Subsurface Model Research Articles

Integrated Subsurface Hydrologic Modeling for Agricultural Management Using HYDRUS and UZF Package Coupled with MODFLOW

The present work aims to compare two different subsurface hydrological models, namely HYDRUS and MODFLOW UZF package, in terms of groundwater recharge; thus, both models were coupled with MODFLOW. The study area is an experimental kiwifruit orchard located in the Arta plain in the Epirus region of Greece. A novel conceptual framework is introduced in order to (i) use in situ and laboratory measurements to estimate parameter values for both sub-surface flow models; (ii) couple the developed models with MODFLOW to estimate groundwater recharge; and (iii) compare and evaluate the performance of both approaches, with differences stemming from the distinctive equations describing the flow in the unsaturated zone. Detailed soil investigation was conducted in two soil horizons in the research field to identify soil texture zones, along with infiltration experiments implementing both double-ring and single-ring infiltrometers. The results of the field measurements indicate that fine-textured soils are predominant within the field, affecting several hydrological processes, such as infiltration, drainage, and root water uptake. Field measurements were incorporated in unsaturated zone flow modeling and the infiltration fluxes were simulated with the application of both the UZF package of MODFLOW and the HYDRUS code. The two codes presented acceptable agreement between the simulated and observed hydraulic head values with a similar performance in terms of statistics; however, they produced different results regarding recharge rates in the aquifer as simulated by MODFLOW. HYDRUS produced higher hydraulic head values in the aquifer throughout the simulation, related to higher recharge rates arising from the root water uptake and the capillary effects that are computed by HYDRUS but neglected by the UZF package of MODFLOW.

Development of a 3D Geologic Model Used in the Seismic Hazard and Liquefaction Hazard Analysis of Madison County, Tennessee

ABSTRACT Madison County of western Tennessee is approximately 100 km southeast of the New Madrid seismic zone and, thus, is subject to potentially dangerous seismic shaking and liquefaction events. Holocene floodplain alluvium and associated Pliocene/Pleistocene terraces of the Forked Deer and Hatchie Rivers cover a large part of Madison County. Underlying these unconsolidated sediments are approximately ∼650 m of poorly consolidated Paleogene and Cretaceous terrestrial and marine sediments, which are underlain by Paleozoic limestone bedrock. Uniform regional geology used in the 2014 United States Geological Survey seismic hazard maps assumes a homogenous seismic velocity structure above bedrock. Three-dimensional geologic mapping of the ∼700 m of Quaternary, Neogene, Paleogene, and Cretaceous sediments conducted in this study provides a more detailed velocity structure, resulting in a more accurate and detailed map of the expected ground motions in Madison County during future earthquakes. In addition, the surface geologic map reveals regions that are susceptible to liquefaction. This paper discusses the methods used to construct the surface geology map, subsurface structure contour maps, isopach maps, and 3D geologic model used in the seismic hazard and liquefaction hazard analyses of Madison County, Tennessee, and summaries of the results of these analyses are presented.

Regularization by double complementary priors for full waveform inversion

Full-waveform inversion (FWI) represents an advanced geophysical imaging technique focused on intricately depicting subsurface physical properties by iteratively minimizing the differences between the simulated and observed seismograms. Unfortunately, the conventional FWI utilizing a least-squares loss function suffers from various drawbacks, including the challenge of local minima and the necessity for human intervention in parameter fine-tuning. It is particularly problematic when handling noisy data and inadequate initial models. Recent works have exhibited promising performance in two-dimensional FWI by integrating structural sparse representation to procure adaptive dictionaries. Drawing inspiration from the competitiveness of structural sparse representation, we introduce a paradigm of group sparse residuals that integrates two types of complementary prior information by harnessing both the internal and external subsurface media models. The proposed algorithm is based on an alternate minimization algorithm to guarantee workflow flexibility and efficient optimization capabilities. We experimentally validate our method for two baseline geological models, and a comparison of the results demonstrates that the proposed algorithm faithfully recovers the velocity models and consistently outperforms other traditional or learning-based algorithms. A further benefit from the group sparse coding used in this method is that it reduces the sensitivity to data noise.

