Inverted Velocity Model Research Articles

Love-Wave Phase-Velocity Estimation from Array-Based Rotational Motion Microtremor

ABSTRACTThe most popular array-based microtremor survey methods estimate velocity structures from the phase velocities of Rayleigh waves. Using the phase velocity of Love waves improves the resolution of inverted velocity models. In this study, we present a method to estimate the phase velocity of Love waves using rotational array data derived from the horizontal component of microtremors observed using an ordinal nested triangular array. We obtained discretized spatial derivatives from a first-order Taylor series expansion to calculate rotational motions from observed array seismograms. Rotational motions were obtained from a triangular subarray consisting of three receivers using discretized spatial derivatives. Four rotational-motion time histories were calculated from different triangular subarrays in the nested triangular arrays. Phase velocities were estimated from the array of the four rotational motions. We applied the proposed Love-wave phase-velocity estimation technique to observed array microtremor data obtained using a nested triangular array with radii of 25 and 50 m located at the Institute for Integrated Radiation and Nuclear Science, Kyoto University. The phase velocities of rotational and vertical motions were estimated from the observed data, and results showed that the former were smaller than those of the latter. The observed phase velocities obtained from vertical and rotational components agreed well with the theoretical Rayleigh- and Love-wave phase velocities calculated from the velocity structure model derived from nearby PS logs. To show the ability of the rotation to obtain Love wave, we estimated apparent phase velocities from north–south or east–west components. The apparent velocities resulted in between the theoretical velocities of Rayleigh and Love waves. This result indicates that the calculated rotation effectively derived the Love waves from a combination of Love and Rayleigh waves.

Efficient Wavefield Inversion With Outer Iterations and Total Variation Constraint

Full-waveform inversion (FWI) is popularly used to retrieve a high-resolution velocity model that maximizes the data fitting directly. It is a highly nonlinear optimization problem, and thus, FWI can easily fall into a local minimum. Wavefield reconstruction inversion (WRI) allows us to relax the wave equation constraint to provide a larger search space. However, it requires a high computational cost to update the velocity in each selected frequency through many expensive iterations. By recasting a linear optimization problem in terms of a modified source function (which includes the original source and secondary sources) and relying on the background velocity model, we end up with cheap inner iterations for inverting the wavefield. We refer to this setup as an efficient wavefield inversion (EWI). However, like WRI, EWI cannot mitigate the cycle-skipping problem completely when the background velocity model is far from the true one and low-frequency components in the data are missing. In this case, we propose to use additional outer iterations to better recover the velocity model. In the salt body inversion, we utilize a total variation (TV) regularization to constrain the inverted velocity model at each outer iteration. We demonstrate these features on a modified Marmousi model and a central part of the BP salt model. The application on a 2-D real data set also demonstrates the effectiveness of the proposed method.

Seismic inversion by Newtonian machine learning

We present a wave-equation inversion method that inverts skeletonized seismic data for the subsurface velocity model. The skeletonized representation of the seismic traces consists of the low-rank latent-space variables predicted by a well-trained autoencoder neural network. The input to the autoencoder consists of seismic traces, and the implicit function theorem is used to determine the Fréchet derivative, i.e., the perturbation of the skeletonized data with respect to the velocity perturbation. The gradient is computed by migrating the shifted observed traces weighted by the skeletonized data residual, and the final velocity model is the one that best predicts the observed latent-space parameters. We denote this as inversion by Newtonian machine learning (NML) because it inverts for the model parameters by combining the forward and backward modeling of Newtonian wave propagation with the dimensional reduction capability of machine learning. Empirical results suggest that inversion by NML can sometimes mitigate the cycle-skipping problem of conventional full-waveform inversion (FWI). Numerical tests with synthetic and field data demonstrate the success of NML inversion in recovering a low-wavenumber approximation to the subsurface velocity model. The advantage of this method over other skeletonized data methods is that no manual picking of important features is required because the skeletal data are automatically selected by the autoencoder. The disadvantage is that the inverted velocity model has less resolution compared with the FWI result, but it can serve as a good initial model for FWI. Our most significant contribution is that we provide a general framework for using wave-equation inversion to invert skeletal data generated by any type of neural network. In other words, we have combined the deterministic modeling of Newtonian physics and the pattern matching capabilities of machine learning to invert seismic data by NML.

