Land Seismic Data Research Articles

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.

On-demand high-resolution areal scouting via unmanned aerial vehicles enables sustainable near-surface geoscience data acquisition

An accurate data acquisition plan is critical for near-surface geoscience investigations. Detailed understanding of the terrain and land cover of the survey area is essential to designing a data acquisition layout that efficiently meets the objective while mapping potential obstacles. Using satellite-based data is the most affordable and straightforward method to obtain this information. However, near-surface geoscience investigations demand a higher-resolution map than commonly available satellite imagery to design a detailed acquisition plan. This paper presents a concept that utilizes unmanned aerial vehicles (UAVs) to successfully and sustainably perform on-demand high-resolution areal scouting to aid near-surface geoscience data acquisition campaigns and seismic drone deployment. We share the results of two field trial cases where the presented concept was evaluated. Case A is a 16 km2 extensive outcrop investigation in an arid desert environment. The initial satellite-based acquisition plan was changed considerably when UAV data sets became available. This led to an approximately 60% improvement in data productivity and efficiency, effectively reducing the carbon footprint of conventional scouting operations. Case B utilizes high-resolution areal scouting to determine the safe landing zone for autonomous seismic drone swarms to further support sustainable geophysical data acquisition. The UAV data enable seismic drone swarms to land optimally on preprogrammed landing points while navigating subsatellite-resolution obstacles. These two successful cases suggest that analogous productivity and efficiency improvements may be achieved in industrial-scale land seismic data acquisition planning that utilizes the UAV data set.

Polarization analysis and filtering of undersampled 3C seismic data in the frequency-slowness domain

Polarization filters have emerged as a useful tool for coherent noise denoising in multicomponent seismic data. By exploiting the polarization properties of seismic waves, these filters can significantly improve wave-type separation, which is a crucial step in seismic data processing. However, their effectiveness in land seismic data in complex areas can be impacted by the presence of scattering noise, and their applicability is limited by irregular spatial sampling, which hinders velocity information for wave-type discrimination. To address these limitations, we introduce a novel two-step method for distinguishing and separating polarized wave types in shot gathers captured from a spatially undersampled linear array of 3C vector sensors. The first step involves conducting a polarization analysis in the frequency-slowness domain using elliptical elements derived from the linear Radon transform of the seismic data. The second step isolates specific polarized waves using the 3C frequency-slowness polarization filter (3C-FSPF), a technique that uses tailored taper functions based on polarization analysis. Our method stands out from single-station filters that cannot use velocity information for wave-type discrimination and existing multistation filters that require regularly sampled data. To evaluate our method, comprehensive tests are conducted on synthetic and real data. The results consistently demonstrate the effectiveness of 3C-FSPF in separating diverse polarized wave types under conditions of irregular and sparse spatial sampling and noise. Our findings underscore the potential of this method for advancing exploration geophysics by enhancing the quality of seismic data, particularly in land regions with complex near-surface structures.

Full Dispersion‐Spectrum Inversion of Surface Waves

AbstractNowadays, the most successful applications of full‐waveform inversion (FWI) involve marine seismic data under acoustic approximations. Elastic FWI of land seismic data is still challenging in theory and practice. Here, we propose a full dispersion spectrum inversion method and apply it to seismic data acquired in West Antarctica. Inspired by the conventional surface wave dispersion curve inversion method, we propose to invert the surface wave dispersion spectrum instead of the complicated waveforms. We compare the frequency‐velocity, frequency‐slowness, and frequency‐wavenumber spectra in terms of their ability to resolve dispersion modes and the feasibility of their adjoint updates and conclude that the frequency‐slowness spectrum is the best for our inversion objectives. We test four objective functions, subtraction, zero‐lag crosscorrelation, optimal transport, and the local‐crosscorrelation to quantify the spectrum mismatch and provide the corresponding adjoint source. We then theoretically analyze the convexity of the proposed objective functions and examine their convergence behavior using numerical examples. We also compare the proposed method with the classic FWI method and the traditional surface wave dispersion curve inversion method and discuss the strengths and weaknesses of each method. This technique is employed to evaluate the shallow velocity structures beneath a seismic array stationed in West Antarctica. Our proposed inversion scheme is also useful for more general applications such as imaging the shallow subsurface of the critical zones, like geothermal reservoirs, and CO2 storage sites.