ARTIFICIAL INTELLIGENCE FOR OPTIMIZED WELL CONTROL AND MANAGEMENT IN SUBSURFACE MODELS WITH UNPREDICTABLE GEOLOGY

A comprehensive control policy structure utilizing Artificial Intelligence (AI) is presented for closed-loop management in subsurface models. Conventional Closed-Loop Optimisation (CLO) approaches entail the iterative implementation of information assimilation, past synchronization details, and effective optimizing procedures. Information assimilation is more difficult when there is uncertainty in the geological approach and the specific model conclusions. Closed-Loop Reservoir Monitoring (CLRM) offers a control strategy that promptly correlates flow information obtained from wells, as typically accessible, to appropriate well stress configurations. The rule is characterized by time-based compression and gate-based converter sections. Learning is conducted during a preprocessing phase utilizing geological modeling derived from various geological settings. Illustrative instances of oil extraction using water insertion, utilizing both 2 and 3-dimensional geological designs, are shown. The AI-oriented technique demonstrates a 17.2% increase in Net Present Value (NPV) for 2D instances, an additional 31.5% for 3D cases compared to the effective optimization of previous models, and a 1-6.5% enhancement in NPV relative to standard CLRM. Based on the methods and variable configurations examined in this study, the controlling policy method yields a 71.34% reduction in processing expenses compared to conventional CLRM.

Use of Deep-Learning-Accelerated Gradient Approximation for Reservoir Geological Parameter Estimation

The estimation of space-varying geological parameters is often not computationally affordable for high-dimensional subsurface reservoir modeling systems. The adjoint method is generally regarded as an efficient approach for obtaining analytical gradient and, thus, proceeding with the gradient-based iteration algorithm; however, the infeasible memory requirement and computational demands strictly prohibit its generic implementation, especially for high-dimensional problems. The autoregressive neural network (aNN) model, as a nonlinear surrogate approximation, has gradually received increasing popularity due to significant reduction of computational cost, but one prominent limitation is that the generic application of aNN to large-scale reservoir models inevitably poses challenges in the training procedure, which remains unresolved. To address this issue, model-order reduction could be a promising strategy, which enables us to train the neural network in a very efficient manner. A very popular projection-based linear reduction method, i.e., propel orthogonal decomposition (POD), is adopted to achieve dimensionality reduction. This paper presents an architecture of a projection-based autoregressive neural network that efficiently derives an easy-to-use adjoint model by the use of an auto-differentiation module inside the popular deep learning frameworks. This hybrid neural network proxy, referred to as POD-aNN, is capable of speeding up derivation of reduced-order adjoint models. The performance of POD-aNN is validated through a synthetic 2D subsurface transport model. The use of POD-aNN significantly reduces the computation cost while the accuracy remains. In addition, our proposed POD-aNN can easily obtain multiple posterior realizations for uncertainty evaluation. The developed POD-aNN emulator is a data-driven approach for reduced-order modeling of nonlinear dynamic systems and, thus, should be a very efficient modeling tool to address many engineering applications related to intensive simulation-based optimization.

FWI-WF: Robust full-waveform inversion using hybrid Wasserstein-1 and Fourier metrics

The performance of full-waveform inversion (FWI) in constructing high-resolution subsurface models is closely related to the design of mismatch functions. The least-squares norm ([Formula: see text]) is commonly used; however, it is prone to local minima when high-quality initial guess and low-frequency data are unavailable. The Wasserstein-1 ([Formula: see text]) metric captures time shifts more effectively, but it may be plagued by imprecise deep structures. The Fourier metric leverages power spectra from simulated and observed data, offering higher-resolution updates near solutions. We develop a progressive waveform inversion method called FWI-WF using [Formula: see text] and Fourier metrics. Specifically, in the early stage of inversion, we apply greater weight to the [Formula: see text] metric for constructing a good background model and avoiding falling into local minima. Then, the Fourier metric gradually dominates to refine edges and deep structures, providing high-resolution inversion results. During the optimization process, we use automatic differentiation to improve inversion efficiency. Experimental results on three baseline geologic models indicate that FWI-WF outperforms three state-of-the-art methods.

