Acquisition Geometry Research Articles

Robust Deep Learning Seismic Velocity Inversion Over Varying Acquisitions

Retrieving subsurface velocity information from recorded seismograms generally involves solving an inverse problem using various optimization methods. Deep learning has recently become an emerging alternative technique to provide solutions for such velocity inversion tasks. However, due to the lack of labeled data in field applications, supervised deep learning methods proposed for velocity model estimations often rely on training with synthetic modeled data. Applying these deep learning models to field data can be severely limited by differences between the configurations used in the synthetic training data and those for field survey. In this paper, we investigate the impacts of different acquisition geometries on deep learning velocity inversion and propose an acquisition-robust deep learning framework to improve model’s applicability by mitigating the impacts of different acquisition geometries. We train the proposed deep learning model with synthetic training data transformed to an alternative domain, e.g., wavenumber-time domain in this study. We demonstrate that deep learning models trained in this way are more robust and allow generalization over different acquisition configurations.

EcoSeis: A novel acquisition method for optimizing seismic resolution while minimizing environmental footprint

All exploration and production projects, whether for oil-and-gas, mining, or clean-technology applications, begin with an accurate image of the subsurface. Many technologies have been developed to enable the acquisition of cost-effective seismic data, with high-density land seismic programs becoming commonplace. However, as the industry progresses and the long-term surface footprint associated with these programs becomes better understood, new methods are needed to reduce the environmental impact of seismic data acquisition while maintaining sufficient subsurface resolution for accurate resource development. New acquisition geometries are typically easier to create than test in the field due to the high cost of field acquisition and processing. However, by using existing data acquired in a grid, one can decimate the original data set into multiple geometries and process them. This provides an opportunity to fully test new geometries without the expense of field acquisition. In this paper, we present processing, interpretation, and inversion tests from an existing ultra-high-density oil-sands seismic data set decimated based on ecologically improved program designs. We then measure and compare the results to understand the impact of these geometries on subsurface resolution.

Least-Squares Kirchhoff Depth Migration With Fast Point-Spread-Function Computation

Seismic migrations are generally formulated as the adjoint operators of linear forward modeling and often lead to images with degraded resolution, unbalanced illumination and migration artifacts, especially in surveys with geologic complexity and irregular acquisition geometry. Least-squares migration (LSM) is able to mitigate these problems and produce better resolved images that are suitable for subsequent AVO/AVA inversion. However, no matter what domain LSM is implemented in, the computational cost is still several times or even one order of magnitude more than that of traditional migration. In this paper, we present an efficient image-domain least-square Kirchhoff depth migration (LSKDM), in which the Hessian matrix is approximated by a grid of point-spread-functions (PSFs). Traditional PSF computing algorithm requires a non-negligible cost caused by a successive operation of modeling and migration, and has to satisfy a sampling restriction to avoid interference between nearby PSFs. We present in this paper that, by using the ray-based Green’s functions and the linear traveltime approximation, the PSFs can be constructed explicitly at a significantly reduced computational cost and is able to adapt flexible spatial sampling that is fine enough to detect small-scale illumination variation. With the constructed PSFs, we formulate an image-domain LSKDM to iteratively solve for the optimal reflectivities. Numerical tests on synthetic and field data examples demonstrate that the proposed LSKDM is highly efficient and is capable of producing images with enhanced spatial resolution and amplitude fidelity when compared with the Kirchhoff depth migration (KDM) image.

