Seismic Full Waveform Inversion Research Articles

Machine learning-based time-lapse 1D seismic full-waveform inversion with efficient training data generation in a carbon capture and storage monitoring

Machine learning (ML)-based time-lapse and seismic full-waveform inversion has recently gained attention in carbon capture and storage (CCS) research because of its potential to overcome the limitations of theory-based inversion and its excellent imaging performance for CO2 plumes. We propose a ML-based time-lapse 1D inversion method with efficient training data generation. The proposed method exploits time-lapse seismic data to infer time-lapse velocity changes and uses a 1D inversion strategy to enable the UNet3+ model to learn the general characteristics of velocity changes between baseline and monitoring phases. Our proposed method quickly generates various 1D velocity models and rapidly provides numerous near-offset data by using 1D ray-based modeling method. This allows us to overcome a challenge in constructing of 2D velocity models based on limited geological assumptions and forward modeling to generate seismic gathers, which requires massive computational resources. However, because 2D effects such as diffraction caused by the CO2 plume only affect the monitoring data, inaccurate predictions in the 1D inversion occurred near the plume, following the diffraction trajectory. We corrected this effect using a diffraction removal process for 2D target data, thereby improving the prediction accuracy of the ML model. The ML model was trained with a large amount of training data generated using the proposed method and validated on 2D target data with three different CO2 injection plume shapes obtained from a modified Marmousi2 velocity model. As a result, information about the CO2 plume was reasonably delineated, although the magnitude of the time-lapse velocity change near the plume boundary was reduced by small amplitude losses during the diffraction removal process. Our results suggest that the proposed method is a powerful tool for CCS monitoring, which requires forward modeling for each iterative survey.

Multiparameter shallow-seismic waveform inversion based on the Jensen–Shannon divergence

SUMMARY Seismic full-waveform inversion (FWI) or waveform inversion (WI) has gained extensive attention as a cutting-edge imaging method, which is expected to reveal the high-resolution images of complex geological structures. In this paper, we regard each 1-D signal in the inversion system as a 1-D probability distribution, then use the Jensen–Shannon divergence from information theory to measure the discrepancy between the predicted and observed signals, and finally implement a novel 2-D multiparameter shallow-seismic WI (MSWI). Essentially, the novel approach achieves an implicit weighting along the time-axis for each 1-D adjoint source defined by the classical WI (CWI), thus enhancing the extra illumination for a deeper medium compared with the CWI. By evaluating the inversion results of the two-layer model and fault model, the reconstruction accuracy for S-wave velocity and density of the new method is increased by about 30 and 20 per cent compared with that of the CWI under the same conditions, respectively. The reconstruction performance for P-wave velocity of these two methods is almost equal. In addition, the new 2-D MSWI is also resilient to white Gaussian noise in the data. Numerically, the inversion system has almost the strongest sensitivities to the S-wave velocity and density, performing the poorest sensitivity to the P-wave velocity. Finally, we test the novel method with a detection case for a power tunnel.

Bayesian variational time-lapse full waveform inversion

SUMMARY Time-lapse seismic full-waveform inversion (FWI) provides estimates of dynamic changes in the Earth’s subsurface by performing multiple seismic surveys at different times. Since FWI problems are highly non-linear and non-unique, it is important to quantify uncertainties in such estimates to allow robust decision making based on the results. Markov chain Monte Carlo (McMC) methods have been used for this purpose, but due to their high computational cost, those studies often require a pre-existing accurate baseline model and estimates of the locations of potential velocity changes, and neglect uncertainty in the baseline velocity model. Such detailed and accurate prior information is not always available in practice. In this study we use an efficient optimization method called stochastic Stein variational gradient descent (sSVGD) to solve time-lapse FWI problems without assuming such prior knowledge, and to estimate uncertainty both in the baseline velocity model and the velocity change over time. We test two Bayesian strategies: separate Bayesian inversions for each seismic survey, and a single joint inversion for baseline and repeat surveys, and compare the methods with standard linearized double difference inversion. The results demonstrate that all three methods can produce accurate velocity change estimates in the case of having fixed (exactly repeatable) acquisition geometries. However, the two Bayesian methods generate significantly more accurate results when acquisition geometries changes between surveys. Furthermore, joint inversion provides the most accurate velocity change and uncertainty estimates in all cases tested. We therefore conclude that Bayesian time-lapse inversion using a joint inversion strategy may be useful to image and monitor subsurface changes, in particular where variations in the results would lead to different consequent decisions.

