First-arrival Traveltime Research Articles

Acoustic full-waveform inversion on land surveys: A case study on the Qademah fault, Saudi Arabia

Geophysical methods have been used to map a Qademah fault near a residential area in western Saudi Arabia. However, the location and spatial extension of the fault zone are still uncertain due to the limited resolution and length of geophysical surveys. We apply seismic full-waveform inversion (FWI) to a land survey 2 km in length, the result of which reveals more details of the fault as well as the subsurface. Before inversion, we remove the surface waves, converted waves, and other elastic phenomena in the f- k domain and normalize the trace amplitude to compensate for attenuation, such that wavefield modeling for FWI can be approximated by acoustic propagators with constant density. Due to the large wavefield amplitudes near the source-receiver positions, the FWI velocity gradient presents a singular zone in the near surface, which is muted to stabilize the inversion. We also invert the short-offset traces before the larger-offset traces to avoid cycle skipping at the far offsets and obtain a reasonable data fit despite some mismatches in the near offsets. The final FWI velocity model shows the Qademah fault at approximately 1000 m distance as well as three low-velocity anomalies at 100 m depth, confirmed by first-arrival traveltime tomography. We also observe a potential low-velocity layer at 250–300 m depth, which was not reported in former studies. This model can also be used for geologic interpretation, hazard assessment, and paleoseismology studies of the local area.

Monitoring spatiotemporal evolution of fractures during hydraulic stimulations at the first EGS collab testbed using anisotropic elastic-waveform inversion

The EGS Collab project acquired continuous active-source seismic monitoring (CASSM) data before, during, and after hydraulic stimulations at the first testbed at the depth of 4850 ft (1478 m) at the Sanford Underground Research Facility in Lead, South Dakota, for monitoring fracture creation and evolution. CASSM acquisition was conducted using 24 hydrophones, 18 accelerometers, and 17 piezoelectric sources within four fracture-parallel wells and two orthogonal wells. 3D anisotropic traveltime tomography and anisotropic elastic-waveform inversion of the campaign cross-borehole seismic data show that the rock within the stimulation region is a heterogeneous horizontal transverse isotropic medium. We use these inversion results as the initial models and apply 3D anisotropic first-arrival traveltime tomography and 3D anisotropic elastic-waveform inversion to the CASSM data acquired after each stimulation in May, 2018 and December, 2018. We observe the spatiotemporal evolution of seismic velocities and anisotropic parameters caused by hydraulic fracture stimulations, showing the regions of rock alternation caused by hydraulic fracture stimulation.

Accurate Trace-Cut and Phase Alignment of Active Ocean-Bottom Seismometer Data

Abstract Accurate positions of ocean-bottom seismometers (OBSs) on the seafloor are critical parameters and can only be obtained by inversion modeling of first-arrival travel times of overhead cross-line airgun shootings. With an increased sampling interval of ≤20 ms for long-term earthquake studies, apparent artifacts affect the phase alignment of first arrivals on the seismic sections of trace-cut airgun shots. Our analysis shows that these apparent misalignments are caused by timing inconsistencies and inaccuracies during the trace-cut, which are so-called rounding errors. To eliminate these rounding errors, a simple interpolation is used to resample traces. Further analysis shows the simple interpolation satisfactorily retains the original waveform. The improved timing accuracy significantly reduces the uncertainty of seafloor locations as shown by Hadal OBS data.

Seismic Characterization of Subsurface Structures at Rock Valley, Nevada

ABSTRACT The Source Physics Experiment (SPE) aims at improving explosion monitoring techniques by investigating source characteristics of chemical explosions in geologic formations. One of the critical tasks in Rock Valley Direct Comparison (RV/DC), SPE phase III, is to prepare for the main experiment by characterizing the subsurface structures at the test site. Based on the seismic data acquired during an accelerated-weight-drop (AWD) seismic survey at Rock Valley, we first pick the P-wave first-arrival travel times and derive a P-wave velocity model using the adjoint-state first-arrival travel-time tomography. We then apply reverse-time migration to the processed seismic data and obtain high-resolution images of the subsurface structures along the two main survey lines. Our migration results show several reflectors corresponding to major geologic formation boundaries. We employ a multitask machine learning model to enhance the reverse-time migration images and identify faults from these images. We find that our automatically picked faults correlate well with the locations of known faults in the region in addition to many geologically undetected faults. Our subsurface characterization results refine our understanding of the geology in this region and provide valuable velocity and structural information for RV/DC geologic model building and fault identification.