Improvement of Spectrum Suppression-Based Deep Learning Interpolation Technique

Seismic data are often coarsely or inconsistently sampled along the acquisition geometry due to the inherent limitations in survey equipment or insufficient survey budgets. Recently, machine learning techniques have been utilized to acquire compactly sampled seismic data. Among them, the self-supervised learning-based techniques that do not require labels are actively being used, and the interpolation technique based on the blind trace network (BTN) and spectrum suppression using suppression masks through line detection has shown high accuracy. However, the interpolation technique using BTN and the suppression masks through line detection suffers from instability caused by the reconstruction loss and inaccuracy of the suppression masks. To mitigate those problems, we propose suppression masks using generalized frequency-wavenumber ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f-k</i> ) trace interpolation (GFKI), patch-based learning, masked UNet, and equalized learning rate. The suppression masks using GFKI are generated by correlating the zero-padded data with the data obtained by regularly removing traces from the zero-padded data in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f-k</i> domain. Additionally, we divide data into patches to enhance the accuracy of the suppression masks. Masked UNet is used to constrain the output to contain the input signals at the designated positions using the binary mask in the space–time domain. Furthermore, we normalize each layer in the network so that the learning speeds for each layer can be commensurate with each other. The synthetic and field data experiments show that the proposed interpolation technique effectively suppresses aliasing of signals and enables the training process to stably converge.

Eucalyptus Leaf area index estimation from Sentinel 2 images: importance of genotype and sun and view acquisition geometry

Eucalyptus Leaf area index estimation from Sentinel 2 images: importance of genotype and sun and view acquisition geometry

A practical guide of the 2D acoustic full waveform inversion on synthetic land seismic data

Full Waveform Inversion (FWI) has been successfully applied in seismic hydrocarbon exploration as it results in the highest resolution inverted velocity and density models. However, the application of FWI on land datasets is a much more challenging process than its application on marine datasets. This paper suggests a step-by-step guide for the 2D acoustic FWI on a synthetic dataset, in particular, in which we used the SEAM Phase II-Foothills dataset that exhibits many of the characteristics of real land data. The methodology includes tools for the processing of seismic data based on the features of acquisition geometry, source estimation, first arrival tomography, plus the software strategies to meet the requirements of the FWI (memory and computation requirements). The document also includes a discussion of the results of velocity models obtained for a low-resolution Dip 2D line of the SEAM data, using a workstation with low-to-intermediate hardware requirements.

Seismic imaging of a seepage gas hydrate system with a harrow-like acquisition geometry

Seismic imaging of a seepage gas hydrate system with a harrow-like acquisition geometry

Underdetermined polarimetric measurements for Mueller extrapolations

Polarized light-matter interactions are mathematically described by the Mueller matrix (MM)-valued polarized bidirectional reflectance distribution function (pBRDF). A pBRDF is parameterized by 16 degrees of freedom that depend upon scattering geometry. A triple degenerate (TD) MM assumption reduces the degrees of freedom to eight: one for reflectance, six for non-depolarizing properties, and one for depolarization. When the non-depolarizing dominant process is known or assumed (e.g. Fresnel reflection), the degrees of freedom are further reduced to two. For a given material, if the TD model is appropriate and the dominant non-depolarizing process is known, then these two degrees of freedom can be estimated from as few as two polarimetric measurements. Thus, the MM can be extrapolated from a reduced number of measurements. The primary contribution of this work is the development and demonstration of a linear estimator for a MM's dominant eigenvalue (i.e. single depolarization parameter) which requires fewer measurements than a full MM reconstruction. MM extrapolations from single snapshot acquisitions with a Sony Triton 5.0MP Polarization Camera are performed at 30 acquisition geometries and two wavelengths on an ensemble of LEGO bricks treated to have varying surface roughness. These extrapolated MMs are compared to MMs reconstructed from a complete dual rotating retarder (DRR) Mueller imaging polarimeter. The flux error mean and mode are 11.06% and 1.03%, respectively, despite a 10x reduction in the number of polarimetric measurements.

Illumination guided sparse geometry optimization for target-oriented full-waveform inversion: An ocean bottom node synthetic study

For full-waveform inversion (FWI) with sparse survey layout, it is crucial to optimize the source and receiver geometry toward full-wave illumination. We propose a sparse geometry optimization method and workflow for target-oriented FWI using finite-frequency illumination analysis. The frequency-dependent impact of a source-receiver pair to the wavefield sensitivity kernel and diagonal terms of approximate Hessian matrix are derived under visco-acoustic model assumptions. Based on the reciprocity principle, we use virtual sources in target zone to efficiently calculate the Green's function and illumination from acquisition surface. Optimal locations of sparse receivers are determined by comparing band-limited illumination at reference stations and their surrounding region through an illumination attribute scan. FWI model residual errors are used to evaluate the performance of optimized geometry layout for a given target zone. The performances of different geometries are compared to determine a suitable frequency band for FWI geometry optimization. Synthetic deep water model and ocean bottom node acquisition geometry are used to verify the feasibility of the workflow. Numerical tests in target-oriented FWI show that optimized geometry with constraints of band-limited illumination below10 Hz has better performance than that above 10 Hz. An extension of our geometry optimization method to sparse source configuration is straightforward.