An efficient approach of meshless node placement in three-dimensional subsurface flow modeling

Meshless methods are valuable tools for real-time subsurface flow modeling and are particularly beneficial for adaptively adjusting node positions when incorporating supplementary measurements. However, an algorithm is required for automatically generating high-quality meshless nodes to adapt to irregularly shaped aquifers and arbitrarily distributed wells. This paper introduces a three-dimensional adaptive meshless node placement technique based on advancing front nodes and proposes a sigmoid exclusion circle to ensure adaptability to specific positions of interest. It is crucial for groundwater simulations to have specified computational points at wellbore locations. The generated meshless nodes were used as computational points in the generalized finite difference method. The node quality was assessed by comparing the numerical solutions to the analytical solution, achieving errors of the order of 10–12. The application scenarios of irregularly shaped aquifers demonstrated the high efficiency of the three-dimensional node placement in generating up to 10,000 nodes in <5 s. The effectiveness of the algorithm was verified using three-dimensional irregular computational domains. The algorithm could be easily adapted to two-dimensional problems, and an irregular boundary example is presented. The validation and application cases highlighted the versatility of the proposed method and established its potential for real-world hydrogeological applications.

2D Transdimensional Joint Inversion of Radio Magnetotelluric and Electrical Resistivity Tomography Data

Summary The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the datasets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the dataset information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary datasets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates.

A comprehensive crosshole seismic experiment in glacial sediments at the ICDP DOVE site in the Tannwald Basin

Abstract. Glaciers have shaped the Alpine landscape by carving deep valleys and depositing sediments to form overdeepened basins. Understanding these processes provides information on the evolution of the climate and landscape. One such overdeepened structure is the Tannwald Basin (ICDP site 5068_1) north of Lake Constance, which was formed by the Rhine Glacier in several glacial cycles. In order to study these sediments and their seismic properties down to about 160 m depth, we conducted seismic crosshole experiments between three boreholes, obtaining compressional (P) wave data. The P-wave data are generated by a sparker source and recorded by a 24-station hydrophone string. We present the data acquisition and review our approach for future optimization, suggesting a finer time sampling interval and a separate registration of borehole and surface receivers. Travel-time tomography of the P-wave first-arrival picks under geostatistical constraints yields initial subsurface models. The tomograms correlate well with cased-hole sonic logs and the lithology derived from the core of one of the boreholes. These results will be further investigated in future research, which will include full-waveform inversion (FWI) to obtain high-resolution subsurface models.

Simultaneous seismic interpolation and statics estimation of land data

Imaging and inversion of land seismic data affected by complex weathering layers near the surface are challenging. When the data are additionally subsampled for economical reasons such as monitoring of sequestrated carbon dioxide and hydrogen, the problem is further exacerbated due to the combined influence of subsampling and weathering layers. On the one hand, interpolation performs poorly since the weathering layers reduce the data's coherency. On the other hand, near-surface corrections require knowledge of the subsurface model, separation between primaries and multiples as well as subsurface velocity estimation, which are difficult to perform from subsampled data. To overcome these hurdles, we combine seismic interpolation and statics estimation into a joint single rank-reduction-based algorithm. To our knowledge, this is the first time that this has been done. The proposed method simultaneously accounts for the weathering and subsampling effects, which both contribute to the low-rank structure destruction typically associated with statics-free densely-sampled data, to provide accurate reconstruction. Since a low-rank approximation is used for statics estimation, we also use it in rank-minimization interpolation as a cost-free initial solution to the optimization problem. As both statics estimation and interpolation operate in the midpoint-offset domain, we avoid the cost of transformations back and forth from the source-receiver to midpoint-offset transform domain. Consequently, the proposed reconstruction, which shows its potential on synthetic and field data is also computationally efficient.

