Geophysical Prospecting最新文献

The success of horizontal loop electromagnetic method in determining ore body model parameters is intricately tied to the inversion process. However, geophysical inverse problems often grapple with ill-posed and non-unique mathematical difficulties. To handle this situation, we presented two new metaheuristics: the Barnacles Mating Optimizer and the Hunger Games Search algorithm. This pioneering approach is the first in the literature to employ both optimizers simultaneously to invert electromagnetic anomalies. To test the core functionality of these inversion tools, we simplified the horizontal loop electromagnetic problem by using a single frequency. Before carrying out the data inversions, a synthetic anomaly caused by a simple multi-source model was used to perform some modal analyses. These analyses revealed the composite modality character of the inversion process and highlighted the necessity of an algorithm that effectively balances global exploration (thoroughly searching the model space while avoiding local minima) and local exploitation, which focuses on approximating the global optimum closely. In addition, the findings indicated the importance of conducting post-inversion analyses to evaluate potential uncertainties in the estimations. Initial experiments with nine challenging benchmark functions revealed that Hunger Games Search outperforms in tackling optimization problems. The performances of both algorithms were then examined using synthetic horizontal loop electromagnetic anomalies and two field data sets from Australia. To achieve a balanced comparison, we fine-tuned the control parameters of both algorithms, optimizing their performance under comparable conditions. These assessments provided valuable insights into the effectiveness of each algorithm when properly configured. Uncertainty appraisal analyses mostly confirmed the consistency and mathematical consistency of the obtained solutions. In the real data inversion cases, the outputs of Hunger Games Search showed strong agreement with the drilling data. This study illustrates the stronger performance of the Hunger Games Search algorithm relative to Barnacles Mating Optimizer in the presented scenarios and demonstrates its effectiveness in estimating source parameters using horizontal loop electromagnetic anomalies.

Seismic data interpolation techniques are vital for preprocessing, as spatial undersampling in seismic data presents processing challenges. Recently, multiple deep learning–based interpolation techniques have emerged, each catering to distinct missing data scenarios, including regular, irregular or large gaps. However, this standardized approach can induce a creeping overfitting issue in terms of various missing types, notably undermining the generalization capability of trained deep learning models. It is worthy of serious consideration for performance generalization of deep learning–based trace interpolation in terms of various missing patterns. This study introduces an innovative approach, redefining deep learning–based seismic data interpolation to focus on enhancing generalized performance be treating unseen data. We highlight how data biases in the training dataset substantially impair interpolation performance on target data with varying features. Then we offer some guidelines to counter these biases during training dataset construction. Furthermore, we propose a versatile, single deep learning model applicable to any case of missing data in real-field situations, utilizing U-Net3+ as the backbone. Experiments using field data considering various missing scenarios reveal that our method excels in interpolating unseen target data; it does this by using an unbiased dataset, bolstering general interpolation performance. This study emphasizes the importance of a systematically designed training dataset to augment generalization in deep learning–based interpolation and indicates the need for more comprehensive research to create a universally applicable deep learning–based seismic data interpolation network for practical use.

Identification of subsurface geological profiles is indispensable to geotechnical design and construction. Subsurface stratification through Bayesian inversion of soil behaviour type index data, obtained from cone penetration tests, is achieved through the development of a novel three-block Markov chain Monte Carlo algorithm. Working in a trans-dimensional context, where the number of layers, layer depths and soil random field parameters are unknown, the algorithm is able to estimate the range of non-unique solutions or the uncertainty of these parameters. A blocking strategy has been applied that allows for the development of a formulation that primarily involves computationally inexpensive tasks such as sampling from truncated normal and Inv-Gamma distributions and evaluation of general normal densities. Part of this strategy involves the design of a novel proposal density for jumping between parameter spaces of different dimensions in the reversible jump Markov chain Monte Carlo applied in the first block. Optimal sampling in trans-dimensional problems with a single reversible jump Markov chain using random walk Metropolis–Hastings proposals is often difficult and requires ad hoc concatenation of multiple independent chains or sophisticated methods like parallel tempering or delayed rejection. The formulation presented in this study renders the conditional posterior density over the mean of the random field representing the soil parameters to be analytical, thereby allowing the corresponding proposals to be made directly from the conditional posterior. Hence, unlike most other existing algorithms, we avoid random walks altogether by sampling from the conditional posterior distribution directly. The algorithm is validated using synthetic and real soil behaviour type index data from benchmark problems. A standard normality check of the decorrelated residuals is used as a measure to test algorithm performance. Results show that the algorithm is able to identify the soil stratification parameters and random field properties correctly and also identify their uncertainties.