Improving parsimonious refraction interferometry through U-Net-based correction of first-arrival traveltimes

Parsimonious refraction interferometry (PRI) is a useful technique for generating dense virtual traveltimes when the seismic survey is performed with sparse acquisition geometry. However, PRI has limitations in accurately calculating the traveltimes of direct and diving waves, leading to inaccurate velocity structures when applying first-arrival traveltime tomography (FATT). To address this issue, we develop an improved approach using a deep-learning network called U-Net. We first examine the feasibility of our algorithm analytically through the traveltime interpretation of a simple model. Then, the U-Net model is trained on various data sets to learn the relationship between PRI results and actual traveltimes. Subsequently, the trained network corrects the traveltime errors of the PRI results. As a result, we can obtain accurate first-arrival traveltimes using only two shot gathers without additional information such as infilled shots. Our technique enables virtual traveltime corrections, allowing for improved FATT results, even in cases for which dense shots are difficult to deploy. Numerical results demonstrate that our method can achieve accuracy comparable to picked traveltime data, indicating its high effectiveness in increasing the resolution of FATT results. Our approach maximizes the cost-saving benefits of PRI and can be advantageous in obtaining high-resolution FATT results when dense shot geometry is unavailable.

Near-surface imaging by joint inversion of ERT and seismic traveltime data with guided FCM clustering

We present a novel joint inversion strategy for electrical resistivity tomography (ERT) and seismic first-arrival traveltime data with guided fuzzy c-means (GFCM) clustering coupling to increase the accuracy of inversion results and improve the characterization of heterogeneous near-surface materials. The physical parameter correlation between the resistivity and seismic velocity is enforced using a GFCM clustering coupling term in the objective function, which also includes misfit and regularization terms. The physical property models estimated by joint inversion are guided by the clustering centers of a priori petrophysical parameters. A limited-memory quasi-Newton approach is used to optimize the objective function. To validate the proposed approach and describe its implementation in detail, tests were first performed using two synthetic models. The stratigraphic structures recovered by joint inversion with GFCM clustering coupling are more consistent with the true model than those inverted by individual inversion, cross-gradient and fuzzy c-means (FCM) joint inversion. Furthermore, the physical parameters estimated using GFCM joint inversion are significantly more accurate than those estimated using individual inversion and the other two joint inversion methods. The joint inversion algorithm was applied to field data from the Liangzhu site in Hangzhou, China. The depth and shape of the stratigraphic interface imaged by GFCM joint inversion are consistent with the drilling results. However, when using individual inversion and the other two joint inversion methods, the recovered stratigraphic structures have some differences with the drilling data. Therefore, GFCM joint inversion is an effective and reliable method for near-surface fine imaging.

First-Arrival Traveltime Tomography With Near-Surface Structural Regularization

First-Arrival Traveltime Tomography With Near-Surface Structural Regularization

Errata to “First-Arrival Traveltime Tomography With Near-Surface Structural Regularization”

Errata to “First-Arrival Traveltime Tomography With Near-Surface Structural Regularization”

Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints

Abstract Walkaway vertical seismic profiling (WVSP) is known for its high level of credibility in obtaining the relationship between first-arrival traveltime and detector depth through first-arrival picking. This relationship can be utilized for seismic velocity model inversion to improve velocity model accuracy and rationality. Here, we present a WVSP first-arrival velocity model with velocity structure constraints, by utilizing layer and fault data interpreted by ground seismic structure technique. We constructed a geological structure and layer sequence models based on sedimentary patterns to obtain complex velocity structures. Furthermore, we introduced a smooth regularization term based on the velocity structure into the inversion model to enhance its consistency with geological laws and compensate for the reduced inversion accuracy owing to the regularization term based on flat structures. This approach addressed the limitations of the regularization term based on flat structures, resulting in more accurate and reliable inversion results. Through simulation analysis, the proposed method realized the WVSP first-arrival velocity inversion, with results being closer to those of the real velocity model.

An adaptive finite-difference method for seismic traveltime modeling based on 3D eikonal equation

3D eikonal equation is a partial differential equation for the calculation of first-arrival traveltimes and has been widely applied in many scopes such as ray tracing, source localization, reflection migration, seismic monitoring and tomographic imaging. In recent years, many advanced methods have been developed to solve the 3D eikonal equation in heterogeneous media. However, there are still challenges for the stable and accurate calculation of first-arrival traveltimes in 3D strongly inhomogeneous media. In this paper, we propose an adaptive finite-difference (AFD) method to numerically solve the 3D eikonal equation. The novel method makes full use of the advantages of different local operators characterizing different seismic wave types to calculate factors and traveltimes, and then the most accurate factor and traveltime are adaptively selected for the convergent updating based on the Fermat principle. Combined with global fast sweeping describing seismic waves propagating along eight directions in 3D media, our novel method can achieve the robust calculation of first-arrival traveltimes with high precision at grid points either near source point or far away from source point even in a velocity model with large and sharp contrasts. Several numerical examples show the good performance of the AFD method, which will be beneficial to many scientific applications.