Multiple joint wavefield inversions: Theory and field data implementations

Accurate velocity models for the near surface and overburden are needed for seismic processing and reliable depth imaging. Seismic with multiphysics data, well logs, and geology information need to be quantitatively integrated to obtain high-resolution velocity models. We detail our development and application of the joint wavefield inversion software platform, which enables flexible algorithmic schemes for the integration of multiparameter data and constraints. Inversion is performed in cascade or simultaneously using a variety of input data to constrain the velocity field reconstruction at multiple scales. Coupling mechanisms based on structure similarity together with rock-physics relations are optimally combined to boost resolution and enhance accuracy of the inverted velocity models. Ill-posed inversion problems are then solved using extensive geologic and rock-physics regularization instead of relying on smoothness constraints alone. We detail workflows and algorithms to guide the application of multiparameter joint inversion for velocity model building whether the input data are seismic traveltimes, electromagnetics (time/frequency domains), gravity, and/or surface waves. Extensive applications of multiparameter joint inversion are presented for a variety of complex geologic scenarios in which various multiparameter coupling strategies are illustrated. Robust velocity modeling and enhanced seismic imaging in time and depth domains are obtained as a result, proving the importance of multiphysics integration for reliable earth model parameter estimation.

Joint Inversion of High-Frequency Receiver Functions and Surface-Wave Dispersion: Case Study in the Parnaíba Basin of Northeast Brazil

ABSTRACTWe assess the performance of the joint inversion of receiver functions (RF) and surface-wave dispersion in the characterization of the sedimentary package comprising the Parnaíba basin. This procedure is routinely utilized in passive-source crustal studies to retrieve S-wave velocity variations with depth, and has seldom been used with higher-frequency datasets to investigate fine sedimentary structure. The Parnaíba basin is a Paleozoic cratonic basin composed of five supersequences, accumulating ∼3.5 km of sedimentary rocks interbedded by Late Cretaceous diabase sills. The dataset used for this research was acquired between 2015 and 2017 through deployment of 10 short-period and one broadband seismic stations distributed along an approximately 100-kilometer-long linear array in the center of the basin. The deployment was carried out under the Parnaíba Basin Analysis Project, a multi-institutional and multidisciplinary effort funded by BP Energy do Brasil. High-frequency RFs (f<4.8 Hz) were calculated from deconvolution of teleseismic P waveforms (30°<Δ<90°) after rotation into the great-circle path, whereas high-frequency dispersion curves (0.25–2 Hz) were obtained through multiple filter analysis of empirical Green’s functions developed from cross-correlation (ZZ component) and stacking (six months) of time–frequency-normalized ambient seismic noise recordings. S-wave velocity–depth profiles down to ∼5 km depth were developed through an iterative, linearized joint inversion approach. Comparison to independent active-source seismic profiles overlapping with our passive-source seismic line reveals the inverted velocity models successfully retrieve sedimentary thickness (top of the Cambrian), sedimentary velocity structure, and depth to the Cenozoic sedimentary sequence. In addition, high-velocity zones at depths ranging from 1.5 to 2.5 km are observed in the inverted velocity–depth profiles, which are interpreted as due to the Late Cretaceous sills interbedding the basin’s sedimentary rocks. The relative low cost of our approach makes it ideal for basic characterization of relatively unknown sedimentary basins.

Using a Time‐Based Subarray Method to Extract and Invert Noise‐Derived Body Waves at Long Beach, California

AbstractThe reconstruction of body waves from the cross‐correlation of random wavefields has recently emerged as a promising approach to probe the fine‐scale structure of the Earth. However, because of the nature of the ambient noise field, the retrieval of body waves from seismic noise recordings is highly challenging and has only been successful in a few cases. Here, we use seismic noise data from a 5,200‐node oil‐company survey to reconstruct body waves and determine the velocity structure beneath Long Beach, California. To isolate the body wave energy from the ambient noise field, we divide the entire survey into small‐aperture subarrays and apply a modified double‐beamforming scheme to enhance coherent arrivals within the cross‐correlated waveforms. The resulting beamed traces allow us to identify clear refracted P waves traveling between different subarray pairs, which we then use to construct a high‐resolution 3D velocity model of the region. The inverted velocity model reveals velocity variations of the order of 3% and strong lateral discontinuities caused by the presence of sharp geologic structures such as the Newport‐Inglewood fault (NIF). Additionally, we show that the resolution that is achieved through the use of high‐frequency body waves allows us to illuminate small geometric variations of the NIF that were previously unresolved with traditional passive imaging methods.

