This article presents a boost converter based on a maximum power point tracking (MPPT) circuit, and a multicurrent detector (MCD), an offset cancellation circuit to achieve high efficiency over a wide range of input power. The proposed boost converter employs an MPPT design for adaptive on-time operation, which adjusts the on-time width to achieve maximum efficiency depending on the input voltage. In addition, a phase frequency detector-based offset cancellation method is proposed to minimize power loss by accurately sensing the current detector, thereby transferring power. Therefore, the proposed method achieves a significant efficiency improvement of up to 10 % by implementing high-precision offset cancellation with an offset level of approximately$5~\mu $A, which is significantly lower than the conventional MCD offset of over 60 mA of maximum offset current level. The proposed boost converter fabricated in a 130 nm Bipolar-CMOS-DMOS (BCD) process occupies an active area of 0.806 mm2 and can self-start with a minimum input voltage of 450 mV ($40~\mu $W) and achieve maximum efficiency of 92.7 %.

At geothermal sites, estimating underground velocity structures from seismic data is essential for improving the accuracy of microseismic event localization, characterizing reservoir properties, and understanding subsurface dynamics. However, constructing detailed velocity models from surface seismic data is challenging due to the poor signal-to-noise ratio (SNR) and the complexity of near-surface structures with significant topographic variations. This study presents a novel application of P-wave travel-time tomography using distributed acoustic sensing (DAS) to develop a 2-D P-wave velocity model for the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) geothermal site. Unlike conventional vertical seismic profile (VSP), the DAS technique enables dense spatial sampling along boreholes, providing higher resolution subsurface characterization. First, we extract the 2-D initial model from the legacy 3-D velocity model estimated from a 3-D active-source seismic survey. Then, we apply P-wave travel-time tomography with DAS-VSP data acquired in two boreholes close to each other. Consequently, the resulting velocity model shows that most first arrivals are close to real VSP data, even for far-offset data, and align well with geological and well-log data. The detailed model reveals lateral variations across three distinct depth zones: unconsolidated alluvium, consolidated alluvium, and granitoid. Thus, this study demonstrates the capability of DAS-VSP data and travel-time tomography in resolving localized subsurface structures and highlights its potential to address cycle-skipping issues in future high-frequency full-waveform inversion (FWI) studies requiring accurate initial velocity models.

Given the potential influence of seismic anisotropy on multichannel seismic data acquired over sedimentary basins, applying an optimal anisotropic time-lapse seismic imaging technique to accurately estimate the injected CO2 plume is imperative. This study conducts acoustic multiparameter assumption full-waveform inversion (FWI) based on the elliptical anisotropy to enhance the accuracy of time-lapse seismic monitoring results at the Sleipner field. The Sleipner project is the pioneering commercial-scale offshore carbon capture and storage (CCS), marking the successful implementation of time-lapse seismic surveys to monitor the injected CO2 plume. To validate the necessity of anisotropic time-lapse FWI at the Sleipner field, we construct an initial anisotropic velocity model using Backus averaging. Subsequently, we compare the accuracy of the velocity structure obtained with isotropic and anisotropic FWI starting from the same initial model. Consequently, we confirm that the elliptical anisotropic time-lapse FWI significantly enhances accuracy, as demonstrated by the improved matching of synthetic data derived from anisotropic FWI to the real data, compared to the isotropic FWI results.

Image enhancement is crucial for improving the resolution of seismic images obtained from band-limited data. While machine learning techniques, particularly the U-Net model, have shown significant progress in this area, they often require substantial computational resources and time. To address these challenges, we introduce a transformer-based approach for enhancing seismic image resolution, which incorporates convolutional layers, an average pooling layer, and an efficient transformer (ET). The ET leverages efficient multihead attention (EMHA) to capture long-term dependencies among image blocks, focusing on the pixels within their contextual surroundings. In our proposed model, we use a combined loss function consisting of the mean square error (mse) and the structural similarity (SSIM) to enhance the network’s learning capability. By training the model on synthetic seismic data, we observe improved structural features, enhanced resolution, and effective denoising. Notably, our approach outperforms the U-Net model in terms of SSIM and the peak signal-to-noise ratio (SNR). Furthermore, we evaluate the pretrained model on several field datasets, yielding promising results compared to the benchmark method. This demonstrates the potential applicability and effectiveness of our proposed approach in real-world scenarios.

The carbon capture and storage (CCS) project is gaining attention for its role in greenhouse gas reduction. In the CCS project, monitoring injected CO2 is crucial for safe and sustainable operation. The Containment and Monitoring Institute (CaMI) project has been launched to secure CO2 monitoring techniques, particularly using the time-lapse seismic survey. In this work, we apply 3-D acoustic full-waveform inversion (FWI) to the walk-away and walk-around vertical seismic profiling data. To construct a baseline P-wave velocity model for future monitoring studies, we compare the performance of 2-D and 3-D FWI on this data. We first conduct a synthetic FWI test using a 1-D velocity model created from well-log data to identify optimal parameters and potential issues. Finally, we apply FWI to real data and analyze the inverted results. As a result, compared with 2-D FWI, we verify that 3-D FWI can be a valuable tool to build a baseline model, anticipating its future extension into 4-D seismic monitoring.

Full-waveform inversion (FWI) aims at building a high-resolution velocity model by fitting numerically computed seismic data to observed one. Considering both the kinematic and dynamic properties of all waves in seismic data makes FWI highly non-linear. To mitigate its non-linearity, one can preferentially build low-wavenumber background velocity first, and then retrieve high-wavenumber reflectivity. However, in the early stage of FWI, the low-wavenumber velocity update is hardly derived from the reflected waves and mainly relies on the diving waves. To secure a wider coverage of low-wavenumber velocity updates from the reflected waves in addition to the diving waves, we propose two-step FWI based on diffraction-angle filtering (DAF) for 3-D seismic reflection data. In our method, DAF, which imitates the amplitude variations of the PP partial derivative wavefields with respect to the model parameters in elastic FWI to control small, intermediate, or large diffraction-angle energy, is adopted for the scale separation of a given velocity model into a background velocity and reflectivity model. The prior reflectivity provides information on reflection wavepaths. Then, the low-wavenumber update generated along the full wavepaths can build a background velocity model with improved wavenumber coverage. In addition, DAF can be implemented without a large increase in computational effort, which enables practical applications of our method to large-scale 3-D seismic field data. Applications of DAF-based two-step FWI to 3-D synthetic and field seismic data demonstrate that DAF-based two-step FWI can build a reliable background velocity model, which leads to stable convergence toward the global minimum.