARYA remarkably well preserved representation of a deeply eroded Palaeozoic orogen is found in the Scandinavian Caledonides, formed by the collision of the two palaeocontinents Baltica and Laurentia. Today, after 400 Ma of erosion along with uplift and extension during the opening of the North Atlantic Ocean, the geological structures in central western Sweden comprise far transported allochthonous units, the underlying Precambrian crystalline basement, and a shallow west-dipping décollement that separates the two and is associated with a thin layer of Cambrian black shales. These structures, in particular the Seve Nappes (upper part of the Middle Allochthons), the Lower Allochthons and the highly reflective basement are the target of the two approximately 2.5 km deep fully cored scientific boreholes in central Sweden that are part of the project COSC (Collisional Orogeny in the Scandinavian Caledonides). Thus, a continuous 5 km tectonostratigraphic profile through the Caledonian nappes into Baltica’s basement will be recovered. The first borehole, COSC-1, was successfully drilled in 2014 and revealed a thick section of the seismically highly reflective Lower Seve Nappe. The Seve Nappe Complex, mainly consisting of felsic gneisses and mafic amphibolites, appears to be highly anisotropic. To allow for extrapolation of findings from core analysis and downhole logging to the structures around the borehole, several surface and borehole seismic experiments were conducted. Here, we use three long offset surface seismic profiles that are centred on the borehole COSC-1 to image the structures in the vicinity of the borehole and below it. We applied Kirchhoff pre-stack depth migration, taking into account the seismic anisotropy in the Seve Nappe Complex. We calculated Green’s functions using an anisotropic eikonal solver for a VTI (transversely isotropic with vertical axis of symmetry) velocity model, which was previously derived by the analysis of VSP (Vertical Seismic Profile) and surface seismic data. We show, that the anisotropic results are superior to the corresponding isotropic depth migration. The reflections appear significantly more continuous and better focused. The depth imaging of the long offset profiles provides a link between a high-resolution 3-D data set and the regional scale 2-D COSC Seismic Profile and complements these data sets, especially in the deeper parts below the borehole. However, many of the reflective structures can be observed in the different data sets. Most of the dominant reflections imaged originate below the bottom of the borehole and are situated within the Precambrian basement or at the transition zones between Middle and Lower Allochthons and the basement. The origin of the deeper reflections remains enigmatic, possibly representing dolerite intrusions or deformation zones of Caledonian or pre-Caledonian age.

A Ccomparison of sedigraph and pipette methods for soil particle-size analysis

Soil Research ◽  
1993 ◽  
Vol 31 (4) ◽  
pp. 407 ◽  
Author(s):  
GD Buchan ◽  
KS Grewal ◽  
JJ Claydon ◽  
RJ Mcpherson
Keyword(s):  
Particle Size ◽  
New Zealand ◽  
Particle Size Analysis ◽  
Size Analysis ◽  
Data Sets ◽  
Regression Equations ◽  
Total Iron Content ◽  
Data Set ◽  
X Ray ◽  
The Difference

The X-ray attenuation (Sedigraph) method for particle-size analysis is known to consistently estimate a finer size distribution than the pipette method. The objectives of this study were to compare the two methods, and to explore the reasons for their divergence. The methods are compared using two data sets from measurements made independently in two New Zealand laboratories, on two different sets of New Zealand soils, covering a range of textures and parent materials. The Sedigraph method gave systematically greater mass percentages at the four measurement diameters (20, 10, 5 and 2 �m). For one data set, the difference between clay (<2 �m) percentages from the two methods is shown to be positively correlated (R2 = 0.625) with total iron content of the sample, for all but one of the soils. This supports a novel hypothesis that the typically greater concentration of Fe (a strong X-ray absorber) in smaller size fractions is the major factor causing the difference. Regression equations are presented for converting the Sedigraph data to their pipette equivalents.

scholarly journals Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

2018 ◽  
Vol 18 (3) ◽  
pp. 1573-1592 ◽  
Author(s):  
Gerrit de Leeuw ◽  
Larisa Sogacheva ◽  
Edith Rodriguez ◽  
Konstantinos Kourtidis ◽  
Aristeidis K. Georgoulias ◽  
...  
Keyword(s):  
Time Series ◽  
European Space Agency ◽  
Mainland China ◽  
Satellite Observations ◽  
Data Sets ◽  
Data Set ◽  
Modis Aod ◽  
The Difference ◽  
Along Track ◽  
Aerosol Properties

Abstract. The retrieval of aerosol properties from satellite observations provides their spatial distribution over a wide area in cloud-free conditions. As such, they complement ground-based measurements by providing information over sparsely instrumented areas, albeit that significant differences may exist in both the type of information obtained and the temporal information from satellite and ground-based observations. In this paper, information from different types of satellite-based instruments is used to provide a 3-D climatology of aerosol properties over mainland China, i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a lidar flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the column-integrated extinction (aerosol optical depth – AOD) available from three radiometers: the European Space Agency (ESA)'s Along-Track Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995–2015. AOD data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS Collection 6 (C6) the AOD data set is used that was obtained from merging the AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further referred to as the DTDB merged AOD product. These data sets are validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data as reference. The results show that, over China, ATSR slightly underestimates the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is overall lower than that from MODIS, and the difference increases with increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD data sets is better than the other one everywhere. However, ATSR ADV has limitations over bright surfaces which the MODIS DB was designed for. To allow for comparison of MODIS C6 results with previous analyses where MODIS Collection 5.1 (C5.1) data were used, also the difference between the C6 and C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed. The AOD data sets show strong seasonal differences and the seasonal features vary with latitude and longitude across China. Two-decadal AOD time series, averaged over all of mainland China, are presented and briefly discussed. Using the 17 years of ATSR data as the basis and MODIS/Terra to follow the temporal evolution in recent years when the environmental satellite Envisat was lost requires a comparison of the data sets for the overlapping period to show their complementarity. ATSR precedes the MODIS time series between 1995 and 2000 and shows a distinct increase in the AOD over this period. The two data series show similar variations during the overlapping period between 2000 and 2011, with minima and maxima in the same years. MODIS extends this time series beyond the end of the Envisat period in 2012, showing decreasing AOD.

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