Combination of Fourier and CRS-Based Reconstruction Algorithms in Land Seismic Data

ABSTRACT. The reconstruction based on the partial CRS stacking operator presents higher signal-to-noise ratio and better continuity of events. However, irregularly sampled land data often introduce errors in the CRS attributes, creating artifacts that contaminate the seismic data. Recently, the combination of Fourier and CRS-based reconstruction algorithms has significantly solved these problems. The approach consists of applying a Fourier-based interpolation method as a regularization operator to the original data and then the CRS attributes are searched in the reconstructed data. The CRS attributes determined in this way are more accurate and they can be applied in two different forms, either in the interpolation and regularization of the original data or in the denoising of the Fourier-based reconstructed data. We propose to compare the combination of the Fourier-based interpolation methods MWNI and MPFI with the CRSbased interpolation method in order to evaluate which is the best preconditioner of the prestack data to search the CRS attributes. We applied the proposed flowcharts combining the interpolation methods mentioned above to the land seismic data from the Tacutu basin, which is vintage data with very low fold and noisy. The reconstructed data obtained by the combinations show significant improvements compared to the data reconstructed using the algorithms separately, in other words, the weaknesses and limitations of each method are overcome when they are applied in combination. The MWNI+CRS combination flow produces the best results, with the stacked section of reconstructed data showing better noise removal, enhancement of coherent events, better definition and continuity of steeply dipping events.

Two‐dimensional SH‐wave and acoustic P‐wave full waveform inversion: A Midland Basin case study

AbstractBuilding a reliable S‐wave velocity model remains challenging from P‐wave land seismic data as multi‐parameter elastic full waveform inversion which updates P‐ and S‐wave velocities simultaneously is not yet widely adopted due to its computational cost, highly nonlinear nature and noise contamination from land seismic data. To overcome this challenge, we propose implementing the SH‐wave full waveform inversion to obtain the S‐wave velocity model. The value of this approach has been examined by applying two‐dimensional time‐domain SH‐ and P‐wave acoustic full waveform inversion to a nine‐component three‐dimensional seismic survey acquired in the Midland Basin. The nine‐component seismic survey was acquired by using both vertical and horizontal component vibrators and receivers, which enables us to apply both SH‐ and P‐wave full waveform inversion to the raw and preprocessed shot gathers along a two‐dimensional line. The SH‐ and P‐wave full waveform inversion applied to the raw shot gathers provide more detailed P‐ and S‐wave velocities compared to the velocities from the stacking velocity analysis. Additionally, there are no problematic artefacts in the inverted S‐wave velocity model, which shows the stability of the SH‐wave inversion. Through the SH‐wave full waveform inversion from the preprocessed shot gathers, we reveal the lateral velocity variations in the Grayburg–San Andres interval, which coincides with the depositional environment changes suggested by the previous regional study mainly based on well log and cores. These variations demonstrate that SH‐wave full waveform inversion can identify additional geological features that are not observed in the inverted P‐wave velocity model. The inverted P‐ and SH‐wave velocities are validated by the flattening of the events observed in the common image gathers after Kirchhoff depth migration. The SH‐wave migration image further reveals the irregular geometries of carbonates in the Spraberry formation. Vp/Vs ratios calculated from the independently inverted P‐ and S‐wave velocity models show strong lateral variations in the Wolfcamp interval (key‐producing interval), which could be caused by the organic content variations between the reservoir and non‐reservoir rocks. The inversion results demonstrate that the SH‐wave full waveform inversion can be used to provide an S‐wave velocity model that is comparable to the P‐wave velocity model derived from acoustic full waveform inversion, which is widely used for P‐wave velocity model building from P‐wave land seismic data.