A novel subsurface damage model in diamond wire sawing of silicon wafers

The subsurface damages (SSDs) generated during fixed abrasive diamond wire sawing (DWS) of silicon wafers can reduce the fracture strength and increase the breakage probability of these wafers. It is crucial to accurately evaluate these SSDs. A theoretical model of SSD depth is developed for the fixed abrasive DWS of silicon wafers considering the size effects of material properties, micro-geometries of abrasive grits, and inclination/interaction effects of subsurface cracks. A series of silicon wafers are processed, and the silicon material properties, diamond wire parameters, and surface/subsurface morphologies of silicon wafers are measured. The model is experimentally validated and then used to study the effects of processing parameters on inclination/interaction effects, cutting behaviors, and SSDs. The results show that the model has a relative error of less than 5.0% after revealing that the inclination/interaction parameters, cutting behavior parameters, and SSD depth almost follow a normal distribution, with a maximum distribution probability ranging from 30% to 50%. The average inclination angle is approximately 30°, the average cutting depth is in the range of one to two hundred micrometers, the average load is a few millinewtons, and the SSD depth is in the range of a few micrometers. With an increasing density of abrasive grit, a decreasing feed rate, or an increasing wire speed, the interaction effect becomes more pronounced, while the inclination angle, cutting depth, load, active grit ratio, and SSD depth decrease. When the protrusion height increases, or the half sharpness angle or tip radius of abrasive grit decreases, there is an increase in the cutting depth, load, active grit ratio, or SSD depth. The inclination angle decreases as the protrusion height, half sharpness angle, or tip radius increases. This research helps to understand the cutting mechanism and evaluate the SSDs during the DWS of silicon wafers.

A New Methodology for Quantitative Risk Assessment of CO2 Leakage in CCS Projects

Summary Assessing risks during carbon dioxide (CO2) sequestration involves a systematic process to identify them (risk assessment), analyze, prioritize, score them (risk analysis), develop mitigation strategies, and plan for measurement, monitoring, and verification (MMV) activities. In this context, risk assessment and analysis are often conducted involving qualitative approaches and/or subjective evaluations based on experts judgments. In this paper, we propose to use numerical results of dynamic subsurface modeling to assess risks in a more precise, less subjective, and quantitative manner, enabling a comprehensive definition of effective MMV plans. In this context, MMV evolves into modeling, measurements, monitoring, and verification [(M)MMV]. The conceptual model used for this study integrates flow and geomechanical-coupled behavior to assess caprock integrity, faults reactivation, and ultimately the risk of CO2 leakage. Uncertainty analysis and a 4D probabilistic workflow are used to quantify the likelihood and consequences of CO2 leakage and to score risks. The proposed approach offers a general framework to enable subsurface-modeling-based quantitative risk assessment (QRA) for the definition of successful (M)MMV plans. In this study, we have focused on the QRA of leakage risks through the fault and intact caprock. Other applications are possible, such as: (1) leakage risks through the caprock resulting from caprock failure; (2) lateral CO2 migration leading to permit limits violation or inducing interferences with neighboring activities; (3) leakage risks resulting from loss of well integrity; (4) risks of faults reactivation; (5) risks of surface heave; and (6) risks of induced seismicity. All these results can be used to formulate effective (M)MMV strategies for an optimized and cost-effective risk mitigation.

Application of Experimental Configurations of Seismic and Electric Tomographic Techniques to the Investigation of Complex Geological Structures

The main purpose of this study is the subsurface investigation of two complex geological environments focusing on the improvement of data acquisition and processing parameters regarding electric and seismic tomographic techniques. Two different study areas, in central–east Peloponnese and SE Attica, were selected, where detailed geological mapping and surface geophysical survey were carried out. The applied geophysical survey included the application of electrical resistivity tomography (ERT), seismic refraction tomography (SRT) and ground penetrating radar (GPR). The geoelectrical measurements were acquired with different arrays and electrode configurations. Moreover, various types of seismic sources were used at seventeen shot locations along the seismic arrays. For the processing of geoelectrical data, clustered datasets were created, increasing the depth of investigation and discriminatory capability. The seismic data processing included the following: (a) the creation of synthetic models and seismic records to determine the effectiveness and capabilities of the technique, (b) spectral analysis of the seismic records to determine the optimal seismic source type and (c) inversion of the field data to create representative subsurface velocity models. The results of the two techniques successfully delineated the complex subsurface structure that characterizes these two geological environments. The application of the ERT combined with the SRT are the two dominant, high-resolution techniques for the elucidation of complex subsurface structures.

