Geophysical Prospecting最新文献

This paper explores the application of machine learning techniques, specifically deep learning, to the inverse problem of marine controlled-source electromagnetic data. A novel approach is proposed that combines the convolutional neural network and recurrent neural network architectures to reconstruct layered electrical resistivity variation beneath the seafloor from marine controlled-source electromagnetic data. The approach leverages the strengths of both convolutional neural network and recurrent neural network, where convolutional neural network is used for recognizing and classifying features in the data, and recurrent neural network is used to capture the contextual information in the sequential data. We have built a large synthetic dataset based on one-dimensional forward modelling of a large number of resistivity models with different levels of electromagnetic structural complexity. The combined learning of convolutional neural network and recurrent neural network is used to construct the mapping relationship between the marine controlled-source electromagnetic data and the resistivity model. The trained network is then used to predict the distribution of resistivity in the model by feeding it with marine controlled-source electromagnetic responses. The accuracy of the proposed approach is examined using several synthetic scenarios and applied to a field dataset. We explore the sensitivity of deep learning inversion to different electromagnetic responses produced by resistive targets distributed at different depths and with varying levels of noise. Results from both numerical simulations and field data processing consistently demonstrate that deep learning inversions reliably reconstruct the subsurface resistivity structures. Moreover, the proposed method significantly improves the efficiency of electromagnetic inversion and offers significant performance advantages over traditional electromagnetic inversion methods.

Reappraisal of legacy reflection seismic data has shown to deliver value in mineral exploration, particularly in brownfield settings. In this work, we demonstrate how the reappraisal and processing of legacy reflection seismic data can be advantageous in the mineral exploration industry. We use today's standard seismic processing tools to improve the imaging of deep and complex geological structures that host mineral deposits. The recovered and processed 25.3 km long legacy seismic profile in this study was acquired in 1983 by the Gold Division of Anglo-American as part of the Witwatersrand Gold Fields exploration program. This study aims to improve the imaging of the Ventersdorp Contact Reef gold-bearing horizon (termed reef), a world-class gold deposit (2 m thick) situated at depths between ∼2400 and ∼4100 m below the ground surface near South Deep Gold Mine in Fochville, South Africa. The final processing results from the pre-stack time and phase-shift migration approaches clearly reveal a dipping reflection associated with the gold-bearing horizon and major steeply dipping faults that crosscut and displace the deposit. The final results are integrated with borehole information, 1D synthetic modelling and aeromagnetic data to constrain the structural interpretation. In particular, 1D synthetic simulation and borehole data constrain the depth position of the gold deposit. The magnetic data provides additional constraints on the complex faulted blocks of the host rocks such as the intrusions that may have a direct impact on ore resources and evaluation. The mining companies, such as South Deep Gold Mine, operating closer to the seismic profiles can use this new structural information to update the current geological models and improve future mine planning and designs, thus providing some insight into the prospectivity of unmined ground.

Estimation of the porosity of a hydrate reservoir is essential for its exploration and development. However, the estimation accuracy was usually less certain in most previous studies that simply assumed that there is a linear relationship between the porosity and single-elastic wave velocities or other rock physical parameters, thus affecting the evaluation of the reserves. In the three-phase Biot-type equations that are fundamental to model a hydrate-bearing reservoir, porosity, alongside hydrate saturation, mineral constituent proportions and hydrate–grain contact factor, is non-linearly responded by density, compressional and shear wave velocities. To improve porosity estimation, we propose to invert simultaneously four-parameter (porosity, hydrate saturation, mineral constituent proportions and hydrate–grain contact factor) using an iteratively nonlinear interior-point optimization algorithm to solve a nonlinear objective function that is a summation of the squared misfits between the well log and three-phase Biot-type equation–modelled density, compressional and shear wave velocities. A test in Mount Elbert gas hydrate research well was conducted for the case of a gas hydrate stratigraphic test well where elastic wave velocities, density, porosity and mineral composition analysis data are available. The four-parameter inversion yielded a lower root mean square error for porosity (0.0245) across the entire well-logging section compared to previous estimations from the linear relationship, post-stacked and pre-stacked seismic traces as well as the pore-filling effective medium theory model applied to other well cases. Additionally, the other three parameters demonstrated good agreement with well logs. Inversion tests conducted at three additional hydrate sites also produced accurate results. Consequently, the new method surpasses previous approaches in porosity estimation accuracy.

The extension of the seismic bandwidth to lower frequencies enhances impedance contrasts that can be poorly represented by the broadband acquisition wavelet. Furthermore, long filters that are used to shape the wavelet of processed data can cause issues with noise, phase and interference between seismic events. In this paper, we use a mathematical technique known as mollification to resolve impedance variations with the highest detail allowed by the bandwidth of the data. The mollifier is integrated and windowed to match the low-frequency content of the data to yield a convenient conversion to relative impedance. Synthetic data created from wedge models show that the windowed mollifier provides an improved representation of the impedance profile. This is replicated by application to an acoustic well log and a regular seismic dataset recorded in the Southern North Sea as well as a broadband dataset recorded in the North Sea.