Multiscale recurrent-guided denoising network for distributed acoustic sensing-vertical seismic profile background noise attenuation

In recent years, distributed optical fiber acoustic sensing (DAS) has emerged as a novel seismic acquisition technique. Compared with conventional hydrophones and geophones of microelectromechanical systems, DAS has an advantage in terms of acquisition geometry, such as low-cost and high-density observations. However, the collected DAS records always suffer from various types of noise, which poses challenges for subsequent processing. Thus, deep learning-based solutions have addressed the attenuation of seismic background noise. Denoising networks can provide excellent denoising results. Nonetheless, the architectures of these networks are relatively simple, which may result in degeneration when confronted with the complex DAS background noise. To effectively attenuate complex noise, a novel multiscale network called recurrent-guided self-enhanced attention network (RGSA-Net) is developed for complex seismic data processing. Specifically, the backbone of RGSA-Net uses a conventional feedforward neural network to preliminarily extract the potential features. Meanwhile, multiscale modules, inspired by the recurrent-guided scheme, are used to enhance the contour information. On this basis, a self-enhanced attention module is applied to fuse the multiscale features and further reinforce the effective information, thereby improving the noise attenuation capability. Synthetic and field experiments demonstrate that RGSA-Net indicates promise in complex noise attenuation and weak upgoing signal recovery.

Historical Structure from Motion (HSfM): Automated processing of historical aerial photographs for long-term topographic change analysis

Precisely measuring the Earth’s changing surface on decadal to centennial time scales is critical for many science and engineering applications, yet long-term records of quantitative landscape change are often temporally and geographically sparse. Archives of scanned historical aerial photographs provide an opportunity to augment these records with accurate elevation measurements that capture the historical state of the Earth surface. Structure from Motion (SfM) photogrammetry workflows produce high-quality digital elevation models (DEMs) and orthoimage mosaics from these historical images, but time-intensive tasks like manual image preprocessing (e.g., fiducial marker identification) and ground control point (GCP) selection impede processing at scale. We developed an automated method to process historical images and generate self-consistent time series of high-resolution (0.5–2 m) DEMs and orthomosaics, without manual GCP selection. The method relies on SfM to correct camera interior and exterior orientation and a robust multi-stage co-registration approach using modern reference terrain datasets for geolocation refinement. We demonstrate the method using scanned images from the North American Glacier Aerial Photography (NAGAP) archive collected between 1967 and 1997. We present results for two sites with variable photo acquisition geometry and overlap — Mount Baker and South Cascade Glacier in Washington State, USA. The automated method corrects initial camera position errors of several kilometers and produces accurately georeferenced, high-resolution DEMs and orthoimages, regardless of camera configuration, acquisition geometry, terrain characteristics, and reference DEM properties. The average RMS reprojection error following bundle adjustment optimization was 0.67 px (0.15 m) for the 261 images contributing to 10 final DEM mosaics between 1970 and 1992 at Mount Baker, and 0.65 px (0.13 m) for the 243 images contributing to 18 individual DEMs between 1967 and 1997 at South Cascade Glacier. The relative accuracy of elevation values in the historical time series stacks was 0.68 m at Mount Baker and 0.37 m at South Cascade Glacier. Our products have reduced systematic error and improved accuracy compared to DEM products generated using SfM with manual GCP selection. Final elevation change measurement precision was ∼0.7–1.0 m over a 30-year period, enabling the study of processes with rates as low as ∼1-3 cm/yr. Our results demonstrate the potential of this scalable method to rapidly process archives of historical imagery and deliver new quantitative insights on long-term geodetic change and Earth surface processes.

