Abstract Resistivity imaging logging has become a direct and precise method for visualizing the structural complexities of reservoir fractures and dissolution pores. The current use of Otsu's thresholding for segmentation results in poor segmentation quality and significant noise. Accurate segmentation of sub-images containing fracture and dissolution pore targets is essential for automated structure identification and subsequent parameter calculation. This study leverages the rapid convergence and robust global optimization capabilities of the dung beetle optimizer to develop enhanced image segmentation approaches. Specifically, it introduces a refined K-means algorithm for multi-category image segmentation and an Otsu algorithm for multi-threshold image segmentation, both optimized by the dung beetle optimizer. Compared to conventional binary segmentation algorithms, this new algorithm effectively isolates noise and extracts multi-category information. Using the segmented sub-images, this paper integrates mathematical morphology techniques to compute parameters such as area, perimeter, tortuosity length, and pore shape factor for identified targets. Additionally, principal component analysis is used to derive recognition factors for fractures and dissolution pores. Applications show that this factor can identify matrix, fracture, and dissolution pore targets in complex background images. By combining parameter information of the target area, the method effectively removes false information in resistivity imaging and segments sub-images of fractures and dissolution pores, calculating fracture area ratio, dissolution pore area ratio, and total area ratio.

Abstract The complexity of volcanic rocks presents challenges in seismic data quality and fine characterization of structural morphology, making velocity modeling difficult. In this study, we propose a velocity modeling method for volcanic rock areas that combines the use of amplitude envelope attribute and grid tomography inversion. By exploiting the strong reflection characteristics of volcanic rocks, the amplitude envelope attribute is employed to identify volcanic rock structures, eliminating the need for labor-intensive manual interpretation. The grid tomography inversion technique is then used to derive background velocity fields for all strata. These are further refined by incorporating the residual velocity fields of volcanic rocks through fusion and superposition. This integrated approach significantly improves the efficiency and accuracy of velocity modeling in complex volcanic rock areas, overcoming the limitations associated with conventional volcanic rock structural models and resulting in more accurate velocity inversion. The effectiveness and feasibility of the proposed method for velocity modeling in volcanic rock zone are demonstrated through the application of seismic data from an oilfield in eastern China. The results highlight the method's strong industrial application value.

Abstract Full waveform inversion (FWI) is a method for building subsurface models with high resolution by optimizing a data-fitting problem. However, it is still a challenge to apply the FWI method to complicated subsurface models, because FWI is a highly ill-fitting inversion problem. When the simulated data differ from the observed data by more than half a cycle, FWI tends to suffer from the cycle-skipping problems and get trapped in a local minimum. To help the inversion to converge to the accurate subsurface model, we typically build a reasonable initial model or use low-frequency data to start our inversion. Here, we developed a novel technique called smoothing of dilated convolutions inversion (SDCI) to generate low-frequency components from seismic data to recover low-wavenumber components of the subsurface. In the theory part, we first present the objective function of the SDCI method and then derive the gradient of the objective function relative to the velocity using the adjoint-state approach. We then apply the method to a set of simulated data from the Marmousi model. The SDCI method can reasonably estimate the subsurface model even if the simulated seismic record does not contain information below 5 Hz. The numerical examples prove the effectiveness and feasibility of the proposed method. From the SDCI results, the FWI method recovers the velocity with higher accuracy and resolution.

Abstract Seismic shear waves have been used in oil and gas exploration for decades. A 2D seismic shear wave inline was acquired in the Sanhu area, located in the Qaidam Basin in western China. Although the acquired shear wave data showed high resolution and comparable bandwidth to the compressional wave, it was contaminated by various types of noise, including linear noise, single-frequency noise, and especially internal multiples. Internal multiples seriously compromise the primary reflections at both near offset and far offset and are difficult to suppress. This paper presents a case study of noise attenuation for the seismic shear wave. First, single-frequency noise and linear noise are attenuated through filtering methods. Then, two methods (the Radon transform and the frequency-wavenumber (F-K) filtering) are evaluated for their effectiveness in multiple suppression and amplitude preservation. The results indicate that both methods successfully reduce long-period multiples at the far offset, enhancing the signal-to-noise ratio. We show that F-K filtering retains the characteristic ‘strong–weak–strong’ amplitude variation in the SV-SV-wave data gather, making it preferable for subsequent amplitude variation with offset analysis and inversion. Finally, a wave equation-based multiple suppression inversion method is used to suppress near-offset internal multiples. This involves iteratively predicting internal multiples and adaptively subtracting them from the original data. Stacked sections of different offsets are compared to demonstrate the de-multiple result, and the result is also validated by the improvement of well calibration with the seismic shear wave data.

Abstract Amplitude variation with azimuth methods have a huge advantage in predicting small-scale fractures. However, the technique of using post-stack seismic data to predict fractures has rarely been investigated, with the main limiting factor being the degradation of resolution due to the stacking process, which makes it difficult to effectively identify small fractures. With the exploration and development of oil and gas fields entering into deeper levels, the traditional post-stack fracture prediction techniques often fail to meet the requirements of fine development. In this study, we propose a post-stack fracture enhancement technique based on phase decomposition. The technique advances parity phase decomposition based on the matched pursuit algorithm to extract the dominant phases in the seismic record, and the filtering process is carried out by the bidimensional empirical mode decomposition, which in turn computes the improved post-stack coherence and similarity attributes. Applying this method to 3D seismic datasets in the Sichuan region, the results indicate that the approach improves the seismic data quality and enhances the seismic attribute identification of fractures. The phase decomposition-based post-stack fracture enhancement technique shows significant potential in improving the signal quality of multidimensional seismic features such as texture, dip, and likelihood compared to traditional methods. This new technique provides a new way for fracture detection and characterization using post-stack seismic data, which can help to meet the needs of fine oilfield exploration and development.