Seismic Characterization of a Landslide Complex: A Case History from Majes, Peru

Seismic characterization of landslides offers the potential for developing high-resolution models on subsurface shear-wave velocity profile. However, seismic methods based on reflection processing are challenging to apply in such scenarios as a consequence of the disturbance to the often well-defined structural and stratigraphic layering by the landslide process itself. We evaluate the use of alternative seismic characterization methods based on elastic full waveform inversion (E-FWI) to probe the subsurface of a landslide complex in Majes, southern Peru, where recent agricultural development and irrigation activities have altered the hydrology and groundwater table and are thought to have contributed to increased regional landslide activities that present continuing sustainability community development challenges. We apply E-FWI to a 2D near-surface seismic data set for the purpose of better understanding the subsurface in the vicinity of a recent landslide location. We use seismic first-arrival travel-time tomography to generate the inputs required for E-FWI to generate the final high-resolution 2D compressional- and shear-wave (P- and S-wave) velocity models. At distances greater than 140 m from the cliff, the inverted models show a predominantly vertically stratified velocity structure with a low-velocity near-surface layer between 5–15 m depth. At distances closer than 140 m from the cliff, though, the models exhibit significantly reduced shear-wave velocities, stronger heterogeneity, and localized shorter wavelength structure in the top 20 m. These observations are consistent with those expected for a recent landslide complex; however, follow-on geotechnical analysis is required to confirm these assertions. Overall, the E-FWI seismic approach may be helpful for future landslide characterization projects and, when augmented with additional geophysical and geotechnical analyses, may allow for improved understanding of the hydrogeophysical properties associated with suspected ground-water-driven landslide activity.

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.

High-precision and high-efficiency first-arrival slope tomography via eikonal solvers and the adjoint-state method

Abstract First-arrival slope tomography (FAST) introduces first-arrival slopes, corresponding to the horizontal components of the slowness vectors at the receiver and source positions to supplement first-arrival traveltime for better guiding ray propagation in the media until the best match is achieved with the observed data. FAST can recover the velocity model with higher resolution and precision than first-arrival traveltime tomography (FATT) but is computationally intensive. In this context, we propose an improved approach, referred to as high-precision and high-efficiency first-arrival slope tomography (HFAST). HFAST redefines one of the slopes using the reciprocity principle and simultaneously employs the first-arrival traveltime and slopes to ensure high-quality model building. On the other hand, HFAST extracts calculated data and derives the gradient of the misfit function from the solutions of relatively limited forward and inverse problems, resulting in a low computational cost. The cost of HFAST is proportional to the minimum between the receivers and sources, whereas the cost of FAST is scaled to the sum of the receivers and sources. Numerical experiments involving the checkerboard and SEAM II Foothill models demonstrate that HFAST can achieve a higher inversion precision than FATT, especially in the recovery of small-scale anomalies and the presence of velocity reversal. Moreover, HFAST is more computationally efficient than FAST and suitable for managing large data sets. Therefore, HFAST can be regarded as a valuable supplement to current first-arrival-based model building methods and has the potential to be applied in static corrections, prestack depth migration and waveform inversion in the future.

First-arrival traveltime tomography for monitoring the excavation damaged zone in the Horonobe Underground Research Laboratory

Subsurface excavation results in the formation of a zone called excavation damaged zone (EDZ) around the tunnel wall. An EDZ is a major concern in the field of high-level radioactive waste disposal because it may act as a flow path after the closure of a repository. In this study, first-arrival traveltime tomography was repeatedly conducted on the EDZ at a depth of 350 ​m in the Horonobe Underground Research Laboratory. However, the acquired data was highly affected by the support structure on the drift wall. For proper visualization of the EDZ, information about the structure was incorporated into the inversion by modifying the model constraint. The synthetic study showed that the approach reproduced the EDZ in the model without the artifacts. The method was applied to field data, and the EDZ around the drift was detected. The inversion was extended to a time-lapse inversion to trace the changes in P-wave velocity in the EDZ. The synthetic study demonstrated that temporal changes in the P-wave velocity distribution could be detected. Data obtained from 12 surveys under open-drift conditions were analyzed by time-lapse inversion. The results indicated that the EDZ did not undergo sealing or evolution at the site for approximately seven years.

