Surveys in Geophysics最新文献

Compared with surface wave corresponding to the normal mode, which is widely studied, there is less research on guided-P wave corresponding to the leaking mode. Guided-P wave carries the dispersion information that can be used to construct the subsurface velocity structures. In this paper, to simultaneously estimate P-wave velocity (({{v}}_{{P}})) and S-wave velocity (({{v}}_{{S}})) structures, an integrated inversion method of guided-P and surface wave dispersion curves is proposed. Through the calculation of Jacobian matrix, the sensitivity of dispersion curves is quantitatively analyzed. It shows that the dispersion curves of guided-P and surface waves are, respectively, sensitive to the ({{v}}_{{P}}) and ({{v}}_{{S}}). Synthetic model tests demonstrate the proposed integrated inversion method can estimate the ({{v}}_{{P}}) and ({{v}}_{{S}}) models accurately and effectively identify low-velocity interlayers. The integrated inversion method is also applied to the field seismic data acquired for oil and gas prospecting. The pseudo-2D ({{v}}_{{P}}), ({{v}}_{{S}}) and Poisson’s ratio inversion results are of significance for near-surface geological interpretation. The comparison with the result of first-arrival traveltime tomography further demonstrates the accuracy and practicality of the proposed integrated inversion method. Not only in the field of exploration seismic, the guided-P wave dispersion information can also be extracted from the earthquake seismic, engineering seismic and ambient noise. The proposed inversion method can exploit previously neglected guided-P wave to characterize the subsurface ({{v}}_{{P}}) structures, showing broad and promising application prospects. This compensates for the inherent defect that the surface wave dispersion curve is mainly sensitive to the ({{v}}_{{S}}) structure.

This review presents the progress made in the last decade in the field of large-scale electromagnetic (EM) induction with natural sources, which fluctuate at periods from seconds to years and originate in oceans, ionosphere and magnetosphere. These mechanisms produce field variations that can be used to image subsurface electrical structure of Earth and planets across scales and depths from the shallow crust to the lower mantle. In the last decade, we have seen a substantial progress made in different areas related to methods, observations and 3-D numerical modelling of EM phenomena at crustal and mantle scales. Specifically, new methods for handling complex ionospheric and magnetospheric sources were proposed, accompanied by more efficient forward and inverse modelling tools that allowed us to combine several broadband sources and constrain electrical conductivity on multiple scales simultaneously. Magnetic signals due to oceanic tides were established as a new source to probe conductivity of the sub-oceanic upper mantle. Further, the launch of ESA Swarm satellites in 2013 and their successful ongoing operation have marked a new era in the field of large-scale EM induction, unlocking a set of new opportunities, but also posing new challenges. These developments were backed by new lab measurements of electrical conductivity for mantle minerals at temperatures and pressures that are getting closer to the relevant pressure and temperature conditions in the mantle, alleviating the need for inaccurate extrapolations. The latter enabled more plausible quantitative estimates of water content, melt fractions and temperature in the mantle. In parallel, crust and mantle conductivity models along with developed modelling techniques have become an integral part of geomagnetic field and geomagnetically induced currents (GICs) modelling workflows, establishing new inter-disciplinary knowledge domains.

Two-dimensional dense seismic ambient noise array techniques have been widely used to image and monitor subsurface structure characterization in complex urban environments. It does not have limitations in the layout under the limitation of urban space, which is more suitable for 3D S-velocity imaging. In traditional ambient seismic noise tomography, the narrowband filtering (NBF) method has many possible dispersion branches. Aliases would appear in the dispersive image, and the dispersion curve inversion also depends on the initial model. To obtain high-accuracy 3D S-velocity imaging in urban seismology, we developed a robust workflow of data processing and S-velocity tomography for 2D dense ambient noise arrays. Firstly, differing from the NBF method, we adopt the continuous wavelet transform (CWT) as an alternative method to measure the phase velocity from the interstation noise cross-correlation function (NCF) without 2π ambiguity. Then, we proposed the sequential dispersion curve inversion (DCI) strategy, which combines the Dix linear inversion and preconditioned fast descent (PFD) method to invert the S-velocity structure without prior information. Finally, the 3D S-velocity model is generated by the 3D spatial interpolation. The proposed workflow is applied to the 2D dense ambient seismic array dataset in Changchun City. The quality evaluation methods include residual iteration error, horizontal-to-vertical spectral ratio (HVSR) map, and electrical resistivity tomography (ERT). All tests indicate that the developed workflow provides a reliable 3D S-velocity model, which offers a reference for urban subsurface space exploration.

The estimation of elastic properties of thin-bed formations from sonic logging is challenging. Standard slowness processing of sonic logging waveforms typically yields an average slowness log profile over the span of the receiver array, obscuring thin-layer features smaller than the array aperture. In order to enhance vertical resolution of the slowness logs, the subarray processing techniques have been developed. However, for the subarrays with smaller aperture, the semblance from subarray waveforms becomes susceptible to noise, which results in a low signal-to-noise (S/N) ratio for the processing slowness logs. To overcome the above drawbacks, we propose a slowness estimation method with the enhanced resolution ranging from the conventional array aperture resolution to the inter-receiver spacing based on the reconstruction of neighboring virtual traces (RNVTs). The method utilizes super-virtual interferometry to reconstruct a large number of waveforms for slowness extraction using redundant information from overlapping receiver subarrays. We validate the feasibility and effectiveness of the proposed method using synthetic numerical experiments. By adding different levels of noise to synthetic data, we conclude that the new method has better noise robustness. Finally, we apply this method to field data, and the estimated high-resolution slowness logs show good agreement in interbedded sand-shale sequences. Both numerical tests and examples of field data show that, the slowness logs estimated by the new method can be obtained with a high resolution as well as with a high S/N ratio, providing an effective method for assessing slowness properties from a borehole.