Self-supervised, active learning seismic full-waveform inversion

A novel recursive, self-supervised machine-learning (ML) inversion scheme is developed. It is applied for fast and accurate full-waveform inversion of land seismic data. ML generalization is enhanced by using virtual super gathers (VSGs) of field data for training. These are obtained from midpoint-offset sorting and stacking after applying surface-consistent corrections from the decomposition of the transmitted wavefield. The procedure implements reinforcement learning concepts by adopting an inversion agent to interact with the environment and explore the model space under a data misfit optimization policy. The generated parameter distributions and related forward responses are used as new training samples for supervised learning. The active learning (AL) paradigm is further embedded in the procedure, for which queries on data diversity and uncertainty are used to generate fully informative reduced sets for training. The procedure is recursive. At each cycle, the physics-based inversion is coupled to the ML predictions via penalty terms that promote a long-term data misfit reduction. The resulting self-supervised, AL, physics-driven deep-learning inversion generalizes well with field data. The method is applied to perform full-waveform inversion (FWI) of a complex land seismic data set characterized by transcurrent faulting and related structures. High signal-to-noise VSGs are inverted with a 1.5D Laplace-Fourier FWI scheme. The AL inversion procedure uses a small fraction of data for training while achieving sharper velocity reconstructions and a lower data misfit when compared with previous results. AL FWI is highly generalizable and effective for land seismic velocity model building and for other inversion scenarios.

OBSTransformer: a deep-learning seismic phase picker for OBS data using automated labelling and transfer learning

SUMMARY Accurate seismic phase detection and onset picking are fundamental to seismological studies. Supervised deep-learning phase pickers have shown promise with excellent performance on land seismic data. Although it may be acceptable to apply them to Ocean Bottom Seismometer (OBS) data that are indispensable for studying ocean regions, they suffer from a significant performance drop. In this study, we develop a generalized transfer-learned OBS phase picker—OBSTransformer, based on automated labelling and transfer learning. First, we compile a comprehensive data set of catalogued earthquakes recorded by 423 OBSs from 11 temporary deployments worldwide. Through automated processes, we label the P and S phases of these earthquakes by analysing the consistency of at least three arrivals from four widely used machine learning pickers (EQTransformer, PhaseNet, Generalized Phase Detection and PickNet), as well as the Akaike Information Criterion (AIC) picker. This results in an inclusive OBS data set containing ∼36 000 earthquake samples. Subsequently, we use this data set for transfer learning and utilize a well-trained land machine learning model—EQTransformer as our base model. Moreover, we extract 25 000 OBS noise samples from the same OBS networks using the Kurtosis method, which are then used for model training alongside the labelled earthquake samples. Using three groups of test data sets at subglobal, regional and local scales, we demonstrate that OBSTransformer outperforms EQTransformer. Particularly, the P and S recall rates at large distances (>200 km) are increased by 68 and 76 per cent, respectively. Our extensive tests and comparisons demonstrate that OBSTransformer is less dependent on the detection/picking thresholds and is more robust to noise levels.

Marchenko imaging assisted by vertical seismic profiling data for land seismic data in the Middle East

Attenuating interference from internal multiples has challenged seismic data imaging in the Middle East basins. The challenge results from the strong short-period internal multiples that exhibit nearly indistinguishable characteristics from the primaries reflected from the underlying reservoirs due to predominantly horizontal strata and occasional low-relief structures, as indicated in the Jurassic formations in Kuwait. To address the internal-multiple issues, multiple prediction followed by adaptive subtraction is the most common approach in the industry. However, due to the similarities between primaries and multiples, applying adaptive subtraction poses a high risk of primary-amplitude damage, preventing quantitative seismic data interpretation. Therefore, we examine the Marchenko method that retrieves Green’s functions from surface seismic data for target-oriented imaging without applying adaptive subtraction. Marchenko imaging has produced promising results on several offshore seismic data sets, but an onshore application is still needed. To better understand the effects of internal multiples and implement Marchenko imaging, we perform integrated analysis through well-log, vertical seismic profiling (VSP), and seismic data from a hydrocarbon field in Kuwait. In addition, we use VSP data to cross-check the retrieved Green’s functions and estimate the scaling factor of the Marchenko method. The results indicate that (1) the poor imaging at the center of the field is due to the destructive interference of internal multiples, (2) the reverberation of internal multiples between the evaporite formations of the overburden is the most likely candidate that affects the seismic images of the Jurassic reservoirs, (3) the retrieved Green’s functions conform to the recorded Green’s functions from VSP data, and (4) Marchenko imaging provides a means to improve the seismic images of the Jurassic formations in Kuwait.

