Near-surface Model Research Articles

Comparison of surface-wave techniques to estimate S- and P-wave velocity models from active seismic data

Abstract. The acquisition of seismic exploration data in remote locations presents several logistical and economic criticalities. The irregular distribution of sources and/or receivers facilitates seismic acquisition operations in these areas. A convenient approach is to deploy nodal receivers on a regular grid and to use sources only in accessible locations, creating an irregular source–receiver layout. It is essential to evaluate, adapt, and verify processing workflows, specifically for near-surface velocity model estimation using surface-wave analysis, when working with these types of datasets. In this study, we applied three surface-wave techniques (i.e., wavelength–depth (W/D) method, laterally constrained inversion (LCI), and surface-wave tomography (SWT)) to a large-scale 3D dataset obtained from a hard-rock site using the irregular source–receiver acquisition method. The methods were fine-tuned for the data obtained from hard-rock sites, which typically exhibit a low signal-to-noise ratio. The wavelength–depth method is a data transformation method that is based on a relationship between skin depth and surface-wave wavelength and provides both S- and P-wave velocity (Vs and Vp) models. We used Poisson's ratios estimated through the wavelength–depth method to constrain the laterally constrained inversion and surface-wave tomography and to retrieve both Vs and Vp also from these methods. The pseudo-3D Vs and Vp models were obtained down to 140 m depth over an area of approximately 900 × 1500 m2. The estimated models from the methods matched the geological information available for the site. A difference of less than 6 % was observed between the estimated Vs models from the three methods, whereas this value was 7.1 % for the retrieved Vp models. The methods were critically compared in terms of resolution and efficiency, which provides valuable insights into the potential of surface-wave analysis for estimating near-surface models at hard-rock sites.

Numerical Simulation of Wave Equation Using the Finite Difference Method with Variable Refined Grid in Complex Near-surface Condition

The horizontal layered refined grid is adopted in the conventional finite difference method (FDM) of variable refined grid for the numerical simulation of wave equation. But, the method cannot well adapt to the characteristics of undulated topography and the variation of velocity structure in near-surface region. To solve this problem, a numerical simulation of wave equation using FDM with undulated variable refined grid is proposed in this paper. The grids are generated according to the undulated topography and the spatial distribution of velocity in the near-surface region from low-velocity to the high-velocity layer. For ensuring the accuracy while taking into account the computational efficiency, the FDM with variable coefficient in above grids is used to discretize the acoustic wave equation. The numerical evaluation and example show that the same accuracy as the method using fine grid can be obtained by the new method. However, its computational time is only 43.6% of the one spent by the fine grid. Furthermore, the new method can also stably adapt to the real complex loess plateau near-surface model in a working area of Ordos Basins.

Seismic Soundoff: A reality check on full-wave inversion

Öz Yilmaz returns to the podcast to highlight his award-winning article, “A reality check on full-wave inversion applied to land seismic data for near-surface modeling.”

Workshop Review: Recent workshop explores near-surface modeling and imaging

Modeling of the near surface plays a significant role in subsurface imaging, reservoir characterization, and monitoring. The Near-Surface Modeling and Imaging Workshop was created to address near-surface challenges in geoscience. The workshop has been held previously in 2014, 2016, and 2019. The latest instance of the workshop, 27–29 September 2022 in Muscat, Oman, continued the tradition.

Evaluating imaging uncertainty associated with the near surface and added value of vertical arrays using Bayesian seismic refraction tomography

AbstractTraditional tomographic methods do not consider the uncertainties associated with near-surface velocities and static corrections and provide a deterministic solution to the estimation problem. However, these uncertainties significantly affect structural mapping and interpretation of seismic imaging results. On the other hand, Bayesian first-arrival tomography provides multiple near-surface models that fit observed traveltimes equally well and enable the study of potential solution distributions. We demonstrate this approach on a complex synthetic near-surface model, representative of arid environments, to quantify associated velocity and statics uncertainties. We evaluate two different parameterizations for subsurface velocities in the context of near-surface Bayesian tomography: Voronoi tessellation with natural neighbor interpolation and the more conventional Delaunay triangulation with linear interpolation. Our analysis shows that the Voronoi cell parameterization with natural neighbor interpolation is more appropriate for this problem. Finally, the new approach is applied to compare two alternative acquisition geometries comprising conventional surface receivers and surface receivers augmented with vertical receiver arrays. The results demonstrate that adding vertical receiver arrays to conventional surface receivers can significantly reduce the near-surface velocity uncertainty and thus increases the accuracy of the seismic imaging results. Furthermore, the study shows that Bayesian tomography can be used as a tool for evaluating different source and receiver geometries during the acquisition design stage.

