Accurate characterization of elastic properties near boreholes is critical for reservoir evaluation, well integrity assessment, and high-resolution formation imaging. Traditional borehole inversion methods often suffer from limited resolution and complex implementation, particularly in scenarios involving radial heterogeneity and thin layers. In this study, we present a novel borehole full waveform inversion framework based on automatic differentiation and recurrent neural networks. By reformulating the elastic wave equation in cylindrical coordinates as a recurrent structure, forward modeling becomes naturally embedded in a deep learning framework. Gradient computation is performed using automatic differentiation, which eliminates the need for manually derived adjoint equations and enables flexible implementation of multi-parameter inversion. We validate the proposed method through numerical experiments on a series of synthetic models with increasing geological complexity, including radially layered formations, curved interfaces, and thin low-velocity layers. We further investigate the effects of different acquisition configurations, such as source-receiver offsets, sparse sampling, and unidirectional observation setups. Results show that the proposed method can recover both P-wave and S-wave velocities with high accuracy, even resolving sub-wavelength thin beds beyond the capability of conventional velocity estimation method using logging data. The approach also demonstrates robustness under reduced acquisition coverage and practical logging conditions. Our findings suggest that the proposed framework offers a powerful and flexible solution for high-fidelity borehole imaging, with broad applicability in geophysical exploration and logging interpretation.

In this paper, a novel reconstruction design scheme is proposed to converter a solar water heating(SWH) system into a PV power generation system with the same annual savings as standard coal. The idea of this paper is based on a practical project in China. Differing from the traditional design of the PV power generation system, the design and performance of SWH systems are firstly analyzed, following the reconstruction of the PV power generation system. In order to guarantee the same production capacity of electric energy, the key issues concerning the installed capacity of the PV power generation system, the equipment choices of the PV modules, inverters, cables and grid-connected circuit breakers. Meanwhile, some key issues affecting the system's implementation and safety, such as the installation angle of PV arrays and lightning protection, have also been investigated. In order to validate this paper, comparative data concerning the generated power energy and income is estimated to show the effectiveness of the reconstruction design scheme.

High-resolution geophysical exploration is crucial for accurately understanding underground structures. In acoustic well logging, conventional methods for enhancing vertical resolution often rely on coherence-based subarray techniques. However, due to the degradation of semblance over short arrays, the estimated acoustic slowness typically lacks accuracy for small aperture resolutions. In this article, an acoustic array processing method based on super-virtual interferometry (SVI) is presented, aiming to achieve high-resolution slowness profiles from borehole logging data. First, a subarray extension technique is proposed to enhance semblance. Second, we extend the SVI-based acoustic array processing methods, which can generate redundant information for semblance calculation from limited observations. The principles and workflow of the method are detailed, and numerical experiments are conducted to demonstrate its advantages over traditional processing approaches. Field data processing results indicate that the proposed methods provide high-accuracy measurements of slowness to the subfoot level, capturing intricate details of variations in formation slowness. Furthermore, field applications demonstrate the method’s capability to process dipole flexural waves and effectively suppress extraction errors through dispersion, providing an alternative solution for the time-domain treatment of dispersive waves.

In complex near-surface geologic settings, first-arrival traveltime tomography suffers from strong data noise and uneven raypath coverage. To reduce the inversion nonuniqueness, model smoothing is necessary but often without incorporating structural constraints based on seismic images. The reasons include a lack of shallow reflections, strong noise, and gradient velocity field with strong lateral variation. We propose to estimate the near-surface structural trend from the velocity solution of deformable-layer tomography (DLT), that inverts for velocity contours directly. Then the structural dips derived from the DLT solution are taken as the structural regularization in the subsequent traveltime tomographic inversion. Tests for the 2011 Symposium on the Application of Geophysics to Engineering and Environmental Problems (SAGEEP) workshop model illustrate that this DLT-guided structural regularization steers the tomographic inversion toward more accurate near-surface models. The application of the DLT-guided structural regularization to field data reveals the near-surface geology of a beach area in Qingdao, China. The DLT solution could complement seismic images or other structural information in regularizing tomographic inversion.

Understanding the properties of rocks surrounding boreholes is crucial for industries such as oil and gas exploration. This article presents an offset-controlled localized velocity inversion method in the$\tau -p$domain, aimed at enhancing the accuracy and computational efficiency of velocity imaging around boreholes. By incorporating positional information from receiver arrays into velocity inversion process, the method more accurately reflects depth and radial variations in acoustic velocity. Numerical models validate the applicability of the method, showing that receiver arrays with a near-to-far configuration should be selected for logging observation systems to effectively capture radial velocity variations. Additionally, considering the offset improves the accuracy of estimating formation velocity variations, particularly for sudden changes such as thin layers. The effectiveness of this approach is further verified using actual borehole logging data, demonstrating its ability to capture velocity variations around wells and providing a new means for interpreting logging data.

In the logging-while-drilling acoustic measurements, distinguishing and estimating formation acoustic properties from the interfering signals are commonly deemed difficult. In this study, we present the results of a new signal processing technique based on the supervirtual interferometry theory for LWD acoustic data processing. Analytical and numerical modeling is used to analyze the waveform characteristics for different collar attention effects. The results show that the formation signal identification can be difficult in the presence of the collar wave, especially in the situation when the collar wave is poorly attenuated. We apply the new technique to the synthetic and field LWD acoustic data to evaluate its validity and effectiveness. The processing results for the interval demonstrate the ability of the technique to extract formation property for the LWD data in the presence of collar wave and drilling-related noise.

The logging-while-drilling (LWD) acoustic technology has been developed to measure formation velocities/slownesses for rig time saving and real-time applications such as seismic-tie, pore pressure determination and/or deep-sea drilling exploration. The formation compression-wave velocity is often hard to determine because of the drilling environment and wave dispersion effects. We develop a new model-based dispersive processing (MDP) method to accurately determine wave slowness from the LWD acoustic data. Two specific steps are included during the MDP procedure. In the first, the effective collar parameters are determined using the collar-wave from the measurements. The second step is the inversion for the wave slowness by minimizing the difference between the theoretical curve and dispersion data. We have validated the result of the MDP inversion with synthetic data testing and applied it to the acoustic LWD data of the Ocean Drilling Program (ODP) 808I hole. Applications of the corrected slowness results to synthetic seismogram and porosity prediction analyses demonstrate the effectiveness of the correction method.