We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum. Thus, we can achieve fine pore evaluation in fracture–vug reservoirs based on electric imaging logging data. We automatically identify and extract fracture–vug information from the electric imaging images by adopting a path morphological operator that remains flexible enough to fit rectilinear and slightly curved structures because they are independent of the structuring element shape. The Otsu method was used to extract fracture–vug information from the background noise caused by the matrix. To accommodate the differences in scale and form of the different target regions, including fracture and vug path, operators with different lengths were selected for their recognition and extraction at the corresponding scale. Polynomial and elliptic functions are used to fit the extracted fractures and vugs, respectively, and the fracture–vug parameters are deduced from the fitted edge. Finally, test examples of numerical simulation data and several measured well data have been provided for the verification of the effectiveness and adaptability of the path morphology method in the application of electric imaging logging data processing. This also provides algorithm support for the fine evaluation of fracture–vug reservoirs.

Marine self-potential (SP) investigation is an effective method to study deep-sea hydrothermal vents and seafloor sulfide deposits. At present, one of the commonly used marine self-potential systems is a towed array of electrodes. Large noises are recorded when great changes in electrode distance and array attitude occur due to the complex seafloor topography. In this paper, a new multicomponent electrical field observation system based on an autonomous underwater vehicle (AUV) was introduced for the measurement of seafloor self-potential signals. The system was tested in a lake, and the multicomponent self-potential data were collected from there. Observed data involve the navigational information of the AUV, which could be corrected using a rotation transform. After navigational correction, measured data can recover the location of the artificial source using self-potential tomography. The experimental results showed that the new SP system can be applied to marine SP observations, providing an efficient and low-noise SP acquisition method for marine resources and environmental investigations.

The crosshole electromagnetic (EM) method is used in reservoir development monitoring and evaluation. It reflects the resistivity distribution between wells in the form of EM response changes and describes the spatial distribution of pore fluids in subsurface reservoirs based on the correlation between resistivity and pore fluids. This technique has been used to describe reservoirs, monitor steam and brine flooding to improve oil recovery, optimize the design of infilled wells, and verify the effectiveness of reservoir numerical simulations. However, due to the complexity of the electrical distribution of underground reservoirs and the limitations of the crosswell EM response range, there is still a lack of practical and effective crosswell EM imaging methods. This article presents a three-dimensional EM inversion scheme for interpreting crosswell data in the frequency domain with general measures. For the forward modeling, the equation for the secondary electric field was discretized using the staggered-grid finite difference scheme and was solved using the Krylov subspace iterative method. For the inversion problem, the general measure of the data misfit and regularization constraints were integrated using the Gauss–Newton minimization method to form an objective function. By selecting appropriate parameters and regularization constraints, the sharp boundaries and blocky features of the inverted target were recovered. The sensitivity was calculated using the reciprocity relationship, and the method was found to be beneficial to determining appropriate initial values of regularization coefficients. Two synthetic data sets and a field data inversion of a crosswell configuration demonstrate the feasibility of this scheme.

The marine self-potential (SP) method is currently playing an increasing role in the exploration and resource evaluation of seafloor massive sulfide (SMS) deposits. SP surveys are conducted using autonomous underwater vehicles (AUV), which yield multicomponent electric field datasets. By comparing with the single-component electrical field data used to date, the inversion of these multicomponent data is expected to provide a more accurate description of the 3D structure of SMS deposits beneath the seafloor (like gradiometry in gravity surveys). We introduce an inversion algorithm specifically adapted to multicomponent SP data. A synthetic model demonstrates that the inversion of multicomponent datasets allows us to better recover the amplitude of the current density and the morphology of the ore bodies compared to using a single component of the electrical field. Next, we apply our approach to a multicomponent SP dataset collected during the DY58 oceanic cruise at the Yuhuang hydrothermal field on the Southwest Indian Ridge. Subsequently, we reconstruct the three-dimensional (3D) geometry of the SMS deposits beneath the seafloor. The AUV-based SP system with the collection of multicomponent SP data inversion appears to be a powerful tool in the exploration and evaluation of seafloor sulfide resource and, in the future, could be used in concert with induced polarization data.

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows.