查看更多>>摘要：Geophysical exploration for concealed orogenic gold deposits is challenging because petrophysical contrasts between the gold lodes and their host rocks are normally subtle. Most greenstone-hosted lode-gold deposits do not contain strong conductors as they normally contain <5 vol% of poorly conductive disseminated arsenopyrite and pyrite. Where there is no outcrop, reliance must be placed on lithological and structural interpretations from aeromagnetic data.Airborne Induced Polarization (AIP) Cole-Cole parameters from helicopter time-domain electromagnetic (HTEM) surveys are demonstrated to be effective in geophysical exploration for concealed gold deposits in the Kabinakagami Lake greenstone belt in Ontario, Canada. Interpretation reveals that most AIP positive responses are associated with clays and all known gold deposits in the study area are in resistive rock formations. Hence, resistivity-scaled chargeability (RSC) can be used to detect weakly chargeable zones within these resistive units.Deep Neural Network (DNN) predictive targeting analysis is utilized to integrate the AIP apparent resistivity, resistivity-scaled chargeability (RSC), EM induction time-constant, and magnetic data. The DNN results imply that the known gold occurrences coincide with high DNN probabilities, and hence exploration targets can be identified based on DNN analysis. From a geological perspective, gold prospects also occur at triple-point junctions between granite intrusions and at probable intersections between potentially hosting shear zones and oblique cross faults or dykes, both common structural geometries for orogenic gold deposits. Consequently, DNN predictive targeting results based both on HTEM and magnetic data, and consistent with expected structural geometries, are proposed as a vectoring tool in the generation of drill targets during exploration for concealed orogenic gold deposits in greenstone belts such as those of the Superior Province, Canada.
查看更多>>摘要：Zeolite is utilized as an effective filling material in a permeable reactive barrier (PRB) at contaminated sites. Evaluation of the long-term performance of such barrier demands thorough understanding of the remediation mechanisms and real-time monitoring technique. In this study, the processes of zeolite remediation of three typical major contaminants from municipal solid waste sites (Chemical Oxygen Demand (COD), NH4+, and Pb2+, respectively) were simulated with column test, analyzed with microscopic methods (MIP, BET, SEM, XPS, FTIR, and XRD) and monitored with the spectral induced polarization (SIP) technique. Zeolite remediates COD simulant, C8H5O4, through cation exchange of K+ and surface complexation of C8H5O4-. Patchy distribution of K+ with size range of 16-60 mu m was both observed and calculated from the peak frequency of imaginary conductivity of SIP responses, which was attributed to the preferential complexation of C8H5O4-at low velocity regions (dents) on zeolite surfaces. Zeolite remediates NH4+ mainly through ion exchange due to the higher affinity of NH4+ to zeolite surfaces than that of Na+. Zeolite remediates Pb2+ mainly through surface complexation, with clusters of complexed Pb (II) of wide size range (a few mu m to 25 mu m) residing on zeolite particle surfaces. The real conductivity of SIP responses for zeolite under contaminant flow-through is primarily sensitive to the ionic strength of influx fluid. A mean size of near 270 mu m was calculated from SIP responses in all three cases, which was attributed to the pore throat of zeolite. Spectral induced polarization demonstrated its capability of monitoring three processes of zeolite remediating COD, NH4+, and Pb2+, respectively, and held promise of non-invasively monitoring of long-term performance of contaminant containing barriers.