Ultra‐resolution surface‐consistent full waveform inversion

AbstractFull waveform inversion for land seismic data requires the development of specific strategies for modelling the complex response associated to the near surface. Seismic wave propagation is distorted by several effects, such as topographic relief, wavefield scattering, attenuation (often frequency‐dependent) and anisotropy. The modelling of such shallow complexities is often unmanageable by parametric inversions such that data preconditioning and surface‐related corrections are required before tackling the full waveform inversion velocity modelling problem. We developed a unified framework addressing both surface‐consistent corrections and full waveform inversion by using the transmitted portion of the wavefield. The problem of surface‐consistent decomposition is recast in terms of the transmitted wavefield leading to kinematic and dynamic corrections to account for sub‐resolution wave propagation distortions occurring in the near surface weathering layer. Seismic data are deconvolved from the effects of the near surface to better represent the deeper subsurface wavefield propagation. Signal‐to‐noise enhancement is obtained after the surface‐consistent transmission preconditioning by the generation of virtual super gathers reconstructed in the midpoint‐offset sorting domain. An original scheme of 1.5D Laplace–Fourier full waveform inversion, involving 3D radiation and 1D velocity inversion, is then applied for the velocity reconstruction where the amplitude information of the seismic data is fully preserved and utilized. The unified approach of surface‐consistent transmission preconditioning and velocity modelling is demonstrated on the Society of Exploration Geophysicists Advanced Modelling arid model dataset as well as on two complex field datasets containing near surface complexities typical of arid regions. The approach provides the solution of the near surface distortions by performing data preconditioning and velocity inversions in a unified scheme. The surface‐consistent full waveform inversion approach has been utilized for large land seismic projects and represents a robust tool for supporting land seismic imaging.

Removing abnormal environmental noise in nodal land seismic data using deep learning

In field seismic data, abnormal environmental noise from the acquisition environment often is unavoidable, and its characteristics are complex. Abnormal environmental noise has a strong amplitude (tens of thousands times the average seismic amplitudes) and masks the effective seismic signal. The traditional method for removing such abnormal environmental noise relies on energy scanning to identify the noise location. However, energy scanning cannot always accurately identify such noise due to the sudden energy change at the locations of first-breaks and surface waves. A deep learning method for removing abnormal environmental noise has been developed. The presence of environmental noise with large amplitude also results in an uneven energy distribution. The existing denoising networks rarely consider this situation. Based on the characteristics of environmental noise received by the nodal seismic acquisition system, a workflow for automatically generating the training data sets has been established. The network is built based on the Unet with the incorporation of a residual block. The Batchnorm layer is specifically omitted to adapt to the uneven energy distribution. Synthetic and field data testing have confirmed the effectiveness and applicability of the proposed method. The new method indicates better denoising performance and greater flexibility than the traditional method. Comparison between different networks also indicates that the new Residual Block Connected Unet is much more effective in denoising seismic data that contain abnormal environmental noise.