Study Investigates Seismic Monitoring for Carbon Storage Leak Detection

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 22980, “Carbon Storage Leak Detection Through Seismic FWI and RTM: Different Survey Analyses,” by Sajjad Amani, Kyoto University. The paper has not been peer reviewed. Copyright 2023 International Petroleum Technology Conference. Reproduced by permission. _ In the complete paper, marine seismic data processing is investigated as a tool for monitoring possible leakages in geological carbon storage. Because of the great importance of storage permanence, a precise leakage-monitoring strategy is crucial. The proficiency of seismic monitoring solutions for leakage monitoring can be affected by shallower layers as a result of structure, seismic wave attenuation, and leak size. The authors explore two popular seismic monitoring methods used in this application in different scenarios: full waveform inversion (FWI) and reverse-time migration (RTM). Introduction Among the various carbon capture and storage (CCS) options, underground storage in saline aquifers is the best-understood solution. To assure storage consistency and permanence, finding the best strategy to precisely detect possible carbon leaks is essential. A perfect method must demonstrate the difference between stored CO2 and injected CO2 to detect potential fast- and slow-leakage areas. The seismic monitoring technique is the most efficient approach in this respect. Two popular tools for seismic monitoring are FWI and RTM. Several studies of their use in this application have been conducted. However, previous research did not analyze different survey arrays for carbon-leak detection using FWI and RTM. The current research aims to investigate quantitative aspects of CCS monitoring to conduct sensitivity analysis of the three different survey arrays [vertical seismic profile (VSP), crosswell, and surface] for different amounts of CO2-storage leakage in a saline aquifer reservoir. The capability of seismic-imaging methods for small amounts of leakage was tested. Comparison of these three arrays using monitoring methods such as RTM and FWI reveals their pros and cons in providing detailed information about the reservoir. In this research, a simple synthetic model was built that closely fits actual reservoirs characterized by suitable physical features such as velocity and density. Subsequently, elastic wave propagation simulation was implemented by use of a finite-difference scheme over a physical model of the reservoir. Then, FWI was applied to enhance the accuracy of the model parameters. With an efficient forward-modeling and inversion scheme, RTM was used as a powerful imaging tool that provides final high-resolution results for monitoring CO2 migration and possible leakage. This process examined the synthetic model for different amounts of CO2 leakage in saline aquifers to evaluate the performance of CO2 leakage monitoring using the seismic method. The results of the different receiver and source arrays also were compared to establish their effectiveness.

On the numerical resolution of the second-order viscoacoustic and viscoelastic anisotropic wave equations using the recursive convolution method

Accurate and efficient seismic wave modeling is fundamental for the high-resolution seismic full-waveform inversion and correct interpretation of seismic data. Based on the viscoelastic mechanism of the Generalized Standard Linear Solid and the constitutive relationship between stress and strain, we demonstrate a generalized recursive formula using the Taylor series expansion to directly compute the temporal convolution in the constitutive equation, rather than solving auxiliary differential equations of the memory variables in the conventional method. New sets of the second order viscoacoustic and viscoelastic anisotropic wave equations are established with the new recursive temporal convolutions, which completely replaces the traditional auxiliary differential equations of the memory variables. The error analysis shows the proposed method can offer better accuracy than the existing recursive methods, and is very competitive with the widely used memory variable method. The numerical results comparing the different methods show the accuracy and feasibility of the generalized recursive convolution method to simulate the viscoacoustic and viscoelastic anisotropic waves.

An Augmented Lagrangian Method-Based Deep Iterative Unrolling Network for Seismic Full-Waveform Inversion

An Augmented Lagrangian Method-Based Deep Iterative Unrolling Network for Seismic Full-Waveform Inversion

Subsoil density field reconstruction through 3-D FWI: a systematic comparison between vertical- and horizontal-force seismic sources