Abstract The conventional reverse time migration utilizes regularly sampled computational grids to simulate wave propagation. Selecting the appropriate grid sampling is important for computational accuracy and efficiency. In general, the uniform-size grid cannot represent the complexity of the geology well. The grid may appear sparse in the low-velocity zone, especially in shallow depths where dispersion may occur. Conversely, it may appear excessively dense in the high-velocity zone, such as at greater depths or within a salt body, which results in higher computational memory and time consumption. To overcome these issues, we developed an efficient and accurate adaptive variable grid discretization method that automatically selects the vertical grid size based on the velocity, depth, and dominant frequency of the wavelet in elastic medium. Then we reformulated the elastic equations based on the adaptive variable grid by introducing a mapping relationship. To test the effectiveness, accuracy, and efficiency of the equation, we implemented it to both the forward propagation and migration of elastic wavefield. Synthetic numerical examples demonstrate that our proposed method can achieve elastic wavefield separation and no significant dispersion phenomenon. The multi-component imaging accuracy of reverse time migration is nearly equivalent to the traditional method, while significantly improving computational efficiency and saving storage space.

Abstract Kathmandu Valley situated in Nepal has experienced several earthquakes in the past. The expanding population of Kathmandu Valley is driving a significant acceleration in building construction reaching record levels and posing risks to both human lives and the economy, despite this surge in development. This study aimed to spatially map the dominant frequency, amplification factor, and vulnerability index map of Kathmandu Valley, Nepal using ArcGIS. This paper presents the nonlinear response analysis of 225 boreholes using the Gorkha earthquake as input motion and DEEPSOIL software for determining the amplification ratio and dominant frequency. The results revealed a range of values for dominant frequency from 0.08 to 7.65 Hz and soil amplification ratio from 0.13 to 8.14. Furthermore, the seismic vulnerability index was derived from the dominant frequency and soil amplification ratio ranging from 0.22 to 21.84. The resulting maps acquired through this study enable the decision-makers and stakeholders, to prioritize resources and implement targeted measures for enhancing seismic resilience in the region.

Abstract The resolution ability of the transient electromagnetic method (TEM) for double-layer water-filled goaf is affected by the low resistance shielding effect of the overlying strata. Currently, there is a lack of quantitative research on how the parameter changes of the upper water-filled goaf affect its resolution capability. In this study, through numerical and physical simulations, the relative error of the TEM response between single- and double-layer water-filled goafs was regarded as the evaluation index to analyse and summarize the resolution capability of the TEM for double-layer water-filled goaf under different burial depths, thicknesses, and areas of the upper water-filled goaf. The results are summarized as follows. (ⅰ) The resolution capability of the TEM for double-layer water-filled goaf decreased with the increasing burial depth, thickness, or area of the upper water-filled goaf. (ⅱ) The rate of decrease in resolution gradually accelerated as the burial depth increased. Conversely, as the thickness increased, the decrease rate in resolution gradually reduced. Furthermore, the resolution decreased rapidly and then tended towards a fixed value as the area increased. (ⅲ) The maximum value of the relative error curve moved towards the early stage with the increasing burial depth, resulting in a detection depth of the target body smaller than the actual value. Moreover, it moved towards the later stage with the increasing thickness or area, resulting in a detection depth of the target body greater than the actual value.

Abstract Nonlinear beamforming (NLBF) has emerged as a highly effective technology for enhancing seismic data quality. The crux of NLBF's success lies in its ability to robustly estimate local traveltime operators directly from input data, a process that entails solving millions or even billions of nonlinear optimization problems per input gather. Among the solvers utilized for estimating these operators is the 2 + 2 + 1 method, for which we have previously introduced algorithmic implementations on both the CPU and GPU platforms. In this paper, we present an efficiency-improved GPU algorithm for the 2 + 2 + 1 method, particularly beneficial when dealing with small data apertures in NLBF. Our enhanced GPU algorithm brings significant improvements in computation efficiency through several strategic measures, which include leveraging Horner's method to minimize the mathematical overhead of traveltime calculation, implementing a GPU-friendly data reduction algorithm to exploit GPU computational power, and optimizing shared GPU memory usage as the primary workspace whenever feasible. To demonstrate the tangible efficiency enhancement achieved by our new GPU algorithm, via two illustrative examples, we compare its performance with that of our previous implementation.

Abstract The emerging distributed fiber-optic acoustic sensing (DAS) technology has broad prospects for application in vertical seismic profiles (VSP). However, the acquired DAS-VSP data often suffers from coupled noise that seriously affects data quality. Traditional methods for suppressing coupled noise are usually time-consuming and not suitable for the large-scale denoising of DAS-VSP data. To address this, a coupled noise suppression method based on the U-Net network is proposed, and a self-attention (SA) block is introduced to enhance the denoising ability of the network. Transfer learning is employed to achieve coupled noise suppression from synthetic data to field data. Denoising results demonstrate that the network can effectively suppress coupled noise in DAS-VSP data while preserving signal energy to a certain extent, exhibiting strong generalization capability. Upon completion of network training, denoising results can be obtained within seconds, making it more convenient and efficient compared to traditional methods.