A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling

In this study, we systematically investigate an inverse method for heterogeneous porous media to obtain porosity and permeability fields considering only production well data. The forward modeling of the input data, namely pressure and saturation fields, is based on the motion equations of a coupled Darcy flow system involving two phases of isothermal fluid flow. We discretize these equations using a multiscale finite volume simulation technique in the spatial domain, and the backward Euler method in time domain. In the inversion procedure, we combine a global optimization method, the Markov Chain Monte Carlo (MCMC) method, with a local optimization method, the Levenberg–Marquardt (LM) method. The MCMC was implemented as the Random Walk algorithm, and to generate the samples of the porosity and permeability fields, we employed Karhunen–Loève (KL) expansion of second-order stationary fields with Gaussian covariance. The coefficients of the KL expansion are estimated by minimizing the norm of the residual dependent on these fields. At the end of the MCMC iterations, we refine the KL coefficients associated with the porosity and permeability fields using the LM method. We verify in the numerical experiments that the accuracy of the porosity and permeability fields is improved by the LM refinement step.

Nonparametric and Continuous Variable-Based Stratigraphic Modelling from Sparse Boreholes using Signed Distance Function and Bayesian Compressive Sensing

An accurate stochastic interpretation of subsurface stratigraphy with quantified uncertainty can benefit the subsequent risk management of geotechnical infrastructure. Traditional approaches to developing geological cross-sections from sparse boreholes typically require the calibration or definition of empirical model parameters and functions, which may introduce subjectivity and bias. In this study, a non-parametric and continuous variable-based spatial predictor that leverages the signed distance function and Bayesian compressive sensing (BCS) is proposed for subsurface stratigraphic modelling. The proposed method transforms sparse categorical borehole data from a low-dimensional space into continuous variables in a high-dimensional space, enabling a comprehensive representation of more implicit characteristics of intricate geological patterns. This transformation facilitates the use of the continuous-variable-based BCS for non-parametric spatial prediction. The most probable geological cross-section and uncertainty qualification plot are derived after transforming spatially interpreted fields of continuous variables back into soil types. The performance of the proposed method is demonstrated using synthetic and real-world cases. Results indicate that the proposed approach can handle intricate stratigraphic scenarios characterized by complex geological structures, such as crossed-inclined, folded, inclined-folded, and interbedded strata, in a data-driven and non-parametric manner. The advantages of the proposed method over existing spatial predictors for developing geological cross-sections are also demonstrated.

Model simplification to simulate groundwater recharge from a perched gravel-bed river

Gravel-bed rivers are an important source of groundwater recharge in some regions of the world. Their interactions with groundwater are complex and highly variable in space and time, with considerable water storage in the riverbed sediments. In losing river sections, where most of the groundwater recharge occurs, the river can be separated from the regional groundwater system by an unsaturated zone (i.e., perched). The complexity of groundwater–surface water interactions in these environments calls for the use of 3D fully integrated hydrological models to represent them, but their computational intensity limits their practicality for parameter inference, uncertainty quantification and regional scale problems. On the other hand, the simple groundwater–surface water exchange functions currently implemented in regional scale groundwater models are not suited to represent complex gravel-bed river systems such as braided rivers. There is therefore a need for developing groundwater–surface water exchange functions tailored to gravel-bed rivers that can be used in regional scale models.To address this issue, we developed a model simplification framework that combines a 3D integrated surface and subsurface hydrological model, a 2D cross-sectional river-aquifer model and a 1D conductance-based analytical model. We aim at broadly simplifying the 3D model while ensuring the appropriate simulation of groundwater recharge. We demonstrate our modelling approach on the Selwyn River (New Zealand) using piezometric data and groundwater recharge estimates derived from field observations and satellite imagery.Our results indicates that groundwater recharge from this river can be simulated using a simple 1D analytical model, which can easily be implemented in regional groundwater models (e.g., MODFLOW models). However, to represent properly the time variability of groundwater recharge, it is essential to use the groundwater level in the shallow aquifer associated with the river as input to the regional groundwater model. Our approach is generally transferable to other gravel-bed rivers but requires some observations of river losses for proper calibration.