Reviews

Land Seismic Case Studies for Near-Surface Modeling and Subsurface Imaging, by Öz Yilmaz, 2021.

Model parameter design for modeling surface topography in VTI elastic finite-difference near-surface simulations

The near-surface anisotropy is one of the factors leading to an inaccurate velocity estimation in the shallow area, which has a strong impact on the static correction of land data and seismic imaging of the subsurface in depth. To investigate the anisotropy influence, the consideration of medium anisotropy in near-surface seismic modeling becomes necessary. In the oil and gas industry, finite-difference (FD) methods are commonly used for seismic wave modeling, thanks to their simplicity, accuracy, and high efficiency. However, for near-surface modeling, the implementation of free-surface boundary conditions and nonflat topography representation are the two main obstacles preventing the effectiveness of FD methods. To address these two issues in the vertical transversely isotropic anisotropic scenarios, we have developed a simple and efficient method for the discrete model design which can be easily applied to the conventional Cartesian-grid FD modeling. Our method involves: (1) the parameter-averaging method for implicitly implementing the stress-free condition by a modification of model anisotropy parameters near the (non) flat free-surface boundary and (2) an independent wavefield superposition with modeling results of different parameter configurations to accurately represent the rugged topography and significantly reduce the staircase diffractions caused by a staircase approximation of continuous surface in the Cartesian-grid discretization. For validation of this method, we conduct several numerical tests in 2D and 3D spaces. The accuracy is demonstrated by a comparison of the spectral-element solutions of SPECFEM in modeling the seismic wave propagation in the presence of an irregular free surface. From the aspect of computational efficiency, it is more promising in practical applications due to the use of wavefield superposition strategy in this method which does not require finer spatial sampling to eliminate the staircase diffractions.

A frequency-velocity CNN for developing near-surface 2D vs images from linear-array, active-source wavefield measurements

This paper presents a frequency-velocity convolutional neural network (CNN) for rapid, non-invasive 2D shear wave velocity (VS) imaging of near-surface geo-materials. Operating in the frequency-velocity domain allows for significant flexibility in the linear-array, active-source experimental testing configurations used for generating the CNN input, which are normalized dispersion images. While normalized dispersion images retain the most important aspects of near-surface wavefields, they are relatively insensitive to the exact experimental testing configuration used to generate and record the wavefields, accommodating various source types, source offsets, numbers of receivers, and receiver spacings. We demonstrate the effectiveness of the frequency-velocity CNN by applying it to a common near-surface geophysics problem, namely, imaging a two-layer, undulating, soil-over-bedrock interface. The frequency-velocity CNN was trained and tested using 100,000 synthetic near-surface models with variable soil-over-bedrock conditions. Then, the ability of the frequency-velocity CNN to generalize across various acquisition configurations was rigorously tested using thousands of synthetic near-surface models with different acquisition configurations from that of the training set. Lastly, it was applied to experimental field data collected at the Hornsby Bend site in Austin, Texas, USA and found to produce a subsurface 2D image that was in great agreement with ground truth from invasive site characterization data.