First arrivalQtomography based on an adjoint-state method

AbstractUnder the assumption of invariant ray path in a weakly dissipative (high quality factor Q) subsurface medium, a tomographic inversion approach composed of two cascading applications of first arrival traveltime and Q tomography is proposed for compensating amplitude loss caused by near-surface anomalies, such as unconsolidated soils or the overburden gas cloud. To improve the computational efficiency, these two related tomography methods were adopted with an adjoint-state technique. First, arrival traveltime tomography will be performed to provide an inverted velocity model as one of the inputs for the following first arrival Q tomography. Then, the synthetic first break generated by the inverted velocity model will be used as a stable guidance of accessing the scopes of first arrival waveforms in the time domain where the potential attenuated time information is contained. The attenuated time will be estimated through a logarithmic spectral ratio linear regression corresponding to frequency-dependent propagation responses of different wave types. All these estimated attenuated times will be applied with reference signals to generate synthetic attenuated seismic data in the time domain, and their discrepancies with real data will be evaluated using similarity coefficients. The ones with larger values will be selected as optimal attenuated time inputs for the following Q tomographic inversion. Examples of both synthetic and field data reveal the feasibility and potential of this method.

Estimation of velocity and borehole receiver location via full waveform inversion of vertical seismic profile data

ABSTRACTBecause a vertical seismic profile (VSP) survey includes the installation of geophones into a borehole, the results of data processing are influenced by the deviation error of the borehole trajectory. In particular, when full waveform inversion (FWI) is applied to VSP data, receiver location errors distort the results of velocity inversion. Under this circumstance, we attempt to estimate the exact locations of receivers and resolve the distortion of the inverted velocity model by adding receiver locations to the inversion parameters. This makes the FWI process a multi-parametric inverse problem. In this case, it is desirable to invert each parameter group alternatively. Because receiver coordinates depend on borehole trajectory, the inverse problem is regularised by adding a term measuring the curvature of the borehole trajectory gradient to obtain a physically meaningful solution. In addition, we propose a grid search method to improve the estimation of receiver coordinates. In the case of VSP data, the inversion of receiver coordinates using the grid search method is more efficient and robust than using a local descent method. Through numerical examples, we verify that the proposed inversion algorithm can improve FWI results and mitigate velocity distortion. We also demonstrate that the convergence of FWI can be improved by applying the grid search method to the estimation of receiver coordinates.

A Physics-Based Neural-Network Way to Perform Seismic Full Waveform Inversion

Seismic full waveform inversion is a common technique that is used in the investigation of subsurface geology. Its classic implementation involves forward modeling of seismic wavefield based on a certain type of wave equation, which reflects the physics nature of subsurface seismic wavefield propagation. However, obtaining a good inversion result using traditional seismic waveform inversion methods usually comes with a high computational cost. Recently, with the emerging popularity of deep learning techniques in various computer vision tasks, deep neural network (DNN) has demonstrated an impressive ability in dealing with complex nonlinear problems, including seismic velocity inversion. Now, extensive efforts have been made in developing a DNN architecture to tackle the problem of seismic velocity inversion, and promising results have been achieved. However, due to the dependence of a labeled dataset, i.e., the barely accessible true velocity model corresponding to real seismic data, the current supervised deep learning inversion framework may suffer from limitations on generalization. One possible solution to mitigate this issue is to impose the governing physics into this kind of purely data-driven method. Thus, following the procedures of traditional seismic full waveform inversion, we propose a seismic waveform inversion network, namely SWINet, based on wave-equation-based forward modeling network cells. By treating the single-shot observation data and its corresponding shot position as training data pairs, the inverted velocity model can be obtained as the trainable network parameters. Moreover, since the proposed seismic waveform inversion method is performed in a neural-network way, its implementation and inversion effect could benefit from some built-in tools in Pytorch, such as automatic differentiation, Adam optimizer and mini-batch strategy, etc. Numerical examples indicate that the SWINet method may possess great potential in resulting a good velocity inversion effect with relatively fast convergence and lower computation cost.

