Multiwave inversion: A key step for depth model building — Examples from the Sultanate of Oman

Donno, Daniela; Farooqui, M.S.; Khalil, Mostafa; McCarthy, David; Solyga, Didier; Courbin, Jillian; Prescott, A.; Meur, D. Le

doi:10.1190/tle40080610.1

Cited by 5 publications

(1 citation statement)

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“…The resulting phase velocity at each frequency can vary depending on who performs the process. Moreover, this process usually requires significant time and effort (Donno et al., 2021). Additionally, the information obtained from MASW is typically limited to the fundamental mode of the surface (Rayleigh) waves.…”

Section: Introductionmentioning

confidence: 99%

Prediction of S‐wave velocity models from surface waves using deep learning

Cho,

Pyun,

Choi

et al. 2023

Near Surface Geophysics

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Surface wave (SW) methods extract dispersion properties of wavefields propagating through a seismic array (1D or 2D). This is achieved by analysing the phase velocity versus frequency (or wavelength) data. Afterwards, an inversion process is performed to construct near‐surface S‐wave velocity models. Among the SW methods, multichannel analysis of SWs (MASW) is commonly used for engineering applications, analysing dispersion characteristics by generating a dispersion image. However, classical MASW depends on the manual picking of dispersion curves, which can lead to subjective outcomes and require time and effort to obtain precise results. To avoid these pitfalls, many studies, including deep‐learning techniques, have focused on automating the process. Similarly, we propose a deep‐learning‐based algorithm that estimates the S‐wave velocity directly from the dispersion image of SWs. This algorithm consists of a convolutional neural network (CNN) designed to directly yield S‐wave velocity profiles and a fully connected network (multi‐layer perceptron) added to regularize predictions. Unlike typical SW techniques, the proposed approach does not incorporate prior information such as layer count and thickness. To ensure successful training, we modified the loss function to exploit the normalized mean squared error. The training dataset was generated by modelling synthetic shot gathers and transforming them into dispersion images for various 1D stratified velocity structures. After a sample is fed to the CNN network for inversion, the inversion network's output subsequently goes through an additional simple neural network (NN) to regularize the predicted S‐wave velocity model (which is the post‐processing step). The combined usage of deep‐learning‐based SW inversion with NN‐based post‐processing was assessed using test data. The proposed algorithm achieved an average relative error of approximately 7.49% in predicting the S‐wave velocity and was successfully applied to the field data. Additionally, we discuss its performance on noisy data as well as its applicability to out‐of‐training data. Numerical examples demonstrated that the proposed method is robust to noise, whereas it requires additional training to handle data beyond the distribution of the training data.

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Section: Introductionmentioning

confidence: 99%

Prediction of S‐wave velocity models from surface waves using deep learning

Cho,

Pyun,

Choi

et al. 2023

Near Surface Geophysics

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Near-surface Site Characterization Based on Joint Iterative Analysis of First-arrival and Surface-wave Data

Wang

Sun

2022

Surv Geophys

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Near-surface site characterization is of great significance in the fields of geotechnical engineering and resource exploration. In this paper, we propose a near-surface site characterization method based on the joint iterative analysis of first-arrival and surface-wave data (JIAFS). The proposed method combines the advantages of first-arrival traveltime tomography (FATT) and multichannel analysis of surface waves (MASW). First, the 1D S-wave velocity ($$v_{{\text{S}}}$$ v S ) models obtained by MASW are interpolated to construct the pseudo-2D $$v_{{\text{S}}}$$ v S model. According to the available geological survey information and borehole data, the initial Poisson’s ratio ($$\sigma$$ σ ) model is estimated. Based on the estimated $$\sigma$$ σ model, the pseudo-2D $$v_{{\text{S}}}$$ v S model is converted to a referenced P-wave velocity ($$v_{{\text{P}}}$$ v P ) model which is utilized to constrain the progress of FATT. This helps FATT overcome the inherent defect that it cannot effectively identify velocity-inversion interfaces and low-velocity zones. On the other hand, the $$v_{{\text{P}}}$$ v P model obtained by FATT can provide a favorable priori information to improve the reliability of the results of MASW. Then, the $$v_{{\text{P}}}$$ v P and $$v_{{\text{S}}}$$ v S models obtained by constrained FATT and MASW are used to update the $$\sigma$$ σ model. In addition, the $$v_{{\text{P}}}$$ v P and $$v_{{\text{S}}}$$ v S models are also used as initial models in the next iterative analysis. Finally, through the iteration of this process, the two inversion methods can make use of their own advantages to improve each other, so we can establish accurate near-surface $$v_{{\text{P}}}$$ v P , $$v_{{\text{S}}}$$ v S and $$\sigma$$ σ models under complex geological conditions. A velocity model including low-velocity zone is established for synthetic model test to analyze and verify the advantage of JIAFS. The $$v_{{\text{P}}}$$ v P , $$v_{{\text{S}}}$$ v S and $$\sigma$$ σ models obtained by JIAFS can accurately identify the low-velocity zone and match the true models well. In addition, the proposed method is applied to the field seismic data acquired for oil and gas exploration in Northwest China. Compared with the results of individual inversions and borehole data, JIAFS can establish more reliable 3D $$v_{{\text{P}}}$$ v P , $$v_{{\text{S}}}$$ v S and $$\sigma$$ σ models by interpolating the 2D inversion results, which reveals further details and enhances the geological interpretation significantly.

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