Full waveform inversion with smoothing of dilated convolutions

Abstract Full waveform inversion (FWI) is a method for building subsurface models with high resolution by optimizing a data-fitting problem. However, it is still a challenge to apply the FWI method to complicated subsurface models, because FWI is a highly ill-fitting inversion problem. When the simulated data differ from the observed data by more than half a cycle, FWI tends to suffer from the cycle-skipping problems and get trapped in a local minimum. To help the inversion to converge to the accurate subsurface model, we typically build a reasonable initial model or use low-frequency data to start our inversion. Here, we developed a novel technique called smoothing of dilated convolutions inversion (SDCI) to generate low-frequency components from seismic data to recover low-wavenumber components of the subsurface. In the theory part, we first present the objective function of the SDCI method and then derive the gradient of the objective function relative to the velocity using the adjoint-state approach. We then apply the method to a set of simulated data from the Marmousi model. The SDCI method can reasonably estimate the subsurface model even if the simulated seismic record does not contain information below 5 Hz. The numerical examples prove the effectiveness and feasibility of the proposed method. From the SDCI results, the FWI method recovers the velocity with higher accuracy and resolution.

Identifying potential artificial recharge zone in an arid craton

Identifying sustainable artificial recharge zones in arid cratons is challenging due to complex geology and limited natural recharge conditions, making accurate site selection and management difficult. This study integrates Vertical Electrical Sounding (VES), the Analytic Hierarchy Process (AHP), and Boolean analysis to identify sustainable artificial recharge zones in the arid Bundelkhand craton of India. Aquifer thickness and fractures emerged as critical determinants of groundwater recharge conditions, revealing varying degrees of suitability for recharge across the study area. Approximately 2.31% (13.36 km2) of the area along streams exhibited "very high" suitability, while 8.09% (45.82 km2) had "high" suitability. “Moderate" suitability covered 17.86% (101.66 km2), "low" suitability accounted for 38.85% (218.39 km2), and "very low" suitability represented 17.35% (98.75 km2) of the area. Recharge potential was highest in the northeast and central parts, with the middle of the watershed exhibiting the lowest potential. The study demonstrated that this integrated approach significantly improved precision from 71.40% to 85.70% and enhanced the F1 score from 0.833 to 0.923, surpassing the performance of the AHP method alone. The findings highlighted the importance of strategic selection and targeting of specific locations for artificial recharge, as only ∼18% of the study area was suitable for such efforts, despite ∼43% showing potential for groundwater. AHP with VES proves more precise and reliable than Fuzzy-AHP with VES, with AHP's conservative approach classifying 55.70% of the area as very low to low suitability compared to Fuzzy-AHP's 41.92%, ensuring only the most suitable sites are selected. VES offers cost-effectiveness, noninvasiveness, and rapid generation of a 1D subsurface model, balancing its lower detail compared to Electrical Resistivity Tomography. When combined with the AHP, VES enhances adaptability to changing conditions, emphasizing ecological preservation and climate change resilience. This approach effectively addresses water challenges in arid regions, contributing to sustainable water resource management.

9th Ishihara lecture: Effects of subsurface heterogeneity on liquefaction-induced ground deformation during earthquakes

The effects of subsurface heterogeneity on liquefaction phenomena during earthquakes are discussed using case histories and nonlinear dynamic analyses with different subsurface modeling approaches. The importance of geologic and anthropogenic controls and the effects of stratigraphic heterogeneity, lithological heterogeneity, and inherent soil variability at the project site scale are discussed. The results of these studies reinforce lessons regarding the importance of subsurface characterization and its representation in analyses for evaluating liquefaction-induced ground deformations and their impacts on civil infrastructure.

Solving East Nile Delta's imaging challenges through new ocean-bottom-node acquisition and advanced imaging

The East Nile Delta, located offshore Egypt, is a challenging area for seismic imaging due to the presence of substantial velocity heterogeneity created by the complex Messinian unconformity and overlaying fault structures. Existing towed-streamer (TS) seismic data have been pushed to their technical limit but remain insufficient for adequate representation of the subsurface. Previous studies have shown that a step change in imaging is possible if advanced acquisition, processing, and velocity model building techniques are employed. Atoll Field is a candidate to benefit from such improvements in seismic data. Results show that ocean-bottom-node acquisition provides improved spatial sampling and higher resolution. Acquiring seismic data with offsets of more than 30 km facilitated the generation of a robust high-frequency velocity model that better captures subsurface complexity. This paper outlines the acquisition and processing, with a focus on the velocity model building process. The generated subsurface model is effectively used for imaging with conventional migration algorithms and full-waveform-inversion-derived reflectivity. Results show a significant step change from previous TS images at the Oligocene target, with channel features clearly imaged. This provides an improved understanding of the depositional systems and reduced depth uncertainty.