Crustal Structure of the Collision-Subduction Zone in South of Iran Using Virtual Seismometers

Improving the resolution of seismic tomography by adding virtual seismometers is an ambitious aim in regions with poor instrumental coverage. In this study, inter-event empirical Green’s functions (EGFs) were retrieved using cross-correlation of the vertical component of 630 earthquakes with M ≥ 4 which occurred around the collision-subduction transition zone in south Iran. To extract reliable inter-event EGFs and obtain stable tomographic results, we used about 1300 event pairs with good signal-to-noise ratio, each pair well aligned to a seismic station. Our results show that the retrieved inter-event EGFs agree well with those obtained from earthquakes in similar paths. The inverted velocity model presents two main layers including upper crust (up to ~16 km) and middle crust (deeper than ~18 km) in both sides of the Minab-Zendan-Palami transition zone. The upper crust contains two main layers: sedimentary and basement layers with thicknesses ~6 and ~10 km, respectively. Moreover, the main faults cause lateral variations in these main layers. The difference between the average velocities of the middle crust, between the collision and subduction zones, is about 0.5 km/s, delimited by faults. Also, an area with a 30 km width along these faults can be defined as the collision-subduction transition zone.

Passive seismic event estimation using multiscattering waveform inversion

Passive seismic monitoring has become an effective method to understand underground processes. Time-reversal-based methods are often used to locate passive seismic events directly. However, these kinds of methods are strongly dependent on the accuracy of the velocity model. Full-waveform inversion (FWI) has been used on passive seismic data to invert the velocity model and source image, simultaneously. However, waveform inversion of passive seismic data uses mainly the transmission energy, which results in poor illumination and low resolution. We developed a waveform inversion using multiscattered energy for passive seismic to extract more information from the data than conventional FWI. Using transmission wavepath information from single- and double-scattering, computed from a predicted scatterer field acting as secondary sources, our method provides better illumination of the velocity model than conventional FWI. Using a new objective function, we optimized the source image and velocity model, including multiscattered energy, simultaneously. Because we conducted our method in the frequency domain with a complex source function including spatial and wavelet information, we mitigate the uncertainties of the source wavelet and source origin time. Inversion results from the Marmousi model indicate that by taking advantage of multiscattered energy and starting from a reasonably acceptable frequency (a single source at 3 Hz and multiple sources at 5 Hz), our method yields better inverted velocity models and source images compared with conventional FWI.

Passive seismic full waveform inversion using reconstructed body-waves for subsurface velocity construction

ABSTRACTFull waveform inversion (FWI) using passive seismic data can use amplitude, phase and travel time information from the data simultaneously. However, at least three challenges are involved in passive seismic full waveform inversion (PSFWI): a low signal-to-noise ratio (SNR), source location uncertainty and an unknown source wavelet. In this study, we propose a method that combines seismic interferometry and a source-independent inversion algorithm to solve these problems. Using seismic interferometry, the original passive seismic data recorded on the surface can be reconstructed into new virtual source records that have a relatively high SNR and certain source location. The source-independent algorithm eliminates the influence of source wavelet error on the final inversion results. Through numerical tests, we discuss the effects of passive source number and recording time on the inversion results and find that increasing the source number or recording time can improve inversion quality. We extract the background velocity model from the results of PSFWI and use it as the initial model of active source FWI. Least square reverse time migration (LSRTM) is then conducted to verify the accuracy of the inverted velocity models. The final results demonstrate that our PSFWI method can construct accurate long-wavelength velocity structures for subsequent active source FWI. The velocity model constructed using our successive inversion strategy can improve the LSRTM results.

Addressing viscous effects in acoustic full-waveform inversion

In conventional full-waveform inversion (FWI), viscous effects are typically neglected, and this is likely to adversely affect the recovery of P-wave velocity. We have developed a strategy to mitigate viscous effects based on the use of matching filters with the aim of improving the performance of acoustic FWI. The approach requires an approximate estimate of the intrinsic attenuation model, and it is one to three times more expensive than conventional acoustic FWI. First, we perform 2D synthetic tests to study the impact of viscoacoustic effects on the recorded wavefield and analyze how that affects the recovered velocity models after acoustic FWI. Then, we apply the current method on the generated data and determine that it mitigates viscous effects successfully even in the presence of noise. We find that having an approximate estimate for intrinsic attenuation, even when these effects are strong, leads to improvements in resolution and a more accurate recovery of the P-wave velocity. Then, we implement and develop our method on a 2D field data set using Gabor transforms to obtain an approximate intrinsic attenuation model and inversion frequencies of up to 24 Hz. The analysis of the results indicates that there is an improvement in terms of resolution and continuity of the layers on the recovered P-wave velocity model, leading to an improved flattening of gathers and a closer match of the inverted velocity model with the migrated seismic data.