SUMMARY Bulk-density (ρ) of soil is an important indicator of soil compaction and type. A knowledge of the spatial variability of in situ soil density is important in geotechnical engineering, hydrology and agriculture. Surface geophysical methods have so far shown limited success in providing an accurate and high-resolution image of 3-D soil-density distribution. In this pursuit, 3-D seismic full-waveform inversion (FWI) is promising, provided the robustness and accuracy of density inversion via this approach can be established in the near-surface scale. However, simultaneous reconstruction of ρ and seismic wave velocities through multiparameter FWI remains a challenging task. Near-surface seismic data are commonly dominated by dispersive surface waves whose velocities are controlled by the value and distribution of shear-wave velocity (VS). One major difficulty in estimating reliably ρ from near-surface seismic data is due to the relatively low sensitivity of the seismic wavefield to ρ compared to seismic velocities. Additionally, the accuracy of the estimated ρ decreases due to error in VS—an issue known as parameter coupling. Parameter coupling makes it difficult to estimate accurately ρ within the framework of conventional gradient-based FWI. More sophisticated optimization approaches (e.g. truncated Newton) can reduce the effect of parameter coupling, but these approaches are commonly not affordable in near-surface applications due to heavy computational burden. In this research, we have investigated how choosing correctly the force direction of the seismic source can contribute to a higher accuracy of ρ estimates through 3-D FWI. Using scattered wavefields, the Hessian, and inversion tests, an in-depth and systematic investigation of data sets corresponding to different force directions has been carried out. A comparison of the scattered wavefields due to a point-localized ρ perturbation for different force directions shows the robustness of the horizontal-force data set to noise compared to the vertical-force data set. Furthermore, for a point-scatterer model, an analysis of the gradients of the misfit function using the Hessian shows that utilizing a horizontal-force source enables one to reconstruct the high-resolution gradient with relatively small parameter coupling. Finally, inversion tests for two different subsoil models demonstrate that 3-D FWI on a horizontal-force-source seismic data set is capable of providing a more accurate 3-D ρ distribution in soil compared to a vertical-force-source data set. Our results show that the use of a horizontal-force source might allow avoiding computationally demanding, costly optimization approaches in 3-D FWI.

Seismic Full Waveform Inversion Accelerated by Overlapping Data Input and Computation

Seismic full waveform inversion (FWI) is a powerful technology to obtain high-precision and high-resolution images of subsurface structures. However, FWI is a data-intensive algorithm that needs to read extensive seismic data from disks, which significantly affects its performance. We proposed a portable parallel framework to improve FWI by overlapping data input and computation (ODIC). The framework is based on POSIX threads (Pthreads), which is a standard thread API library and can create a parent thread and a child thread in the FWI process. The former is used to perform computation and the latter to read data from disks, both running simultaneously. This framework has two attractive features. First, it is broadly applicable; it can run on almost any computer from a laptop to a supercomputer. Second, it is easy to implement; it can be readily applied to existing FWI programs. A 3D FWI example shows that the framework speeds up FWI considerably.

A novel seismic full waveform inversion approach for assessing the internal structure of a medieval sea dike

AbstractCoastal protection in the form of dike constructions has a long history at the German North Frisian coast dating back to the High Middle Ages. As the vast majority of the dikes built prior to the devastating storm surges of the Middle Ages have been irretrievably destroyed, mostly sparse remains and only a few well preserved of these medieval dikes are found along the German North Frisian coast and within the Wadden Sea. Not all details of their construction and dimensions are yet understood. In the present case study, we investigate the historical Schardeich on the island of Pellworm in the German North Sea in a noninvasive way using shear waves (SH‐waves). For the data interpretation, we applied a combination of seismic full waveform inversion and classical seismic reflection imaging to determine the interior structure of the dike and its underlying layers at the highest possible resolution. The results obtained on land are compared with dike remains found in the tidal flats. These remains show up in marine seismic sections as characteristic reflections, which probably represent a compaction layer caused by the load of the former dike. For ground truthing, we compare the seismic results with internal dike structures found in nearby excavations. The comparison highlights that FWI is a reliable tool for near‐surface archaeological prospecting. We find that SH‐wave FWI provides decimetre‐scale velocity and density models that allow, together with the seismic reflection section, to determine distinct construction phases of the dike. The investigated dike further shows a depression at base level of about 0.75 m, which is of the same order as observed for the dike base reflections in the tidal flats. Transferring these findings to the dike remains mapped in the tidal flats, we derive a height of the former dike from 2.2 to 4.4 m.