查看更多>>摘要：How to determine the wavelet of air gun source and how to improve the performance of air gun source have gradually become two important topics in the field of air gun. The existing performance evaluation system of air gun array can only evaluate the performance of air gun from a single point of view, and can not characterize the change of three-dimensional acoustic field produced by air gun array. We first use the Van der Waals air gun wavelet model to simulate the three-dimensional air gun spatial wave field under real conditions. The experimental and modeled data are in good agreement at different pressure amplitudes and frequencies. Then, based on the three-dimensional spatial acoustic field simulated by the model, we proposed a facial characteristics to evaluate the time domain and frequency domain characteristics of the three-dimensional acoustic field. In this air gun wavelet evaluation system, we extend the evaluation parameters such as zero peak, peak-to-peak amplitude, bubble period and main bubble ratio from single wavelet to acoustic field, from one dimension to three dimension. By introducing the instantaneous property of wavelet, we can observe the amplitude, phase and frequency of the far-field wavelet from different angles. The experimental results show that the proposed air gun wavelet evaluation system can evaluate the spatial wave field from any Angle with multiple parameters, and effectively guide the optimization of the structure of the air gun array.
查看更多>>摘要：The ill-posed feature is one basic attribute of geophysical inversion methods. As an example of geophysical inverse problem, AVA (Amplitude variation with incident angle) inversion of pre-stack seismic data is susceptible to noise and uncertainty in the acquisition. To get stable and accurate inversion results, the regularization constraints on model parameter need to be added into the objective function of AVA inversion. In AVA inversion, the most commonly used regularization includes sparsity constraint (e.g. L1-norm regularization, Cauchy regularization) and a priori model parameters constraint, and so forth. However, the existing AVA inversion methods do not consider the structural similarity of different model parameters. All of the different model parameters represent the same underground geological structure, so they should have similar structure. This paper adopts the cross gradient to measure the structural similarity of different model parameters. Next, the cross gradients of different model parameters are added into the objective function of AVA inversion as a regularization term to implement structural similarity constraint. Results of the model numerical tests and real seismic data indicate that the AVA inversion with cross-gradient constraint has higher stability compared to the AVA inversion without structural similarity constraint, especially for the density inversion results.
查看更多>>摘要：The trans-dimensional Bayesian inversion method based on statistical theory regards the inversion parameters as random variable, it not only provides a reasonable inverse model based on the probability, but also offers the probability distribution and the uncertainty information of the inverse model parameters. However, for the conventional trans-dimensional Bayesian inversion, the model sampling efficiency and inversion convergence rate are influenced by several factors, and it requires sampling over a big model space, those limit the application of the trans-dimensional Bayesian inversion for airborne time-domain electromagnetic data. In this paper, we present a novel trans-dimensional Bayesian inversion strategy for airborne time-domain electromagnetic data, which uses a combining sampling update method to implement the model sampling. This method uses the block wise updating method to run only one Markov chain with the initial state constructed by conductivity-depth imaging during the burn-in period, and adopts the component-wise updating method with an adaptive sampling step size to perform multiple Markov chains in parallel after the burn-in period ends. The effectiveness of the novel Bayesian inversion strategy was validated by both synthetic data and survey data. The experimental results showed that this inversion strategy can not only obtain better inversion results, but also shorten the burn in period and reduce the total sampling times of the Markov chain.
查看更多>>摘要：How to suppress the background noise and also recover signals is a widely-concerned and long standing problem in the field of seismic data processing. Effective seismic denoising methods can significantly enhance the quality of seismic data and its signal-to-noise ratio (SNR). Recently, deep-learning-based denoising methods have developed rapidly and achieved more remarkable results than traditional methods. To follow this promising trend and further reinforce the denoising performance, we propose a progressive denoising network (PDN) for land prestack seismic data and apply it to suppress the random noise and surface waves. This proposed PDN contains a feature extraction sub-network and a layer-by-layer denoising sub-network. With the cooperation of the two PDN achieves the layer-by-layer accurate separation of signals and noise according to the difference of low and high features extracted by applying continuous convolution operations. In addition, we utilize both synthetic and real seismic data to construct a rich training dataset with high authenticity and then adopt random-patch-based method to fed the network. The denoising result of synthetic example indicates the excellent attenuation performance of random noise by using PDN. In real example, PDN removes the random noise and surface waves from real land prestack seismic data simultaneously. Furthermore, compared with two existing deep-learning-based denoising methods, PDN has a stronger ability to recover weak reflections.
NSTL
Elsevier