Wookeen Chung

Least-squares reverse time migration (LSRTM), a linearized inverse problem, provides high-resolution subsurface images by minimizing the misfit between observed and simulated data. LSRTM can be implemented in both time and frequency domains. Compared to time-domain LSRTM, frequency domain LSRTM (FLSRTM) offers advantages in handling multi-shot datasets. In addition, wavefield extrapolation needs to be performed only once at the initial iteration of the LSRTM process, provided that Green’s functions can be stored directly in memory. However, the memory required to store Green’s functions increases exponentially, being proportional to the dimensionalities of both model and data spaces. To address this memory issue, we propose a memory-efficient FLSRTM approach based on low-rank approximation via Tucker decomposition. In our FLSRTM method, the Green’s functions for all shot and receiver positions at a given frequency are decomposed into a core tensor and several factor matrices. Based on optimal ranks determined by an energy preservation criterion, the core tensor and factor matrices are truncated, thereby significantly reducing the dimensionality of the Green’s functions. Numerical tests on a modified Marmousi2 velocity model demonstrate the feasibility and substantial memory efficiency of our FLSRTM approach, while preserving the accuracy of migration images. Furthermore, a field data example confirms the practical applicability of our FLSRTM approach.

In full waveform inversion (FWI), long-wavelength velocity models are essential for accurately estimating subsurface physical parameters. However, building long-wavelength velocity models with low-frequency components is challenging due to mechanical limitations in seismic data acquisition. We propose a novel FWI method that utilizes a regenerated wavefield derived from the Suppressed Wave Equation Estimation of Traveltime (SWEET) algorithm. The regenerated wavefield in our approach was obtained by convolving the arbitrary source wavelet with a Green’s function, which is represented by the first-arrival traveltime and amplitude extracted from the SWEET algorithm. Our approach can build long-wavelength velocity models, provided that a low-frequency wavelet is used. Furthermore, the potential for multi-scale inversion was demonstrated by gradually increasing the frequency of the source wavelet, leading to the acquisition of high-resolution models. In numerical examples, our proposed algorithm was validated using both synthetic and field data sets. We also assessed the noise sensitivity of the proposed method, confirming its applicability in practical scenarios. These results demonstrate that the proposed method is a robust and versatile tool for constructing long-wavelength and high-resolution velocity models from band-limited seismic data.

Offshore wind power is gaining increasing importance because of its high energy security, potential, and economic benefits. In the development of offshore wind farms, the characteristics of the seafloor topography and sub-seafloor geology are critical factors that significantly influence the foundation design. Inaccurate interpretation of geological conditions can create substantial financial risks. To mitigate these risks and optimize design, precise investigations of the seafloor and sub-seafloor layers through geophysical explorations are essential. This paper presents an overview of geophysical exploration techniques used in offshore wind farm development, along with recent trends and case studies highlighting their application.

Seismic data recorded in shallow water in the Arctic Ocean contain not only primary reflections but also surface-related multiples with strong amplitudes and short-period characteristics. These multiples generate false stratigraphic boundaries on stacked seismic sections, thereby reducing the accuracy of geological interpretation. Therefore, the attenuation of multiples is an essential step in seismic data processing for accurate geological interpretations. Recently, with the advancement of deep learning technology, research on suppressing surface-related multiples using deep learning networks (such as U-Net and stacked BiLSTM) has been actively proposed.Firstly, surface-related multiple suppression algorithms using U-Net and stacked BiLSTM were applied to Arctic field data respectively. Each algorithm was designed to predict surface-related multiples by using input data that contained both primary reflections and surface-related multiples. Fractional Fourier transform (FrFT) and continuous wavelet transform (CWT), which represent time-series data in the time-frequency domain, were applied to synthetic data and used as input data feature for each network. Finally in order to suppress the surface-related multiples for seismic data in shallow depth Arctic Ocean, the proper methods (network architectures, input data feature) are suggested. AcknowledgmentsThis research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (RS-2023-00259633).