Borehole acoustic full-waveform inversion

Full-waveform inversion (FWI) is a technique that has the potential for building high-resolution elastic velocity models. We apply this technique to wireline monopole acoustic logging data to image the near-wellbore formation velocity structures, which can be used in a fluid intrusion evaluation. Our FWI workflow is established in cylindrical coordinates instead of Cartesian coordinates to adapt to the borehole geometry. Assuming that the acoustic logging tool is centralized and formation properties are azimuthally invariant, we can simplify the problem to two dimensions in the inversion. Synthetic tests find that the high-resolution P- and S-wave velocity model around the borehole could be successfully inverted using FWI once a reasonable starting velocity model is given. However, borehole FWI differs from seismic FWI in that strong borehole-guided waves exist near the borehole wall due to the elastic effects. The borehole-guided waves have little sensitivity to velocity away from the borehole wall. In addition, it is difficult to simultaneously match the waveform of the body waves and guided waves in synthetic data and field data. Therefore, for field data applications, we propose to obtain the formation velocity structures around the borehole by FWI using only the first arrived P waves. The field data tests find that the proposed method is applicable for inverting the P-wave velocity structure around the borehole with a similar resolution compared with ray-tracing tomography. FWI may achieve higher resolution in field applications in the future with better assumptions made in the forward modeling and using more information in the field data.

Characterising ice slabs in firn using seismic full waveform inversion, a sensitivity study

AbstractThe density structure of firn has implications for hydrological and climate modelling, and ice-shelf stability. The structure of firn can be evaluated from depth models of seismic velocity, widely obtained with Herglotz–Wiechert inversion (HWI), an approach that considers the slowness of refracted seismic arrivals. However, HWI is strictly appropriate only for steady-state firn profiles and the inversion accuracy can be compromised where firn contains ice layers. In these cases, full waveform inversion (FWI) may yield more success than HWI. FWI extends HWI capabilities by considering the full seismic waveform and incorporates reflected arrivals. Using synthetic firn density profiles, assuming both steady- and non-steady-state accumulation, we show that FWI outperforms HWI for detecting ice slab boundaries (5–80 m thick, 5–80 m deep) and velocity anomalies within firn. FWI can detect slabs thicker than one wavelength (here, 20 m, assuming a maximum frequency of 60 Hz) but requires the starting velocity model to be accurate to ±2.5%. We recommend for field practice that the shallowest layers of velocity models are constrained with ground-truth data. Nonetheless, FWI shows advantages over established methods, and should be considered when the characterisation of firn ice slabs is the goal of the seismic survey.

Seismic full-waveform inversion based on superresolution for high-precision prediction of reservoir parameters

The frequency band limitation of seismic data limits the resolution of full-waveform inversion (FWI) results. In addition, the high computational cost seriously affects the practical application of FWI. To alleviate these concerns, an FWI method based on superresolution (SR-FWI) is developed to improve the prediction efficiency and accuracy of the reservoir parameters. A channel attention mechanism is introduced for the multifrequency characteristics of the model images. A constrained residual channel attention network (CRCAN) is built for superresolution (SR) by adding structural constraints to the loss function of a deep learning network. A total of 65,000 sets of geologic models and natural images constitute the network training data, 90% of which are used for training with the rest used for testing. The iterative calculation for FWI is time-consuming; hence, SR is applied to the iterative process to reduce the number of iterations and accelerate the model update. Low-resolution images along with the synthetic and field data are used for the evaluation of the CRCAN and SR-FWI algorithms, respectively. The test results find that CRCAN can effectively improve the image resolution, whereas SR-FWI is beneficial due to its high efficiency and precision, especially in predicting the stratum edge and small-scale anomalies. Therefore, SR-FWI is a powerful means of reservoir static and dynamic detection and can provide high-resolution information for projects, such as resource development and CO2 storage.

Implicit Seismic Full Waveform Inversion With Deep Neural Representation

AbstractFull waveform inversion (FWI) is arguably the current state‐of‐the‐art amongst methodologies for imaging subsurface structures and physical parameters with seismic data; however, important challenges are faced in its implementation and use. Keys amongst these are (a) building a suitable initial model, from which a local minimum is unlikely to be reached, and (b) availability of tools for evaluation of uncertainty. An algorithm we refer to as implicit full waveform inversion (IFWI), designed using continuously and implicitly defined deep neural representations, appears in principle to address both of these issues. We observe in IFWI, with its random initialization and deep learning optimization, improved convergence relative to standard FWI model initialization and optimization. Models close to the global minimum, capturing relatively high‐resolution subsurface structures, are obtained. In addition, uncertainty analysis, though not solved in IFWI, is meaningfully addressed by approximating Bayesian inference with the addition of dropout neurons. Numerical experimentation with a range of 2D geological models is suggestive that IFWI exhibits a strong capacity for generalization, and is likely well‐suited for multi‐scale joint geophysical inversion.