Diagnosing Near-Surface Model Errors with Candidate Physics Parameterization Schemes for the Multiphysics Rapid Refresh Forecast System (RRFS) Ensemble during Winter over the Northeastern United States and Southern Great Plains

Abstract During the winter of 2020/21 an ensemble of FV3-LAM forecasts was produced over the contiguous United States for the Winter Weather Experiment using five physics suites. These forecasts are evaluated with the goal of optimizing physics parameterizations within the future operational Rapid Refresh Forecast System (RRFS) in the Unified Forecast System (UFS) realm and for selecting suitable physics suites for a multiphysics RRFS ensemble. The five physics suites have different combinations of land surface models (LSMs), planetary boundary layer (PBL) parameterizations, and surface layer schemes, chosen from those used in current and possible future operational systems and likely to be supported in the operational UFS. Full-season evaluation reveals a persistent near-surface cold bias in the U.S. Northeast from one suite and a nighttime warm bias in the southern Great Plains in another suite, while other suites have smaller biases. A representative case is chosen to diagnose the cause for each of these biases using sensitivity simulations with different physics combinations or modified parameters and verified with additional mesonet observations. The cold bias in the Northeast is attributed to aspects of the Noah-MP LSM over snow cover, where Noah-MP simulates lower soil water content, and thus lower thermal conductivity than other LSMs, leading to less upward ground heat flux during nighttime and consequently lower surface temperature. The nighttime warm bias found in the southern Great Plains is attributed to overestimation of vertical mixing in the K-profile-based eddy-diffusivity mass-flux (K-EDMF) PBL scheme and insufficient land–atmospheric coupling from the GFS surface layer scheme over short vegetation. A few key parameters driving these systematic biases are identified.

Prediction of Deflection Behavior of NSM Strengthened Reinforced Concrete Beam Using Fuzzy Logic

Abstract This paper aims to present a deflection prediction model of Near Surface Mounted (NSM) Reinforce Concrete (RC) beams using the Fuzzy Logic Expert System (FLES) with different types of membership functions (MF). The absence of a complete theoretical deflection prediction model of NSM-strengthened RC beams persuades this research to develop an Artificial Intelligence (AI) based prediction model using FLES. The proposed model uses triangular and trapezoidal MF to predict the deflection behavior of six NSM-strengthened RC beams. The research variables are strengthening materials and NSM bar length. In this study, two inputs (applied load and variable length) were used to predict two outputs (deflection of two types of strengthened RC beams). The relative error of predicted values was within 5% and the suitability of fit was close to 1.0 which affirms the efficacy of the FLES. Besides, a tiny difference was detected using triangular and trapezoidal MF for the prediction model.

Central Iranian playas: The first 3D near-surface model

Playas are a valuable tool for studying the climate of the past. In recent years, several studies have been conducted on Iranian playas, but there is a lack for subsurface models of playas. The aim of this research was to model the subsurface of playa to reconstruct the past climate. We employed the drilling cores of wells drilled in the two large playas in central part of Iran and created 3D models from near-surface structures. We obtained 3 models: an eight-layer model and a 4- layer model and a 2-layer models in 3 different parts. Based on the models, we have proposed a scenario for the evolution of central Iran playas. The 3D models show that the overlaying of the layers not only follows the morphology of the bedrock, but also that the mud layers sit exactly in the wet basins. In other words, layers of mud have formed in wetter-than-present surfaces. The 8-layers model suggest at least four periods of severe climate change had occurred in central Iran. Our 3D models are consistent with the reality of the climate change during Quaternary time scale in arid environment of central Iran.

Diving Wave Tomography: Velocity Modelling Using First Arrival Traveltime

In hydrocarbon exploration, information carried by diving waves and post-critical reflections that are used to reconstruct the long-to-intermediate wavelength of the subsurface is an integral part of successful velocity model building. Diving wave tomography (DWT) is one of the tools for shallow velocity assessment particularly when seismic data has poor signal-to-noise ratio (SNR) with complex geologic settings where no clear reflector is present. Considering the relationship between velocity with time and space, the output from tomography plays a crucial role to align data between time and depth domain and produce a reliable image of the deeper structure where hydrocarbon reservoir is typically located. In geophysics, tomography is primarily used to correct seismic trace alignment to produce a reliable stack section. In advanced imaging it is used as an initial model for waveform inversion in an integrated workflow. In the post-processing stage, it is used to correct the misfit between well logs and seismic data and is crucial for the quantitative analysis of rock physics. In this paper, we focus on tomography and its working principle on near-surface velocity modelling. We restricted our workflow to 2D synthetic data simulating the shallow gas occurrence that is prominent in the offshore Malay Basin to demonstrate how tomography works in velocity reconstruction. Results from synthetic and real data example shows that DWT can recover local large-scale structure and improved stacked data, considering no other seismic data and constraint from well data is included in the iterative process.