High‐resolution 2.5D multifocusing imaging of a crooked seismic profile in a crystalline rock environment: Results from the Larder Lake area, Ontario, Canada

ABSTRACTA high‐resolution seismic reflection transect was acquired over a hard‐rock geological setting along an existing roadway in the Larder Lake area of the Superior Craton of Canada for the Metal Earth project in 2017. This profile, as well as other Metal earth transects, primarily aims to enhance the knowledge and to better understand the subsurface geology of the Abitibi Greenstone Belt within the Canadian Shield. The complex geological settings of the study area as well as the tribulations caused by the survey geometry have made the imaging and velocity field estimation more challenging. A recently introduced 2.5D multifocusing stacking method is one potential solution for processing crooked‐line seismic data with a poor signal‐to‐noise ratio. The 2.5D multifocusing approach offers more realistic modelling of the zero‐offset wavefield by explicitly accounting for the midpoint dispersion and cross‐dip effects. The main practical problem of the 2.5D multifocusing implementation is the simultaneous determination of the optimal wavefield parameters for each image point and time location. We address this optimization problem using a multidimensional constrained differential evolution global optimization algorithm, as this improves the efficiency and accuracy of the estimation. We have also designed an efficient processing sequence for multifocusing seismic imaging. The performance of the 2.5D multifocusing procedure has been examined on a synthetic model, generated using the same real acquisition geometry. Numerical tests demonstrate that the 2.5D multifocusing technique can produce a more focused stack with the primary reflections appearing at their poststack correct locations, and the procedure can also provide reliable estimates of interface dips. Due to the importance and difficulty of imaging the data, several conventional and advanced processing strategies have been attempted on the transect, specifically: 2D phase‐shift time migration of a dip moveout corrected stack; 2D prestack Kirchhoff time migration; swath 3D poststack migration; and our 2.5D multifocusing imaging algorithm. We found that applying the 2.5D multifocusing stacking algorithm followed by a poststack time migration approach improved the resolution of the image significantly compared to all the conventional and advanced methods and identified new reflections. The 2.5D multifocusing method also focused the steeply dipping reflections more coherently, which resolved ambiguities in geological architecture by understanding the location and continuity of structures. The method also accurately extracts 3D structural information and results in an improved signal‐to‐noise ratio.

Reflection seismic imaging across a greenstone belt, Abitibi (Ontario), Canada

AbstractEstablished 2D seismic data processing methods such as Kirchhoff pre‐stack time migration and Kirchhoff pre‐stack depth migration function relatively well for regular acquisition geometries and well‐constrained velocity models. Recently developed focusing pre‐stack depth migration methods have the potential to enhance image quality in the case of sparse and non‐regular source–receiver distribution. We have tested the performance of the coherency migration method as one of these focusing migration approaches in comparison to standard dip‐moveout and Kirchhoff pre‐stack time migration techniques by applying them to the Swayze East seismic profile acquired in the Abitibi greenstone belt of Canada. This seismic profile represents a crooked‐line survey that intersects several metal‐bearing deformation zones, providing good target geometries to examine various pre‐stack migration methods. Analysis of the seismic data indicates reflectivity associated with shallowly dipping reflections appears relatively well preserved over the entire 0–6 km offset range for the frequency range between 20 to 90 Hz. Although most reflections are visible already in either the dip‐moveout or the Kirchhoff pre‐stack time migration results, the coherency migration method delivers the most improved image showing all reflective structures inferred for this area. The comparisons suggest the coherency migration method can be considered as superior in terms of resulting seismic image quality compared with conventional approaches for this type of crooked‐line seismic survey in such a complex geological setting.