Full waveform inversion with combined misfit functions and application in land seismic data

Full waveform inversion reconstructs subsurface structures by matching the synthetic waveform to the observed waveform. Inaccuracy of the source wavelets can, thus, easily lead to an inaccurate model. Simultaneously updating source wavelets and model parameters is a conventionally used strategy. However, when the initial model is very far from the true model, cycle skipping exists, and estimating a reliable source wavelet is very difficult. We propose a combinatory inversion workflow based on seismic events. We apply a Gaussian time window around the first break and gradually increase its width to include more seismic events. The influence of inaccurate source wavelets is alleviated by applying a Gaussian time window around the first break to evaluate the normalized cross-correlation-based objective function. There are inevitable small model artifacts caused by inter-event interactions when calculating cross-correlations. As a result, we switch to the optimal transport function to clean the model and update the source wavelets simultaneously. The combinatory strategy has been applied to models with different types of geological structures. Starting from a crude initial model, we recovered a high-resolution and high-fidelity model and the source wavelets in two synthetic experiments. Finally, we apply our inversion strategy to a real-land seismic dataset in Southeast China and obtain a higher-resolution velocity model. By comparing an inversion velocity profile with well log information and the recorded data with the simulated data, we conclude that our inversion results for the field data are accurate and this new strategy is effective.

A minimalist approach for improved time imaging of a noisy vibroseis data: A case study from Jaisalmer Basin, Rajasthan

AbstractProcessing land seismic data, especially vibroseis data, is often challenging due to complicated near‐surface situations and source‐generated noise. The case study deals with noisy vibroseis data acquired in the Jaisalmer Basin. The near‐surface estimation in this area is difficult due to a possible velocity reversal manifested in the shingled patterns of the first breaks. The near‐surface workflow incorporates a model‐adaptive first break‐picking approach, essentially integrating two problems of first‐break‐picking and model estimation into a single problem. The signal‐conditioning workflow is based on cascaded scaling and single‐channel‐based noise reduction to prevent the removal of weak signals. Horizon‐based migration velocity analysis was used to focus reflectors on the constant velocity‐migrated stacks. This was particularly useful in areas with dubious velocity trends based on semblance panels. The velocity volume has structural consistency, which provides a better time‐migrated image. The workflow also incorporates a targeted post‐stack processing sequence to enhance continuity, sharpen discontinuities and improve the resolution, as notable by comparing the legacy‐processing results of the same dataset.

DIW-based reflection full waveform inversion and its application of land seismic data

Abstract The exploration zone in Northwest China poses unique challenges due to its complex structures, fractured blocks, deep reservoirs, and geological anomalies. Conventional velocity modeling and migration imaging methods face difficulties in accurately imaging the underlying reservoirs beneath abnormal rock formations, resulting in poor signal-to-noise ratio and resolution. Moreover, the seismic data quality often falls short, leading to unfocused images. To address these challenges, we propose a novel approach: dynamic image warping-based reflection full waveform inversion. This method combines a model regularization strategy and structural constraints to generate high-resolution velocity models. The application in the Taha Industrial Zone demonstrates that the proposed method effectively characterizes the velocity characteristics of special geological bodies, such as deep weathering crusts, improving imaging effects and resolution, which allows for a more precise understanding of the complex geological features.

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

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.”

An adaptive linear-mode decomposition for effective separation of linear and nonlinear seismic events, ground roll, and random noise

Ground roll and random noise usually mask primary reflections in land seismic data. Different sets of signal processing methods are used to suppress these two noises based on statistical and/or transformation filtering. Among these methods, linear-mode decomposition (LMD) decomposes linear and nonlinear seismic events into amplitude-frequency modulated modes using the Wiener filter. Different combinations of these decomposed linear modes then can be used to represent different seismic events. However, LMD requires predefining the level of decomposition that must be selected carefully to avoid suboptimal binning, which can influence the fidelity of the decomposed seismic modes. To that end, we introduce an adaptive LMD (ALMD) that optimally separates seismic events, ground roll, and random noise. ALMD uses the correlation between the decomposed modes and the input data to determine the decomposition level. Consequently, an optimum decomposition divides the data into linear modes with minimum mixing. In addition, unlike conventional ground roll suppression methods, ALMD does not require estimating the slope or the frequency bandwidth of the ground roll. Moreover, ALMD automates the random noise segregation by separating modes as the signal, noise, and mixed modes, based on the permutation entropy and kurtosis criteria. ALMD iteratively decomposes mixed modes with remnant random noise until a signal or noise criterion is met. Using synthetic and real data examples, we demonstrate that the proposed ALMD is an effective method for separating desired linear and nonlinear events, unwanted ground roll energy, and random noise from the seismic data.

