Journal of Applied Geophysics最新文献

Distributed acoustic sensor (DAS), which utilizes the entire optical fiber as the sensing medium, provides distinct advantages of high resolution, dynamic monitoring, and resistance to high temperatures. This technology finds diverse applications in the seismic exploration, oil survey, and submarine cable monitoring industries. However, DAS signals are susceptible to various kinds of noise, such as horizontal noise, erratic noise, random noise, and so on, which significantly degrade the SNR. This low SNR is likely to affect some subsequent analyses, such as inversion and interpretation. The mixed noises feature of the DAS data poses a serious challenge for SNR enhancement. To address this issue, we develop a supervised learning-based densely connected residual convolutional denoising network (DCRCDNet), which leverages both encoding and decoding processes to extract features and reconstruct DAS data. The design of dense connectivity and residual blocks allow the network to extract both shallow and deep features. The network is trained using both synthetic and field data to obtain the optimal network parameters. Testing on synthetic data demonstrates that DCRCDNet improves the signal-to-noise ratio (SNR) from −10.21 dB to 15.61 dB. The test results from both synthetic and field data indicate that, compared to traditional filtering methods and other deep learning approaches, this network effectively suppresses noise in DAS signals. Consequently, DCRCDNet shows great potential in reconstructing DAS signals from hidden noise, suppressing strong and mixed noise, and extracting hidden signals.

A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir is modeled as a coupled poroviscoelastic–viscoelastic medium, and the underlying wave propagation problem is solved using a three-dimensional discontinuous Galerkin method coupled with an Adams–Bashforth time stepping scheme. The wave problem solver is used to generate databases for the neural network-based machine learning model to estimate the water volume. In the numerical examples, we investigate a deconvolution-based approach to normalize the effect from the source wavelet in addition to the network's tolerance for noise levels. We also apply the SHapley Additive exPlanations method to obtain greater insight into which part of the input data contributes the most to the water volume estimation. The numerical results demonstrate the capacity of the fully connected neural network to estimate the amount of water stored in the porous storage reservoir.

The Closed-Loop surface-related multiple elimination (CL-SRME) shares the common theoretical foundation with traditional surface-related multiple elimination (SRME). Nevertheless, it introduces an inversion-based approach to avoid the adaptive subtraction process in SRME, aiming to prevent the energy damage to the primaries that may occur when they interfere with multiples during multiple suppression. With the advancements of computing power, the seismic data for processing has evolved from 2D to 3D. However, traditional 2D algorithms are no longer sufficient to effectively suppress surface-related multiples in 3D data. Consequently, based on the theories of 3D SRME and 2D CL-SRME, the 3D CL-SRME algorithm is proposed in this study. Moreover, the implementation of the CL-SRME necessitates numerous matrix operations and frequent data conversions between the time domain and frequency domain, resulting in colossal computational costs. Therefore, a GPU acceleration strategy is introduced to address this challenge. Numerical examples of 3D seismic data demonstrate that 3D CL-SRME can provide higher accuracy of multiple suppression and wider adaptability to complex 3D cases. Simultaneously, the graphics processing unit (GPU) parallel computing can substantially enhance the computational efficiency. This study employs a novel approach that achieves significant improvements in performance and accuracy for surface-related multiple elimination tasks in 3D applications. The combination of its closed-loop approach and GPU acceleration renders it a valuable tool for 3D multiple suppression, enabling high-precision multiple suppression with less computational cost.