Full Waveform Inversion Using a High-Dimensional Local-Coherence Misfit Function

Conventional full-waveform seismic inversion (FWI) tries to estimate a subsurface model that can accurately predict surface records by minimizing an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm misfit between observed and synthetic data. If the initial model is far away from the true model, the cycle-skipping issue might occur and the L2-norm based FWI produces a spurious model. To mitigate this problem, we present a novel FWI scheme using a high-dimensional local coherence misfit function. A 2D/3D window is first used to extract local seismic waveform from the common-shot gathers. Then, we apply a normalized cross-correlation to measure the coherence of local synthetic and observed records, which is used as the misfit to iteratively update subsurface velocity model. The new misfit function enhances the contribution of phase fitting while reducing the amplitude contribution, which helps to increase the tolerance of FWI to an inaccurate initial velocity model. In addition, the computation of local waveform coherence along the temporal and spatial axes can adaptively balance the adjoint source amplitudes for strong near-offset reflections and weak far-offset refractions, which improves the low-wavenumber updates. Numerical experiments for synthetic and field data demonstrate that the proposed FWI scheme has a better tolerance to inaccurate starting models and is less sensitive to cycle-skipping issue compared with conventional FWI method.

Joint FWI of Active Source Data and Passive Virtual Source Data Reconstructed Using an Improved Multidimensional Deconvolution

Traditional full waveform inversion (FWI) highly depends on sufficient low-frequency data or a good initial model. Passive seismic data contain rich low-frequency components, and passive seismic FWI using virtual source data by seismic interferometry (SI) is a promising method. However, the distribution of passive sources in the subsurface is always inhomogeneous, which will lead to artifacts in the reconstruction results by SI using cross-correlation (CC). SI by multidimensional deconvolution (MDD) can counteract the source inhomogeneity but requires the separation of the reference wavefields, which is difficult to achieve for noise source data. To mitigate this problem, we propose an improved SI method by linear Radon transform based multidimensional deconvolution (LRTMDD). The interferometric point-spread function can be extracted more accurately and efficiently in the linear Radon domain, thus improving the reconstruction results. The passive virtual source full waveform inversion (PVSFWI) based on LRTMDD is further proposed, which can effectively use the low-frequency information in the virtual source data to invert the macroscopic velocity structures even in the case of inhomogeneous source distributions, and without the need to estimate the virtual source wavelets. A joint multi-source FWI strategy is proposed to solve the problem of missing low-frequency data suffered by active source FWI. Numerical experiments on the Marmousi model and the SEG/EAGE overthrust model show that the proposed methods can fully combine the respective advantages of multisource seismic data to stably achieve high-accuracy velocity models in the case of inhomogeneous passive source distributions and the lack of low-frequency data in active seismic data.

Weighted-average time-lapse seismic full-waveform inversion

As seismic data can contain information over a large spatial area and are sensitive to changes in the properties of the subsurface, seismic imaging has become the standard geophysical monitoring method for many applications such as carbon capture and storage and reservoir monitoring. The availability of practical tools such as full-waveform inversion (FWI) makes time-lapse seismic FWI a promising method for monitoring subsurface changes. However, FWI is a highly ill-posed problem that can generate artifacts. Because the changes in the earth’s properties are typically small in terms of magnitude and spatial extent, discriminating the true time-lapse signature from noise can be challenging. Different strategies have been proposed to address these difficulties. In this study, we propose a weighted-average (WA) inversion to better control the effects of artifacts and differentiate them from the true 4D changes. We further compare five related strategies with synthetic tests on clean and noisy data. The effects of seawater velocity variation on different strategies also are studied as one of the main sources of nonrepeatability. We tested different strategies of time-lapse FWI (TL-FWI) using the Marmousi and the SEG Advanced Modeling time-lapse models. The results indicate that the WA method can provide the best compromise between accuracy and computation time. This method also provides a range of possible answers of other TL-FWI strategies.