Quantitative evaluation of 3D land acquisition geometries with arrays and single sensors: Closing the loop between acquisition and processing

The growing popularity of land nodes demands careful survey design practices to smoothly supersede cabled seismic acquisition with geophone arrays. Unfortunately, trace density is often used as a catchall proxy to describe survey quality, which is a gross oversimplification. We describe comprehensive and quantitative workflows focusing on final image quality for evaluating existing or new 3D land acquisition geometries with arrays and single sensors. They streamline the design process, remove human bias, and close the loop between acquisition and processing. A central element is a data-driven approach for deriving absolute signal-to-noise ratio (S/N) directly from the data. The resulting S/N volumes can be analyzed as cubes or slices or distilled to statistical quantities. We apply new workflows to three typical use cases from 3D land seismic data. First, we quantitatively contrast different 3D data sets acquired with various field acquisition geometries and understand which acquisition parameters are likely responsible for S/N differences. Second, we perform a realistic numerical feasibility study evaluating multiple 3D acquisition geometries with arrays and single sensors and assess their expected performance on a complex SEG Advanced Modeling Arid data set representative of the desert environment. For feasibility studies, complete automation can be achieved by applying migration in lieu of processing and data-driven S/N as evaluation steps. Finally, we show how to predict absolute S/N outcomes of new 3D acquisitions based on the existing legacy data with different acquisition geometry. We demonstrate the excellent predictive power of the analytical signal-strength estimate formula for both field and synthetic elastic data sets. Translating survey design into commonly spoken “image S/N language” improves communication between geoscientists and enables more effective decision-making.

Elastic vertically transversely isotropic full-waveform inversion: From synthetic to field data

Representing the earth as an elastic medium leads to a more accurate approximation of the seismic response than can be obtained by assuming an acoustic medium. However, this representation requires a larger number of unknown parameters to be introduced and a more complex seismogram to be considered. Consequently, it is important to carefully deal with two challenging issues, the impact of strong surface waves and the crosstalk among parameters, for land seismic multiparameter full-waveform inversion (FWI). Under the elastic vertically transversely isotropic assumption, a robust FWI approach is developed by combining the optimal transport (OT) misfit function, the Gaussian time window, and the suitable parameterization [Formula: see text]. Without specific preprocessing, surface waves usually dominate the inversion process, leaving the body waves not fully exploited. Correspondingly, the subsurface model is only updated at a shallow depth. The OT misfit function can balance the contributions of surface and body waves to model updates in an automatic manner. In addition, the Gaussian time window further improves the weights of the body waves during inversion. We efficiently exploit the entire seismogram with these two approaches. In this configuration, the performances of several parameterizations chosen from radiation pattern analysis are evaluated. Our inversion strategy is applied to the Middle East model and a field data application. In both cases, it outperforms other inversion strategies. The entire land seismic data are efficiently exploited and multiparameters are simultaneously reconstructed.

Application of full-waveform inversion to land data: Case studies in onshore Mexico

Velocity model building and imaging for land surveys are often challenging due to near-surface complexity contaminating the reflection signal. Incorporating full-waveform inversion (FWI) in the velocity model building workflow for land surveys offers benefits not achieved with traditional model building tools, but it also brings difficulties. We have developed an effective model building workflow for land seismic data that incorporates dynamic matching FWI (DMFWI). DMFWI employs an objective function that uses multidimensional local windowed crosscorrelations between the dynamically matched version of observed and synthetic data. Dynamic matching de-emphasizes the impact of amplitudes, allowing the algorithm to focus on using kinematic information for velocity updates. The proposed workflow produces a geologically plausible and consistent model for data acquired with limited offsets. Refraction and reflection tomography may also be included in the workflow. The workflow is applied to onshore surveys in Mexico. Despite challenges of the near-surface geology and limitations of the acquisition parameters in the study areas, the proposed model building workflow successfully derives a high-resolution velocity model that significantly improves the migrated depth image.