Active and passive surface-wave methods have garnered significant attention in the near-surface geophysical community for their non-destructive, non-invasive, low-cost, and accurate advantages in delineating subsurface shear (S)-wave velocity structures. They are increasingly being utilized to address numerous engineering and environmental problems. Surface waves obtained from actively excited sources such as a hammer and a harmonic shaker, however, lack low-frequency components, resulting in limited investigation depth. Conversely, passive surface waves such as microseisms (< 0.4 Hz, associated with natural ocean wave activity) and microtremor (>1 Hz, generated by cultural and wind sources) retrieved from ambient seismic noise typically lack high-frequency components, which is not conductive to characterizing fine near-surface structures. To overcome these frequency limitations, we employ a “mixed-source data” strategy, imposing active shot gathers into ambient noise data, to widen the frequency range of dispersion images and depth of investigative capabilities. We simulate both active and passive surface-wave data based on a two-layer model, noting that their dispersion images suffer from a mode kissing phenomenon at lower frequencies. By analyzing influencing factors such as the amplitude intensity, the signal-to-noise ratio and the excitation locations of active shot gathers, as well as the length of passive surface-wave data, we better understand their impacts on dispersion images from mixed-source surface-wave data. Simulation tests demonstrate that processing mixed-source data can effectively distinguish the mode kissing phenomenon. Moreover, the effectiveness of this strategy in enhancing the quality of dispersion image is verified, especially when surface-wave dispersion images perform poorly in either the low- or high-frequency bands. Additionally, a real-world example further demonstrates that processing mixed-source data offers significant advantages in improving the quality of dispersion images. This way provides a convenient and efficient measurement strategy for delineating shear-wave velocity profiles in finer shallow layers and deeper penetration depths.

The simultaneous source acquisition technology breaks the limitations of traditional seismic survey, which allows more than one source to fire almost at the same time. When the survey time is fixed, simultaneous source acquisition can increase the number of sources, while when the number of sources is fixed, this technique can greatly reduce the survey time. At present, the great advantages of this high-efficiency acquisition technology have received wide attention from academia and industry, and researchers have proposed a series of deblending methods and obtained good results. In recent years, the rapid development of deep learning provides a new solution for deblending, and it has obvious advantages in computational time compared to traditional methods when processing large-scale seismic data. We proposed a novel iterative deblending method based on deep learning, which integrates the advantages of seismic data processing in different domains. In the proposed method, by selecting the appropriate combination of domains, the separation quality is significantly improved compared to the deblended results in a single domain. The effectiveness of the proposed method is verified by deblending the synthetic and field data, and the better performance of the proposed method are demonstrated by comparing it with the multilevel median filter method and conventional deep learning-based methods.

The staggered grid finite difference method (SGFDM) of a monopole source is used to simulate a three-dimensional vug reservoir model and study the effect of acoustic logging responses of different vug models on radial probing depth. The results show that the first arrival of the head wave peak of the compressional wave (P-wave) and the shear wave (S-wave) is unrelated to the radius of the vug, and the amplitude of the head wave peak of the P-wave and S-wave decreases as the vug volume increases. Compared with the volume change of the vug, the radial distance from the vug wall has little influence, while the vertical source distance has large influence on the P-wave and S-wave. When there are multiple vugs in the model, the amplitudes of the P-wave and S-wave head wave peaks change sinusoidally with the angle between the vugs. The ellipsoidal vug model with the same volume has a greater influence on the P-wave and S-wave than the spherical vug model. In the ellipsoidal vug model, the axial vug size has a greater impact on the first arrival of the head wave peak, while the radial vug size significantly influences the amplitude of the head wave peak. Finally, we validate the numerical simulation conclusions by comparing them with actual logging data responses in complex formations, demonstrating the practical value of the elastic wave response simulations for vugs.

This study delves into Horizontal-to-Vertical Spectral Ratio (HVSR) and directional HVSR (D-HVSR) using continuous 30-min ambient seismic noise (ASN) signals recorded at 68 sites in the Chalco lakebed zone of the Valley of Mexico (VM). Our aim is to explore the shallow stratigraphy and subsurface structures in that zone. The isoperiod map using the HVSR, and the ensuing velocity structure inversion reveals a basin of irregular shape and shallow depth in the surroundings. Additionally, we explore azimuthal effects by computing the D-HVSR by rotating the records to obtain the time series for a given direction. We found different polarizations at the measurement points, and focused on the frequencies in which the effect is more pronounced. The cases with significant polarizations at the dominant frequencies suggest an irregularity at the basement level. This is supported by preliminary synthetic results that uses the three-dimensional (3D) Indirect Boundary Element Method (IBEM) to assess the effects of interface irregularities like a channel or dike. In the present stage of development, the D-HVSR is a powerful tool to reveal the existence of lateral variations. The complexity of the lakebed zone response in the VM and its irregular characteristics show the potential of the D-HVSR approach as well as the need to deeply explore into the modeling for its better understanding and interpretation.