A novel weight-drop seismic energy source for subsurface characterization

Multichannel Analysis of Surface Waves (MASW) studies for subsurface characterization in Central Ganga Basin of India, where thick cover of alluvium absorbs most of the vibrations generated by a sledgehammer source, give low quality seismic data at large offsets and generally provide imaging depth lesser than 30 m due to low quality of dispersion curves at lower frequencies. Therefore, a novel seismic energy source is designed and developed to produce large amplitude surface waves even at longer offsets and for deeper depths of imaging using dispersion analysis of surface waves. The developed source for seismic data acquisition is low-cost, repeatable, portable, and easy to operate with no environmental impact. Unlike the sledgehammer source where the quality of surface waves generated also depends on the person hitting the metal plate, the seismic data quality from the novel weight-drop source is independent of the operator.The developed source is an impact type where a free-falling mass accelerated due to gravitational pull hits onto a base plate coupled with the earth generating vibrations in the ground. The performance of the source was evaluated in a field trial. A receiver array of 24-channel cabled geophones with 3 m spacing was planted on the ground. Seismic data were acquired using the source at 5 m negative offset inline from the first geophone. Seismic data were also recorded with the sledgehammer as a source for comparison. Five shot gathers for each source were acquired with the same acquisition geometry and source position.Shot gathers were analyzed and compared using trace attributes, 1D and 2D Fourier spectra, signal-to-noise ratio, relative energy, and dispersion images. The study shows that the developed source produces low frequency high amplitude signal and better dispersion image, notably in the lower frequency range, in comparison with the sledgehammer source. Therefore, the developed source is adapted to investigate deeper subsurface velocity structure with dispersion curve from high amplitude surface wave analysis. Moreover, the repeatability study suggests that the repeat shot gathers acquired from the developed source have remarkably high degree of similarity. Therefore, seismic data acquisition with the developed source does not require repeat shots for vertical stacking as often done in case of a hammer source. Sound pressure level measurement indicates that the developed source has no adverse environmental impact on the operator. From the field trial of the developed source, it is convinced that the source is cost-effective, portable, easy to operate and handle and is environmentally friendly.

Different experimental approaches for Fourier-transform infrared spectroscopy applications in biology and biotechnology: A selected choice of representative results.

Fourier transform infrared (FTIR) spectroscopy is a powerful tool for analyzing the biochemical properties of biological samples such as proteins, cellular materials, and tissues. It provides objective information on samples and has been adopted in many research areas of biomedical and biotechnological interest. FTIR spectroscopy can be performed using different approaches at the macro and micro levels allowing the examination of an incredibly broad class of materials. However, it has become evident that the choice of proper spectra acquisition geometries and the modalities of sample preparation in FTIR spectroscopy analysis require special consideration, especially for certain classes of materials such as cells and tissues. In the present paper, we described the different procedures used for preparing and analyzing different types of biological and biotechnological samples when the more largely available approaches are employed using a commercial FTIR spectrometer. Some basic aspects of data analysis procedures are presented in an Appendix. A certain number of our previous experimental results are reported for demonstrating once more the versatility and the potentiality of FTIR spectroscopy.

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles.

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition's geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with and RMSE CV = 0.88 m2 m-2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloudprone agri-environments.

An investigation into lidar scan angle impacts on stand attribute predictions in different forest environments