Groundwater silcrete linked to brine migration in a continental rift: An alternative to the near-surface model of silcrete

Silcretes have long been recognized as modern and ancient duricrusts, but more recently also as silicified layers precipitated at groundwater tables, termed groundwater silcrete. However, the silica sources, transport mechanisms, and precipitation processes of groundwater silcrete are not well understood, and models are limited to the near-surface groundwater environment, where silica saturation is low. Here, an example of a groundwater silcrete from Upper Cambrian strata of the Potsdam Group is described and interpreted to be formed in a rift where Cambrian fault reactivation coincided with silcrete formation. Field relationships strongly support a connection between fault activity and silicification, including a systematic thickening and development of massive silcrete horizons above shear zones, brecciated silcrete near where faults intersect shear zones, and nodules along the margins of shear zones. Petrographic and cathodoluminescence microscopy of silcrete reveal early pre-compaction overgrowth cements with abundant primary fluid inclusions. Fluid inclusion microthermometry indicates that these fluids were high salinity (22.7–25.8 eq. wt% NaCl+CaCl2) brines with homogenization temperatures of ~120.2 °C–151.6 °C, which implies that silica precipitated from a hot, silica-saturated crustal brine from Grenville Province basement. A combination of weathering reactions and direct quartz dissolution explains the chemical evolution of the source fluid, which likely originated as infiltrated meteoric water that had chemically equilibrated with Grenville crust at depth. Later, this brine was mobilized upward along reactivated faults during the Late Cambrian, and ultimately to the water table, where a combination of reduced pH and temperature promoted quartz supersaturation and quartz overgrowths on detrital quartz. This case example, therefore, expands the definition of silcrete to include near-surface silicification from externally sourced crustal fluids, here termed brine silcrete, and provides a basis for interpreting silcrete as a feature of deformation and fluid migration along shear zones in fault-bounded continental basins.

Reconstruction of the Near-Surface Model for the Conditions of Eastern Siberia Using Full Waveform Inversion

Reconstruction of the Near-Surface Model for the Conditions of Eastern Siberia Using Full Waveform Inversion

Unlocking ultra-high-density seismic for CCUS applications by combining nimble nodes and agile source technologies

In the quest for denser, nimbler, and lower-cost seismic surveys, the industry is seeing a revolution in the miniaturization of seismic equipment, with autonomous nodes approaching the size of a geophone and sources becoming portable by crews on foot. This has created a paradigm shift in the way seismic is acquired in difficult terrains, making zero-environmental-footprint surveys a reality while reducing cost and health, safety, and environmental risk. The simplification of survey operation and the new entry price of seismic surveys unlocked by these technologies are already benefiting industries beyond oil and gas exploration. High trace density seismic has become accessible to industries playing a key role in the net-zero era, such as geothermal and carbon capture, utilization, and storage (CCUS), to which a good understanding of the subsurface geology is crucial to their success. We describe these benefits as observed during an ultra-high-density seismic survey acquired in June 2020 through a partnership between STRYDE, Explor, and Carbon Management Canada over the Containment and Monitoring Institute site. The smallest and lightest source and receiver equipment in the industry were used to achieve a trace density of 257 million traces/km2 over this test site dedicated to CCUS studies. We discuss the operational efficiency of the seismic acquisition, innovative techniques for data transfer and surveying, and preliminary results of the seismic data processing with a focus on the near-surface model and fast-track time migration.

A reality check on full-wave inversion applied to land seismic data for near-surface modeling

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.

Seismic Soundoff: Exploring land seismic case studies

In this episode, Öz Yilmaz discusses his latest book, Land Seismic Case Studies for Near-Surface Modeling and Subsurface Imaging. Written for practicing geophysicists, the book is the culmination of land seismic data acquisition and processing projects conducted by Yilmaz over the last two decades. His expertise and experience are highlighted in detail in this revealing and essential conversation. Hear the full episode at https://seg.org/podcast/post/12564 .