The near surface is characterized by using different numerical techniques, among them seismic techniques that are non-destructive. More particularly, for a better understanding of acoustic and seismic measurements in unconsolidated granular media that can constitute the near surface, many studies have been conducted in situ and also at the laboratory scale where theoretical models have been developed. In this article, we want to model such granular media that are difficult to characterize. At the laboratory scale, dry granular media can be modelled with a homogenized power-law elastic model that depends on depth. In this context, we validate numerically a similar power-law elastic model for such media by applying it to a homogenized elastic medium or to the solid frame of a poroelastic medium that consists of solid and air components. By comparing the response of both rheologies, we want to highlight what poroelastic media can bring to better reproduce the experimental data in the time and frequency domains. To achieve this objective, we revisit studies carried out on unconsolidated granular media at the laboratory scale and we compare different models with different rheologies (elastic or poroelastic), dimensions (2D or 3D), boundary conditions (perfectly matched layer/PML, or Dirichlet) and locations of the source (modelled as a vibratory stick or a point force) in order to reproduce the experimental data. We show here that a poroelastic model describes better the amplitudes of the seismograms. Furthermore, we study the sensitivity of the seismic data to the source location, which is crucial to improve the amplitude of the signals and the detection of the different seismic modes.

The retrieval of surface waves from ambient noise is important for delineating the solid earth’s near-surface structures, especially in urban environments. Seismic interferometry (SI) with linear arrays is becoming popular in urban areas with abundant anthropogenic noise. However, retrieving the noise correlation functions (NCFs) is usually challenging for a dense linear array under the demand of short-time recordings and the limited number of stations in urban environments. We comprehensively compare the SI and three-station interferometry, and the results show that the convolution-based three-station interferometry can accurately retrieve the NCFs using short-time recordings for dense linear arrays from traffic-induced noise. A synthetic example demonstrates the superiority of the convolution-based three-station interferometry over the traditional SI and the correlation-based three-station interferometry. Results from two field examples validate the convolution-based three-station interferometry for linear arrays deployed synchronously and asynchronously and confirm its advantage for multi-component data. We conclude that the convolution-based three-station interferometry performs better because it makes better use of linear arrays with short-time recordings and retrieves higher-quality NCFs.

Globally, unconventional hydrocarbons, known for the symbiosis of their hydrocarbon source and reservoir, pose significant seismic exploration challenges due to their confined target regions, extensive burial depth, minimal acoustic impedance variation, marked heterogeneity, and strong anisotropy. Over the past decade, electromagnetic (EM) exploration has evolved markedly, improving resolution and reliability, thus becoming indispensable in unconventional hydrocarbon exploration. Focusing on China's application of the controlled source electromagnetic method (CSEM), this review examines the geological and electrical attributes of these reservoirs, notably the low resistivity, high polarization and strong electrical anisotropy of shale gas reservoirs. Despite the demonstrated positive correlation between induced polarization (IP) parameters and reservoir parameters, current methodologies emphasize the IP effect, inadvertently neglecting electrical anisotropy, which affects data precision. Moreover, single-source CSEM methodologies limit the observational components, acquisition density, and exploration area, impacting the accuracy and efficacy of data interpretation. Recently developed CSEM techniques in China, namely wide-frequency electromagnetic method (WFEM), time–frequency electromagnetic method (TFEM), long offset transient electromagnetic method (LOTEM), and wireless electromagnetic method (WEM), harness high-power pseudo-random binary sequence (PRBS) waveforms, reference observation and processing technology, hybrid inversion, and enhancing operational efficiency and adaptability despite the pressing need for multi-functional software for data acquisition. Case studies detail these methods' applications in shale gas sweet spot detection and continuous hydraulic fracturing monitoring, highlighting the immense potential of EM methods in unconventional hydrocarbon sweet spot detection and total organic content (TOC) predication. However, challenges persist in suppressing EM noise, streamlining 3D inversion processes, and improving the detection and evaluation of sweet spots.

Deep seismic reflection (DSR) profiling is an effective technique for mapping subsurface structures. Generally, reflections in DSR data are used to constrain underground structures at the crustal scale. In addition to reflections, surface waves in DSR data can be used to investigate shallow/near-surface structures. In this study, we extracted multimodal dispersion curves and estimated their uncertainties from the DSR data in the Beijing Plain, North China, using the frequency-Bessel transform method. Compared to other surface wave surveys conducted in this area, the dispersion curves obtained from DSR data have a unique frequency band, which enables an accurate image of the structure to a depth of 200 m. The 2-D shear wave velocity model obtained by surface wave inversion is consistent with the borehole data and existing shallow/near-surface geophysical studies, which can effectively resolve the faults in the study area. Given the extensive deployment of DSR surveys worldwide and the potential of DSR surface wave analysis, we believe that the development of DSR surface wave analysis could be highly beneficial.