As studies have underlined the sensitivity of lidar metrics to scan angles, the objective of this study was twofold. Firstly, we further investigated the influence of lidar scan angle on the ABA predictions of stand attributes of riparian (29 field plots), broadleaf (42 field plots), coniferous (31 field plots) and mixed (45 field plots) forest types in France. Secondly, we evaluated the potential of voxelisation approaches to normalise scan angle effects in lidar metrics and mitigate scan angle effects in ABA models. To achieve these objectives, we first selected a model based on four lidar metrics with different sensitivities to lidar scan angle, i.e. mean and variance of canopy height values, gap-fraction, and coefficient of variation of plant area density (PAD) profile. For each plot, we considered the point cloud scanned from one flight line independently and characterised each resulting point cloud by the mean scan angle (MSA) and classified them into one of three classes: A (0° <=MSA < 10°), B (10°<=MSA < 20°) or C (20°<=MSA < 30°). An experimental setup involving nine scenarios was conceived to study the impact of the number of flight lines (scenarios fl1, fl2 and fl3) and predominant scan angle (scenarios A, B or C) or combination of scan angle directions (scenarios A and B, or A and C, or B and C), on area-based approach (ABA) models. We built ABA models for the same forest plots for 5000 resampled datasets in each scenario to predict three forest attributes, i.e., stem and total volume (Vst and Vtot) and basal area (BA). Three goodness-of-fit criteria were computed for each model (coefficient of determination (R2), relative root mean square error (rRMSE) and mean percentage error (MPE). We compared the distributions of the goodness-of-fit criteria between scenarios to assess the behaviour of the predictive models when: 1) the number of flight lines (i.e., scan angles) increases (fl1, fl2 or fl3); 2) lidar datasets comprise specific scan angle (A, B or C) or combination of scan angles (AB, AC or BC); 3) voxelisation is used to compute Pf and CVPAD. The results show that models built with point clouds scanned from multiple flight lines were more robust, with a lower standard deviation of their goodness-of-fit criteria. On average, across all forest types, compared to fl1, the standard deviations of R2 distributions were lower for fl2 and fl3 by 42 % and 77 %, respectively. We also observed that a dataset with a predominantly nadir configuration (i.e., scenario A) did not always result in better predictions (mean R2 higher by 0.08, 0.07, 0.04 for scenario B for broadleaf, coniferous and mixed, respectively). For a set ofcalibration plots, the resulting forest attribute models depend on the acquisition geometry over the plots, as observed in this study, which could result in unreliable wall-to-wall predictions. The risk is particularly high in acquisitions with low overlapping rates, with many areas covered by only one flight line. Using voxel-based Pf and CVPAD together with the mean and variance of heights helped to mitigate the impacts of changes in scan angles by a) increasing the means of the distributions, thereby improving the accuracy of predictions, or b) reducing the standard deviations, thereby increasing prediction precision, or c) both of the above.

Detecting subsea permafrost layers on marine seismic data: An appraisal from forward modelling

ABSTRACTDetecting the top and base subsea permafrost from 2D seismic reflection data in shallow marine settings is a non‐trivial task due to the occurrence of strong free surface multiples. The potential to accurately detect permafrost layers on conventional 2D seismic reflection data is assessed through viscoelastic modelling. Reflection imaging of permafrost layers is examined through the evaluation of specific characteristics of the subsurface, acquisition parameters and their impact. Results show that limitations are related to the principles of the method, the intrinsic nature of the permafrost layers, and the acquisition geometry. The biggest challenge is the occurrence of free surface multiples that overprint the base permafrost reflection, with the worst‐case scenario the case of a thin layer of ice‐bonded sand. Wedge models suggest that if the base permafrost is dipping, it would intersect internal and free surface multiples of the seafloor and the top permafrost and be detected. Also, the amplitude ratio of the base permafrost reflection and the multiples decreases with the increasing thickness of permafrost. Therefore, the crosscutting relationship between the reflection at base permafrost reflection and the multiples might not be enough to detect the base permafrost for thicker permafrost layers. Finally, the experiment results show that, for partially ice‐bonded layers, the attenuation combined with the low reflectivity of the basal interface limits the likelihood to resolve the base permafrost, especially for thick permafrost layers.

Reverse Time Migration Imaging Using SH Shear Wave Data

In this paper, we discussed the reverse time migration imaging of compressional wave (P-wave) and horizontally polarized shear wave (SH shear wave) seismic data, together with P- and SH shear wave constrained velocity model building. In the Sanhu area in Qaidam Basin, there are large areas of gas clouds, which leads to poor P-wave seismic imaging. The P and SH shear wave seismic data of a co-located survey line with the same acquisition geometry were used to access their imaging capability using reverse time migration. We first estimated the change in P-wave and SH shear wave velocity ratio using pre-stack time migration (PSTM) for constraining the overall depth domain velocity model. Additionally, we then used an eighth-order finite difference scheme for P-wave reverse time migration on a variable grid and used the sixth-order combined compact difference (CCD) wave field simulation method for SH shear wave reverse time migration on a regular grid. The results show that the constrained velocity model produces a good match in the overall geological structure shown in the P-wave and SH shear wave reverse time migration results. However, in the gas cloud areas, SH shear wave reverse time migration has obvious imaging advantages, which can clearly image the structure inside the gas clouds.