Case Study of a Long-lived Tornadic Mesocyclone in a Low-CAPE Complex-terrain Environment

On 22 May 2008 a long-lived mesocyclone spawned an EF2 tornado over terrain as high as 2650 m MSL in southeastern Wyoming. The mesocyclone was part of an elongated, complex storm system that grew rather early in the day near a slow-moving warm front. The mesocyclone is unusual in that it persisted and became tornadic in rather cold (~7°C), saturated surface conditions in an environment with CAPE < 1000 J kg-1 and no surface-based convective inhibition. The mesocyclone intensified as its parent storm moved over terrain gradually ascending by ~1000 m, reaching a radar-estimated low-level horizontal shear as high as 84 m s-1 km-1. This fast-moving mesocyclone could be tracked by the nearest Doppler radar for over 90 min.
 This paper examines the characteristics and the environment of this mesocyclone using both operational weather data and high-resolution numerical simulations. Near-surface radar observations and model output suggest that the formation and maintenance of the mesocyclone in this low-CAPE environment benefited from two terrain-related factors. One is the observed channeling of the low-level flow, locally enhancing the storm-relative helicity. The second is the presence, suggested by high-resolution simulations, of banners of high potential vorticity generated by the strong southerly flow shearing around the Colorado Front Range.

Multiparameter viscoelastic full-waveform inversion of shallow-seismic surface waves with a pre-conditioned truncated Newton method

SUMMARY 2-D full-waveform inversion (FWI) of shallow-seismic Rayleigh waves has become a powerful method for reconstructing viscoelastic multiparameter models of shallow subsurface with high resolution. The multiparameter reconstruction in FWI is challenging due to the potential presence of cross-talk between different parameters and the unbalanced sensitivity of Rayleigh-wave data with respect to different parameter classes. Accounting for the inverse Hessian using truncated Newton methods based on second-order adjoint methods provides an effective tool to mitigate cross-talk caused by the coupling between different parameters. In this study, we apply a pre-conditioned truncated Newton (PTN) method to shallow-seismic FWI to simultaneously invert for multiparameter near-surface models (P- and S-wave velocities, attenuation of P and S waves, and density). We first investigate scattered wavefields caused by these parameters to evaluate the coupling between them. Then we investigate the performance of the PTN method on shallow-seismic FWI of Rayleigh wave for reconstructing all five parameters simultaneously. The application to spatially correlated and uncorrelated models demonstrates that the PTN method helps to mitigate the cross-talk and improves the resolution of the multiparameter reconstructions, especially for the weak parameters with small sensitivity such as attenuation and density parameters. The attenuation of Pwaves cannot be inverted reliably due to its negligible sensitivity on the Rayleigh wave. The comparison with the classical pre-conditioned conjugate gradient method highlights the improved performance of the PTN method and thus the benefit of accounting for the information included in the Hessian.

Simulation of scattered seismic surface waves on mountainous onshore areas: Understanding the “ground roll energy cone”

The accurate simulation of seismic surface waves on complex land areas requires elastic models with realistic near-surface parameters. The SEAM Phase II Foothills model, proposed by the SEG Advanced Modeling (SEAM) Corporation, is one of the most comprehensive efforts undertaken by the geophysics community to understand complex seismic wave propagation in foothills areas. However, while this model includes a rough topography, alluvial sediments, and complex geologic structures, synthetic data from the SEAM consortium do not reproduce the qualitative characteristics of the scattering energy that is generally interpreted as the “ground roll energy cone” on shot records of real data. To simulate the scattering, a near-surface elastic model in mountainous areas ideally must include the following three elements: (1) rough topography and bedrock, (2) low-velocity layer, and (3) small-scale heterogeneities (size approximately Rayleigh wavelength). The SEAM Foothills model only includes element (1) and, to a lesser extent, element (2). We represent a heterogeneous near surface as a random medium with two parameters: the average size of the heterogeneities and fractional fluctuation. A parametric analysis shows the influence of each parameter on the synthetic data and how similar it is compared to real data acquired in a foothills area in Colombia. We perform the analysis in the shot gather panel and dispersion image. Our study shows that it is necessary to include the low-velocity layer and small-scale distributed heterogeneities in the shallow part of the SEAM model to get synthetic data with realistic scattered surface-wave energy.