This study conducts numerical simulations using the boundary integral equations method to model electric resistivity tomography for investigating an embankment dam. Computational speed is enhanced by formulating the direct problem in integral equations and employing the Fourier transform along the strike direction, thereby reducing the 2.5D problem to a set of 1D integral equations. Synthetic data obtained for potential installations are interpreted using the inversion programme ZondRes2D. The impact of varying dam slope angles and the resistivity of interfacing media on interpretation outcomes is systematically evaluated across different dam models. Main geometric anomalies associated with the three-dimensional structure of the dam are described.

The elastic properties and wave propagation of porous rocks are sensitive to the stress variation. The existing theories mainly focus on the impacts of effective stress, confining and pore pressures. The physics of uniaxial stress effect on rock elasticity and wave propagation is seldom well studied, although the uniaxial stress case is frequently encountered in several scenarios, such as the laboratory loading and the subsurface tectonic deformation. Therefore, we propose a new dual-porosity model to describe the effect of uniaxial effective stress on elastic properties of dry porous rocks, based on the Palmer equation and Shapiro dual-porosity model. The Gurevich squirt-flow model is then incorporated to model the dispersion and attenuation of wave velocities of fluid-saturated porous rocks. Modelling results show that the increase of uniaxial effective stress inflates the P- and S-wave velocities along the stress direction until the velocities asymptotically reach their maximum values within the elastic limit. However, the relevant wave dispersion and attenuation gradually decline with the elevating stress possibly due to the gradual closure of cracks. The effect of viscosity, fluid modulus and crack aspect ratio on wave dispersion is investigated in detail as well. By comparing our model to the published laboratory ultrasonic measurements, we confirm the validity of our model. Furthermore, our dual-porosity model is used to establish a rock-physics approach to estimate the wave velocities with the well-logging data. The well-logging examples show the reasonable agreement between the predicted results and real data, illustrating the feasibility of our approach.

Time domain finite difference methods have been widely used for wave-equation modelling in exploration geophysics over many decades. When using time domain finite difference methods, it is desirable to use a larger time step so as to save numerical simulation time. The Lax–Wendroff method is one of the well-known methods to allow larger time step without increasing the time grid dispersion. However, the Lax–Wendroff method suffers from more time consumption because there are more spatial derivatives required to be approximated by the finite difference operators. We propose a new finite difference scheme for the Lax–Wendroff method so as to reduce the numerical simulation time. Then we determine the finite difference operator coefficients and analyse the dispersion error of the proposed finite difference scheme for the Lax–Wendroff method. At last, we apply the proposed finite difference scheme for the Lax–Wendroff method to different velocity models. The numerical simulation results indicate that the proposed finite difference scheme for the Lax–Wendroff method can effectively suppress time grid dispersion and is more efficient compared to the traditional finite difference scheme for the Lax–Wendroff method.

Based on the elastic wave equation, a pseudoelastic pure P-mode wave equation has been recently derived by projecting the wavefield along the wavefront normal direction. This pseudoelastic pure P-mode wave equation offers an accurate simulation of P-wave fields with accurate elastic phase and amplitude characteristics. Moreover, considering no S-waves are involved, it is computationally more efficient than the elastic wave equation, making it an excellent choice as a forward simulation engine for P-wave exploration. Here, we propose a new pseudoelastic pure P-mode wave equation and apply the stress image method to it to implement the free surface boundary condition. The new pseudoelastic wave equation offers significantly improved computational efficiency compared to the previous pseudoelastic wave equation. Additionally, the wavefields simulated by this new pseudoelastic wave equation exhibit clear physical interpretations. We evaluate the accuracy of the new wave equation in simulating elastic P-waves by employing a model with high-velocity contrasts. We find that this new equation, which purely admits P-waves, though having exact amplitude and phase behaviour as the elastic waves for transmission components, the amplitudes slightly suffer in the scattering scenario. The difference in amplitude between the elastic and our pseudoelastic increases as the contrast in velocity at the interface (interlayer velocity ratio) increases, especially the S-wave velocities. This has negative implications on scattering from the free surface boundary condition or the sea bottom interface, especially if the shear wave velocity below the surface or the sea bottom is high. However, in cases where, like for land data in the Middle East, the transition to a free surface is smoother, the accuracy of the pseudoelastic equation is high. In all cases, regardless of the interlayer velocity ratio, the accuracy of the pseudoelastic wave equation in simulating the elastic case, for scattered waves, exceeds that of the acoustic wave equation in phase and amplitude.

Time-lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time-lapse seismic exploration has been conducted using a large-scale exploration system. However, for efficient monitoring of shallow subsurface, time-lapse monitoring based on the small-scale exploration system is required. Small-scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal-to-noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small-scale time-lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre-stack, post-stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre-stack data processing stage. In the post-stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high-resolution time-lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.