This study aims to detect the bioacoustics signal in the underwater soundscape, specifically those produced by snapping shrimp, using adaptive iterative transfer learning. The proposed network is initially trained with pre-classified snapping shrimp sounds and Gaussian noise, then applied to classify and remove snapping-free noise from field data. This separated ambient noise is subsequently used for transfer learning. This process was iterated to distinguish more effectively between ambient noise and snapping shrimp sounds characteristics, resulting in improved classification. Through iterative transfer learning, significant improvements in precision and recall were observed. The application to field data confirmed that the trained network could detect signals that were difficult to identify using existing threshold classification methods. Furthermore, it was found that the rate of false detection decreased, and detection probability improved with each stage. This research demonstrates that incorporating the noise characteristics of field data into the trained network via iterative transfer learning can generate more realistic training data. The proposed network can successfully detect signals that are challenging to identify using existing threshold classification methods.

Full waveform inversion (FWI) is a notable technique that provides high-resolution physical parameters of subsurface media. Although FWI is frequently employed to recover velocity models for relatively weak parameter perturbations, its effectiveness is limited by the lack of low-frequency information in the presence of strong parameter perturbations. To address this limitation, we propose to utilize Direct envelope inversion (DEI), which highlights the low-frequency information contained within the seismic envelope data to successfully construct velocity models for strong parameter perturbations. However, conventional DEI requires envelope computation for the source wavefield, which limits the application of memory cost-reduction methods and significantly increases the computational time needed for the envelope. To mitigate these computational challenges, we introduce excitation amplitude (ExA) as a means to reduce the computational cost associated with DEI. By utilizing only the most energetic amplitude and its arrival time at each grid point of the direct envelope virtual source field, this method can reduce the computational time and memory requirement while maintaining the accuracy of the DEI. In the numerical examples, we demonstrate that the proposed method overcomes the computational cost limitations of conventional DEI. Additionally, applying our method to field data acquired in the Arctic helped reconstruct strong scattering models for the subsea permafrost.

In least-squares reverse time migration (LSRTM), which is typically an overdetermined linear inverse problem, the kinematic traveltime error from an inaccurate migration velocity leads to migration image degradation and slow convergence speed. Extended LSRTM (ELSRTM), with an extended imaging condition along the subsurface offset in LSRTM, can effectively mitigate sensitivity to the migration velocity accuracy by adding an extra dimension to the model space. However, for assembling migration images, large imaging condition operations that are proportional to the number of subsurface-offset bins hinder the practical application of ELSRTM. To address this computational problem in ELSRTM, we develop an efficient ELSRTM method based on a modified excitation amplitude (ExA) imaging condition. Furthermore, our ELSRTM method can correctly represent the forward source wavefield by convolving the source wavelet with the ExA. With the forward source wavefield represented this way, Born-modeled data can be simulated efficiently. Thus, wavefield simulation for generating the forward source wavefield is implemented only once at the first iteration. Based on these computational advantages, our ELSRTM method becomes highly efficient. Synthetic data examples on a modified Marmousi2 velocity model demonstrate that our ELSRTM method produces an accurate migration image efficiently even if an inaccurate migration velocity is used. Field data acquired from the Chukchi Sea of the Arctic Ocean also are used to verify the practicality of our ELSRTM method.

Full-waveform inversion (FWI) is a useful tool for estimating the properties of subsurface media, but it has the disadvantage of incurring considerable computing costs. Therefore, we develop a method for reducing the computing memory requirement in the time domain of FWI that uses a new approach with the discrete cosine transform (DCT) and the Nyquist sampling theorem. Our method enables accurate reconstruction of a wavefield with several optimal DCT coefficients derived by considering the maximum wavenumber component of the virtual source wavefield and the maximum frequency of the source wavelet. Consequently, virtual source wavefields can be stored in a computer’s memory rather than on a disk. Our FWI algorithm is compared with existing compression methods that use DCT and wavelet transform in a numerical example. The results of this comparison demonstrate that our algorithm provides significantly improved computational efficiency.