Solving Geophysical Inversion Problems with Intractable Likelihoods: Linearized Gaussian Approximations Versus the Correlated Pseudo-marginal Method

A geophysical Bayesian inversion problem may target the posterior distribution of geological or hydrogeological parameters given geophysical data. To account for the scatter in the petrophysical relationship linking the target parameters to the geophysical properties, this study treats the intermediate geophysical properties as latent (unobservable) variables. To perform inversion in such a latent variable model, the intractable likelihood function of the (hydro)geological parameters given the geophysical data needs to be estimated. This can be achieved by approximation with a Gaussian probability density function based on local linearization of the geophysical forward operator, thereby, accounting for the noise in the petrophysical relationship by a corresponding addition to the data covariance matrix. The new approximate method is compared against the general correlated pseudo-marginal method, which estimates the likelihood by Monte Carlo averaging over samples of the latent variable. First, the performances of the two methods are tested on a synthetic test example, in which a multivariate Gaussian porosity field is inferred using crosshole ground-penetrating radar first-arrival travel times. For this example with rather small petrophysical uncertainty, the two methods provide near-identical estimates, while an inversion that ignores petrophysical uncertainty leads to biased estimates. The results of a sensitivity analysis are then used to suggest that the linearized Gaussian approach, while attractive due to its relative computational speed, suffers from a decreasing accuracy with increasing scatter in the petrophysical relationship. The computationally more expensive correlated pseudo-marginal method performs very well even for settings with high petrophysical uncertainty.

Building near-surface velocity models by integrating the first-arrival traveltime tomography and supervised deep learning

SUMMARY Accurate near-surface velocity models are necessary for land seismic imaging. First-arrival traveltime tomography (FTT) routinely used for estimating near-surface velocity models may fail in geological complex areas. Supervised deep learning (SDL) is capable of building accurate velocity models, based on tens of thousands of velocity model-shot gathers training pairs. It takes lots of time and memory space, which may be unaffordable for practical applications. We propose integrating the FTT and SDL to build near-surface velocity models. During the neural network training, the FTT-inverted models rather than the original seismic data are used as the network inputs and corresponding true models are the outputs. The FTT-inverted and true models are the same physical quantities and with the same dimensions. Their relationship is less non-linear than that between shot gathers and true models. Thus, the neural network of the proposed method can be trained well using only a small number of training samples, dramatically reducing the time and memory costs. Numerical tests demonstrate the feasibility and effectiveness of the proposed method. We applied the proposed method to a land data set obtained in mountainous areas in the west of China and obtained satisfactory near-surface velocity models and stacking images.

3D Near-surface Velocity Model Building by Integrating Surface, Borehole Seismic, and Well-logging Data in a Small-scale Testbed

This study aims to build a 3D velocity model for investigating and evaluating underground contaminants flowing through groundwater pathways. To accomplish this goal, we performed seismic exploration at a test site with fractured rock layers within a depth of 100 m. The acquired seismic data was then used to construct a three-dimensional (3D) P-wave velocity model of the test site. Although comprehensive geophysical exploration may be useful for investigating the near-surface structure, this paper focuses only on constructing a P-wave velocity model using the seismic method. The primary information of the model consists of extracted velocities from a two-dimensional surface, borehole first-arrival traveltime tomography results, and full waveform sonic log data. Since the test site has the spatial restriction of the survey line, we intended to improve the geological structure analysis results using various quantitative and qualitative analysis methods. First, to increase the reliability of velocity information, we performed a traveltime analysis on the zero vertical interval (ZVI) gather from the borehole seismic data. Then, we identified the qualitative information of the fracture zone's location by analyzing the amplitude variation of the ZVI gather. Moreover, we extracted structural information using the common reflection point gather from the borehole seismic data to supplement the obtained lithological information. However, the information for constructing a 3D P-wave velocity model was still insufficient due to the spatial constraints of the survey line and the limited depth of the borehole seismic survey. We filled this gap using the radial basis function interpolation method. We could verify the completed 3D P-wave velocity model by comparing it with core log data. Overall, the integrated interpretation of the final velocity model and the analysis results could provide a probable pathway that indicates hydraulic connectivity.