Results of the downhole microseismic monitoring at a pilot hydraulic fracturing site in Poland — Part 1: Event location and stimulation performance

A precise velocity model is necessary to obtain reliable locations of microseismic events, which provide information about the effectiveness of the hydraulic stimulation. Seismic anisotropy plays an important role in microseismic event location by imposing the dependency between wave velocities and its propagation direction. Building an anisotropic velocity model that accounts for that effect allows for more accurate location of microseismic events. We have used downhole microseismic records from a pilot hydraulic fracturing experiment in Lower-Paleozoic shale gas play in the Baltic Basin, Northern Poland, to obtain accurate microseismic events locations. We have developed a workflow for a vertical transverse isotropy velocity model construction when facing a challenging absence of horizontally polarized S-waves in perforation shot data, which carry information about Thomsen’s [Formula: see text] parameter and provide valuable constraints for locating microseismic events. We extract effective [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text] for each layer from the P- and SV-wave arrivals of perforation shots, whereas the unresolved [Formula: see text] is retrieved afterward from the SH-SV-wave delay time of selected microseismic events. An inverted velocity model provides more reliable location of microseismic events, which then becomes an essential input for evaluating the hydraulic stimulation job effectiveness in the geomechanical context. We evaluate the influence of the preexisting fracture sets and obliquity between the borehole trajectory and principal horizontal stress direction on the hydraulic treatment performance. The fracturing fluid migrates to previously fractured zones, while the growth of the microseismic volume in consecutive stages is caused by increased penetration of the above-lying lithologic formations.

A Probabilistic Shear Wave Velocity Model of the Crust in the Central West Australian Craton Constrained by Transdimensional Inversion of Ambient Noise Dispersion

AbstractThe Capricorn Orogen in central Western Australia played important roles in initializing and finalizing the West Australian craton. Surface geological mapping and isotopic studies show that the crust has recorded over a billion years of tectonic history spanning from its crustal formation in the Archean to episodes of tectonothermal events during the Proterozoic cratonization processes. The region therefore provides us with an ideal laboratory to characterize the seismic signature associated with tectonic processes. We constructed a crustal shear wave velocity model of the core region of the orogen, the Glenburgh Terrane and its north boundary, by inverting the array group velocity dispersion data measured from a high‐density temporary array. A modified Bayesian transdimensional tomography technique, which incorporates a smooth‐varying regional reference velocity model and Moho topography, was used to invert for the crustal velocity variations. The inverted velocity model adds great detail to the intracrustal structure and provides complementary seismic velocity information to refine the regional tectonic processes. Distinct patterns in the velocity structure support that the Glenburgh Terrane is an Archean microcontinent and favor the role of Paleoproterozoic subductions/accretions during the assembly of the West Australian Craton.

Microseismic velocity model inversion and source location: The use of neighborhood algorithm and master station method

The accuracy of the velocity model strongly affects the accuracy of microseismic source location and hence the reliability of fracture imaging. We have developed a systematic methodology for microseismic velocity model inversion and source location. A new misfit function is used for both problems, which yields more reliable result than the conventional ones. Using the same measure of misfit, the location errors resulting from the use of different misfit functions are eliminated. The neighborhood algorithm and master station method (MSM) are adopted for calculating the velocity model and source location, respectively. The reason for using the neighborhood algorithm is that it has fewer tuning parameters and is easy to be tuned, whereas the advantage of the MSM is that it can automatically remove the mispicks. The performance of the proposed methods is illustrated using the ball-hit events with known locations, and the validity of the inversion results is verified by the relocations of these events. We also used the inverted velocity models to locate the microseismic events detected from the monitoring data. The location result indicates that the fractures have an average half-length of 280 m and height of 55 m and the fracture azimuth is approximately N77°W.

Full waveform inversion using a decomposed single frequency component from a spectrogram

Although many full waveform inversion methods have been developed to construct velocity models of subsurface, various approaches have been presented to obtain an inversion result with long-wavelength features even though seismic data lacking low-frequency components were used. In this study, a new full waveform inversion algorithm was proposed to recover a long-wavelength velocity model that reflects the inherent characteristics of each frequency component of seismic data using a single-frequency component decomposed from the spectrogram. We utilized the wavelet transform method to obtain the spectrogram, and the decomposed signal from the spectrogram was used as transformed data. The Gauss–Newton method with the diagonal elements of an approximate Hessian matrix was used to update the model parameters at each iteration. Based on the results of time–frequency analysis in the spectrogram, numerical tests with some decomposed frequency components were performed using a modified SEG/EAGE salt dome (A–A′) line to demonstrate the feasibility of the proposed inversion algorithm. This demonstrated that a reasonable inverted velocity model with long-wavelength structures can be obtained using a single frequency component. It was also confirmed that when strong noise occurs in part of the frequency band, it is feasible to obtain a long-wavelength velocity model from the noise data with a frequency component that is less affected by the noise. Finally, it was confirmed that the results obtained from the spectrogram inversion can be used as an initial velocity model in conventional inversion methods.