Full-waveform inversion method for tunnel seismic forward prospecting

SUMMARY During tunnel construction, accurately ascertaining the adverse geological condition in front of the tunnel face is essential to ensure construction safety and decrease economic loss. As an accurate wave velocity modelling method, seismic full-waveform inversion (FWI) still faces many difficulties when applied in a tunnel observation environment because of fewer observation data and smaller offset than ground seismic detection. This paper analysed the FWI for tunnel active seismic forward prospecting with a single shot. We adopted the N-order time integral wavefield and normalized integration objective function method to improve the stability and reduce the dependence of FWI on the initial model. Based on two assumptions on the wave velocity distribution in the tunnel, we proposed a 1-D velocity structure correction method to reduce the multiplicity of inversion. Then the improved tunnel FWI method based on these two inversion strategies was applied to a synthetic model with a rectangular anomaly and a lithology interface, verifying the method's effectiveness. Based on typical adverse geological bodies during tunnelling, three additional adverse geological models were built and verified the reliability of the methods in tunnel detection environments. There are still some false anomalies interference in the inversion results of the synthetic models from the inversion results. However, these problems are acceptable in limited tunnel observation space. Then the method was applied in a field example in the Yinsong water supply project in Jilin Province, China, and verified the effect on field data. Finally, the influence of the shot number on the inversion effect and the method's robustness are discussed.

Data-driven seismic prestack velocity inversion via combining residual network with convolutional autoencoder

Seismic full-waveform inversion (FWI) is a powerful imaging approach in exploration geophysics, to estimate high-resolution subsurface geophysical properties via fitting synthetic data to observed seismic records. Nevertheless, there exist several intractable issues, e.g., solution non-uniqueness, cycle-skipping phenomenon, weak robustness and high computational burden. Recently, more attention has been paid to data-driven FWI techniques based on deep neural networks. However, their success greatly depends on an ocean of training instances, which is not available in oil exploration conveniently. To cope with this situation, an effective strategy is to reduce the dimensionality of input and output spaces for given training instances. By exploiting 2D velocity profile nonlinear compressibility, we train a residual convolutional autoencoder under an unsupervised mode using velocity data, where its encoder acts as a velocity dimensionality reduction operator to transform 2D velocity profiles into low-dimensional 1D pseudo-velocity curves. Then, a residual network is trained to build a nonlinear mapping from multi-shot seismic records to 1D pseudo-velocity curves. In the inference stage, the trained decoder converts those predicted 1D curves back to 2D velocity profiles. Therefore, we propose a novel two-stage data-driven seismic inversion technique combining a residual network with a residual convolutional autoencoder. The detailed experiments on four synthetic data sets (salt body, layered, faulted and salt dome models) illustrate that our proposed method with three different residual network architectures outperforms other approaches including FCNVMB, VelocityGAN and pure residual network, according to four objective evaluation metrics and subjective visualization.

Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization

Full waveform inversion is a widely used technique to estimate the subsurface parameters with the help of seismic measurements on the surface. Due to the amount of data, model size and non-linear iterative procedures, the numerical computation of Full Waveform Inversion are computationally intensive and time-consuming. This paper addresses the parallel computation of seismic full waveform inversion with Graphical Processing Units. Seismic full-waveform inversion of in-plane wave propagation in the finite difference method is presented here. The stress velocity formulation of the wave equation in the time domain is used. A four nodded staggered grid finite-difference method is applied to solve the equation, and the perfectly matched layers are considered to satisfy Sommerfeld’s radiation condition at infinity. The gradient descent method with conjugate gradient method is used for adjoined modelling in full-waveform inversion. The host code is written in C++, and parallel computation codes are written in CUDA C. The computational time and performance gained from CUDA C and OpenMP parallel computation in different hardware are compared to the serial code. The performance improvement is enhanced with increased model dimensions and remains almost constant after a certain threshold. A GPU performance gain of up to 90 times is obtained compared to the serial code.

Potential of Phase-Amplitude-Based Multi-Scale Full Waveform Inversion with Total-Variation Regularization for Seismic Imaging of Deep-Seated Ores

As the demand for ore resources increases, the target for mineral exploration gradually shifts from shallow to deep parts of the Earth (&gt;1 km). However, for the ore-hosting strata, it is difficult to obtain high-resolution images by using the electromagnetic method. Seismic full waveform inversion (FWI) is an optimization algorithm which aims at minimizing the prestack seismic data residual between synthetic and observed data. In this case, FWI provides an effective way to achieve high-resolution imaging of subsurface structures. However, acquired seismic data usually lack low frequencies, resulting in severe cycle skipping of FWI, when the initial velocity model is far away from the true one. Phase information in the seismic data provides the kinematic characteristics of waves and has a quasi-linearly relationship with subsurface structures. In this article, we propose to use a phase-amplitude-based full waveform inversion with total-variation regularization (TV-PAFWI) to invert the deep-seated ores. The ore-hosting velocity model test results demonstrate that the TV-PAFWI is suitable for high-resolution velocity model building, especially for deep-seated ores.