Seismic characterization of a fluid escape structure in the North Sea: the Scanner Pockmark complex area

SUMMARYSubsurface fluid escape structures are geological features which are commonly observed in sedimentary basins worldwide. Their identification and description have implications for various subsurface fluid flow applications, such as assuring integrity of overburden rocks to geological CO2 storage sites. In this study, we applied 3-D first-arrival traveltime tomography to a densely sampled wide-azimuth and wide-angle ocean bottom seismometer (OBS) data set collected over the Scanner Pockmark complex, a site of active gas venting in the North Sea. Seismic reflection data show a chimney structure underlying the Scanner Pockmark. The objective of this study was to characterize this chimney as a representative fluid escape structure in the North Sea. An area of 6$\times $6 km2 down to a depth of 2 km below sea level was investigated using a regularized tomography algorithm. In total, 182 069 manually picked traveltimes from 24 OBS were used. Our final velocity model contains compressional wave velocity perturbations ranging from −125 to +110 ms−1 relative to its average 1-D model and compares favourably with a coincident seismic reflection data set. The tomographic velocity model reveals that the chimney as observed in seismic reflection data is part of a larger complex fluid escape structure, and discriminates the genuine chimney from seismic artefacts. We find that part of the seeping gas migrates from a deep source, accumulates beneath the Crenulate Reflector unconformity at ∼250 m below seafloor (mbsf) before reaching the porous sediments of the Ling Bank and Coal Pit formation at <100 mbsf. In addition, the model shows that the venting gas at Scanner Pockmark is also being fed laterally through a narrow NW–SE shallow channel. Quantitative velocity analysis suggests a patchy gas saturation within the gas-charged sediments of the Ling Bank and the Coal Pit formations. Confined to the well-resolved regions, we estimate a base case average gas saturation of ∼9 per cent and in-situ gas volume of ∼1.64 $\times {10^6}\ {{\rm{m}}^3}$ across the Ling Bank and Coal Pit Fm. that can sustain the observed methane flux rate at the Scanner Pockmark for about 10 to 17 yr.

Seismic Imaging of Mineral Exploration Targets: Evaluation of Ray- vs. Wave-Equation-Based Pre-Stack Depth Migrations for Crooked 2D Profiles

Seismic imaging is now a well-established method in mineral exploration with many successful case studies. Seismic data are usually imaged in the time domain (post-stack or pre-stack time migration), but recently pre-stack depth imaging has shown clear advantages for irregular/sparse acquisitions and very complex targets. Here, we evaluate the effectiveness of both ray-based and wave-equation-based pre-stack depth imaging methodologies applied to crooked-line 2D seismic reflection profiles. Seismic data were acquired in the Kylylahti mining area in eastern Finland over severely folded, faulted and subvertical Kylylahti structure, and associated mineralization. We performed 3D ray-based imaging, i.e., industry-standard Kirchhoff migration and its improved version (coherency migration, CM), and wave-equation-based migration, i.e., reverse time migration (RTM) using a velocity model built from first-arrival traveltime tomography. Upon comparing the three different migrations against available geological data and models, it appeared that CM provided the least noisy and well-focused image, but failed to image the internal reflectivity of the Kylylahti formation. RTM was the only method that produced geologically plausible reflections inside the Kylylahti formation including a direct image of the previously known shallow massive sulfide mineralization.

Illumination compensation for the adjoint-state first-arrival traveltime tomography in VTI media

The sensitivity kernel of each parameter is angle-dependent in an anisotropic traveltime multiparameter inversion. To analyze the angle illumination in a transversely isotropic media with a vertical symmetry axis (VTI) by the adjoint state traveltime tomography (AST) method, a circular observation system is designed with receivers radially distributed at different propagated angles. Then, the nonuniform angular illumination in the gradient of different parameters in the VTI AST is revealed. The nonuniform angular illumination seriously hinders the simultaneous updates of multiple parameters toward the true solution and decreases the accuracy of the anisotropic multiparameter inversion. To overcome the illumination problem, a preconditioned AST method (PAST) is proposed by utilizing the diagonal elements of the approximate Hessian matrix as the angle illumination compensation for multiparameter gradients in the VTI medium. The PAST method not only compensates for angular illumination but also balances the ray density and eliminates gradient singular values, which improves the inversion accuracy for multiple parameters. The BP synthetic model test shows that the PAST method based on angular illumination compensation can invert the VTI multiparameter models more accurately. Therefore, the PAST inversion model can predict first-arrival traveltime information more accurately. Field data application in the East China Sea indicates that the inverted velocity of the proposed PAST fits well with the logging velocity, and the inverted anisotropic parameter agrees well with the Backus average values. Consequently, the image quality based on the PAST inverted models is slightly improved. It is proven that the proposed PAST method can better realize the near-surface multiparameter inversion of the VTI medium.