A multi-frequency receiver function inversion approach for crustal velocity structure

In order to constrain the crustal velocity structures better, we developed a new nonlinear inversion approach based on multi-frequency receiver function waveforms. With the global optimizing algorithm of Differential Evolution (DE), low-frequency receiver function waveforms can primarily constrain large-scale velocity structures, while high-frequency receiver function waveforms show the advantages in recovering small-scale velocity structures. Based on the synthetic tests with multi-frequency receiver function waveforms, the proposed approach can constrain both long- and short-wavelength characteristics of the crustal velocity structures simultaneously. Inversions with real data are also conducted for the seismic stations of KMNB in southeast China and HYB in Indian continent, where crustal structures have been well studied by former researchers. Comparisons of inverted velocity models from previous and our studies suggest good consistency, but better waveform fitness with fewer model parameters are achieved by our proposed approach. Comprehensive tests with synthetic and real data suggest that the proposed inversion approach with multi-frequency receiver function is effective and robust in inverting the crustal velocity structures.

Integrated interpretation of 2D ground-penetrating radar, P-, and S-wave velocity models in terms of petrophysical properties: Assessing uncertainties related to data inversion and petrophysical relations

Near-surface geophysical techniques are extensively used in a variety of engineering, environmental, geologic, and hydrologic applications. While many of these applications ask for detailed, quantitative models of selected material properties, geophysical data are increasingly used to estimate such target properties. Typically, this estimation procedure relies on a two-step workflow including (1) the inversion of geophysical data and (2) the petrophysical translation of the inverted parameter models into the target properties. Standard deterministic implementations of such a quantitative interpretation result in a single best-estimate model, often without considering and propagating the uncertainties related to the two steps. We address this problem by using a rather novel, particle-swarm-based global joint strategy for data inversion and by implementing Monte Carlo procedures for petrophysical property estimation. We apply our proposed workflow to crosshole ground-penetrating radar, P-, and S-wave data sets collected at a well-constrained test site for a detailed geotechnical characterization of unconsolidated sands. For joint traveltime inversion, the chosen global approach results in ensembles of acceptable velocity models, which are analyzed to appraise inversion-related uncertainties. Subsequently, the entire ensembles of inverted velocity models are considered to estimate selected petrophysical properties including porosity, bulk density, and elastic moduli via well-established petrophysical relations implemented in a Monte Carlo framework. Our results illustrate the potential benefit of such an advanced interpretation strategy; i.e., the proposed workflow allows to study how uncertainties propagate into the finally estimated property models, while concurrently we are able to study the impact of uncertainties in the used petrophysical relations (e.g., the influence of uncertain, user-specified parameters). We conclude that such statistical approaches for the quantitative interpretation of geophysical data can be easily extended and adapted to other applications and geophysical methods and might be an important step toward increasing the popularity and acceptance of geophysical tools in engineering practice.

An application of full-waveform inversion to land data using the pseudo-Hessian matrix

Full-waveform inversion (FWI) is used to estimate the near-surface velocity field by minimizing the difference between synthetic and observed data iteratively. We apply this method to a data set collected on land. A multiscale strategy is used to overcome the local minima problem and the cycle-skipping phenomenon. Another obstacle in this application is the slow convergence rate. The inverse Hessian can enhance the poorly blurred gradient in FWI, but obtaining the full Hessian matrix needs intensive computation cost; thus, we have developed an efficient method aimed at the pseudo-Hessian in the time domain. The gradient in our FWI workflow is preconditioned with the obtained pseudo-Hessian and a synthetic example verifies its effectiveness in reducing computational cost. We then apply the workflow on the land data set, and the inverted velocity model is better resolved compared with traveltime tomography. The image and angle gathers we get from the inversion result indicate more detailed information of subsurface structures, which will contribute to the subsequent seismic interpretation.