Velocity and attenuation (the inverse of the Q factor, Q⁻¹) are fundamental elastic wave attributes used to understand subsurface physical properties and characterize unconventional reservoirs. Mudstone, an abundant sedimentary rock, is commonly regarded as a source rock with great promise for unconventional exploration. Despite this, velocity and attenuation studies are lacking on mudstones and have failed to address the link between these elastic wave attributes and physical rock properties. In this study, we measured the P- and S-wave velocities (Vp and Vs) and Q factors (Qp and Qs) of the Naparima Hill organic-rich mudstone at effective pressures up to 130 MPa. The main aims are to investigate the effects of mineralogy, bulk density, porosity, and saturation on these elastic wave attributes and to evaluate if a relationship exists between velocity and attenuation. Vp and Vs were obtained using the trough transmission method, while the spectral-ratio and rise-time techniques were used to determine Qp and Qs in the frequency range of 0.8 to 1.7 MHz. The results show that the velocities and Q factors rose with increasing carbonate, bulk density, and effective pressure, and decreasing clay, silica, and porosity. The saturated mudstones have lower velocities and Q factors than the dry specimens. Strong, linear relationships (R² > 0.85) were established between Vp and Vs under both dry and saturated conditions. Moderate to strong linear relationships (R² > 0.5) were established between Qs and Qp, Qp and Vp, Qs and Vs, and Qs and Vp under saturated conditions. These relationships create a framework for predicting the elastic wave attributes (Vs, Qp, and Qs), from the readily extractable Vp in mudstones.

At present, the detection of geological structural plane that give rise to rockburst is a complex problem. A large number of rockburst cases show that the geological structural plane influence the boundary of rockburst, especially the slip-type rockburst of structural plane. However, there is no set of comprehensive geological prediction methods at home and abroad for the detection of geological structural plane that give rise to rockburst, or low accuracy and high workload. Therefore, a method that can effectively detect geological structural plane is needed. Based on the analysis of regional engineering geological characteristics and geostress, this paper puts forward that based on the characteristics of rockburst cases in high geostress area and the rockburst prone to occur in the position with large geostress difference, the method of “combination of long and short geophysical prospecting method first, drilling and engineering geological analysis combination” is adopted to detect geological structural plane finely. The results show that: (1) Under the background of regional engineering geological survey and geostress inversion, the comprehensive geological prediction is carried out to detect the geological structural plane. The TSP has the detection accuracy of 10 m to meters, the ground penetrating radar has the detection accuracy of meters to decimeters, and the borehole imaging has the observation accuracy of centimeters. The combination of the three can multi-scale clearly reflect the distribution of geological structural plane compared with the previous single geological prediction. (2) Due to the complex distribution of surrounding rock mass interface and geological structural plane, the collapse phenomenon will also appear in the rockburst area on site, and the collapse causes a number of structural plane, which means that these structural plane becomes an important factor in slip-type rockburst of structural plane. In short, accurate detection of geological structural plane has certain reference significance for predicting the boundary of slip-type rockburst of structural plane and collapse.

Microorganisms play a critical role in hydrocarbon degradation, contaminant sequestration, and pollution monitoring. However, the complex relationships between microbial processes and geological media's physical and chemical properties remain ambiguous. The self-potential (SP) is an efficient, low-cost, and nonintrusive passive geophysical technique suitable for monitoring dynamic activities. Herein, we conducted the 3D monitoring experiments to obtain time-lapse SP signals generated from cultivating typical microorganisms (Shewanella oneidensis MR-1) under laboratory-controlled conditions. The 3D multi-channel SP experimental devices enable dynamic monitoring and measurement of weak signals. At the beginning of the experiment, we observed a rapid increase in SP signals that consist mainly of the streaming potential and the redox potential. The peak values of negative anomalies monitored in the two experiments were − 75.9 mV and − 59.5 mV, respectively. During subsequent monitoring, the abnormal potential signal gradually decreased. After a sufficient period, the amplitude of the SP generated solely by the Shewanella oneidensis MR-1 activities ranged from −45 to −35 mV. Our laboratory research paves the way for developing dynamic model data to link the self-potential response with microbial processes. Then, we inverted the measured SP data to obtain the current source density distribution. The consistency of current density results and anomalous potentials shows that SP data collected by pre-buried non-polarizable electrodes can be utilized as a direct indicator signal for spatiotemporal monitoring of microbial activities. The SP method shows promise in environmental bioremediation and biodegradation.