Capability of elastic-wave imaging for monitoring conformance and containment in geologic carbon storage

• We have studied the capability of elastic-wave imaging for monitoring conformance and containment in geologic carbon storage, particularly for monitoring secondary CO 2 plumes, using the waveform information of time-lapse multi-component seismic reflection data for the Kimberlina-2 models. We have described the elastic models of different secondary CO 2 plume scenarios and performed elastic-wave modeling and imaging for these models. Our results show that seismic imaging with elastic least-squares reverse-time migration of multi-component data is able to locate the secondary CO 2 plumes at different depths in the Kimberlina-2 model. With noise-free data, the time-lapse seismic images of CO 2 migration scenarios also reveal the growth of the secondary CO 2 plumes with time. We have also demonstrated that elastic-wave imaging using multi-component data can provide the relative magnitude difference in elastic properties caused by the secondary CO 2 plumes. Even with noisy data, seismic imaging using elastic LSRTM is capable of locating early stages of the secondary CO 2 plumes. Seismic noise has more impact on locating the very early stage of the secondary CO 2 plumes than those in the later stages. Our image noise analysis further suggests that the elastic-wave imaging capability decreases for the acquisition geometry with sparser shots/receivers or shorter recording offsets. The image sign-to-noise ratio is approximately proportional to the square root of the total number of seismic traces used for elastic-wave imaging. This relationship can be use to guide seismic monitoring design. Note that it is difficult to use multi-component data to accurately invert for the time-lapse change of an elastic property if its change is much smaller than those of the other elastic properties. We will study how seismic noises can affect the CO 2 detection threshold in the future. Commercial-scale geologic carbon storage requires monitoring for conformance and containment of the injected CO 2 , including secondary CO 2 plumes. Detecting and locating secondary CO 2 plumes are even more challenging than monitoring the primary CO 2 plumes. We study the capability of elastic-wave imaging for monitoring secondary CO 2 plumes with time-lapse multi-component data from 3D elastic models built using the geologic settings of the Kimberlina site located in the San Joaquin Basin, California. In the numerical simulation of CO 2 migration in these time-lapse Kimberlina-2 models, CO 2 migrates from the storage reservoir along a steeply dipping fault and forms secondary CO 2 plumes in formations at different depths. In our study, we extract a 2D slice through the location of the secondary CO 2 plumes to perform elastic-wave modeling and imaging based on numerical simulation to the elastic-wave equation. We use a finite-difference algorithm to generate time-lapse multi-component synthetic elastic-wave reflection data and employ elastic least-squares reverse-time migration to produce images of P- and S-wave velocities and density for various sizes of the secondary CO 2 plumes after CO 2 migration through the fault. Our imaging results show that, for noise-free data, elastic-wave imaging accurately locates the secondary CO 2 plumes at different depths and reveals the growth of the size of the secondary CO 2 plumes with years of CO 2 migration. The relative amplitude differences of seismic images reveal how secondary CO 2 plumes change elastic parameters differently. We further study the capability of elastic-wave imaging for monitoring the secondary CO 2 plumes using noisy synthetic data containing seismic noise extracted from a field surface seismic dataset. We evaluate elastic-wave imaging capability for monitoring secondary CO 2 plumes using different noise levels and different acquisition geometries. Our results demonstrate that elastic-wave imaging is capable of locating secondary CO 2 plumes even with strong noisy data, and a relatively higher signal-to-noise ratio (SNR) is needed to locate (or image) the secondary CO 2 plumes at earlier stages. The imaging capability decreases for sparser or shorter-offset acquisition geometry. Our results indicate that the image SNR is approximately proportional to the square root of the total trace number used for elastic-wave imaging. This relationship can be used to guide whether a denser or longer-offset acquisition geometry is worth the increase of seismic image quality for improving the capability to locate secondary CO 2 plumes. Note that multi-component data can inform us whether the time-lapse change of one elastic property is much smaller than others, but it is difficult to accurately invert for the time-lapse change of an elastic property if its change is relatively much smaller than the changes of the other elastic properties.