When using electrical prospecting methods to monitor the front edge of waterflooding in the residual oil development, the electromagnetic responses generated by the high conductivity of the steel casing in the observation area usually obscure the electrical abnormal signal of the formation caused by waterflooding, largely affecting the imaging accuracy. Considering the densely distributed casing wells in the working area, we propose a novel method that steel casing serves as paths not only for transmitting, but also transferring the waterflooding front signals to the observations that are carried out on the surface. As the electric signals going through the steel casing have nearly zero attenuation, we can effectively observe the weak electric signals of underground waterflooding fluids at the surface receivers. To achieve a high-precision simulation of steel casings, we used an electrical conductivity equivalent method to overcome the problem of excessively large comparative geometrical dimensions. Meanwhile, we utilized unstructured tetrahedral grids to accurately model the irregular shape of waterflooding fluids migration. Based on numerical experiments, we analysed the effects of source type, location, and waterflooding depth on the conduction effect of steel casing, confirming that our method can effectively monitor the migration of waterflooding and capture the boundary of the waterflooding front edge.

The brittleness index is a significant indicator for forecasting the characteristics of coalbed methane reservoirs. However, the brittleness index of coalbed methane reservoirs is susceptible to a number of influencing factors, including the pore structure, coalbed methane content and pore fluid. Moreover, coalbed methane reservoirs are characterized by vertical transverse isotropy anisotropy, which presents a significant challenge to the accurate prediction of brittleness. Accordingly, this paper presents a new rock physics model for coalbed methane reservoirs, based on experimental test data obtained from coal samples. This model considers the vertical transverse isotropy anisotropy characteristics of coalbed methane reservoirs and the influence of pore structure, adsorbed gas and pore fluid in coal. The accuracy of the model is corroborated by the results of the experimental tests. The model predictions indicate that organic matter and clay content exert a greater influence than the stratification indicator factor. The impact of content is smaller than that of pore structure, and the influence of coalbed methane increases following water saturation. The model may be employed to forecast the elastic anisotropy and brittleness index of vertical transverse isotropy-type coalbed methane reservoirs. The inversion results of the logging data indicate that the brittleness index of coalbed methane reservoirs exhibits some variation in different directions and the brittleness index is greater along the bedding direction. The study assists in elucidating the interrelationship between the rock physical parameters of coalbed methane reservoirs and the brittleness index of coal seams, thereby furnishing a basis for anticipating the anisotropic sweet spot in coalbed methane reservoirs.

The attenuation property of earth media can lead to amplitude loss and phase dispersion effects on multicomponent elastic data. Ignoring their impacts during imaging process will result in blurred and dislocated imaging profiles. To accurately characterize the attenuation effect in viscoelastic media, we first derive a new viscoelastic wave equation with decoupled fractional Laplacians. Numerical tests show that the proposed wave equation can accurately capture the propagation characteristics of seismic waves in viscoelastic media. Furthermore, our new wave equation can be modified to yield a decomposition equation, which enables the separated propagation of vector P- and S-wavefields. Building on the derived viscoelastic forward propagator, we develop a stable Q-compensated viscoelastic reverse-time migration approach. Usually, the inner product imaging condition is used to obtain imaging results. However, the result of inner product is affected by the angle between vectors, making the resulting images contaminated with the angle information. In this article, we introduce the magnitude- and sign-based imaging condition for PS imaging and conduct a cross-correlation imaging condition based on the scalar P-wavefield for PP imaging. In contrast to the inner product imaging condition, our imaging scheme is capable of overcoming the contamination by the angle information. In addition, high-frequency noise is amplified exponentially during the attenuation compensation process, affecting imaging precision. To address this problem, we derive the stabilized Q-compensation wave equations explicitly for vector- and scalar wavefields. Numerical examples demonstrate that the proposed Q-compensated viscoelastic reverse-time migration method can effectively correct the viscoelastic effects, yielding high-quality PP- and PS-imaging profiles.

We introduce a novel approach for seismic pre-stack data regularization using the common-offset common-reflection-surface method with a global attribute search strategy, employing a bio-inspired differential evolution optimization algorithm. We compare the global common-offset common-reflection-surface approach with the conventional sequential attribute search, focusing on their performance in pre-stack data regularization. Tests on synthetic and onshore (field) seismic datasets demonstrate that the global search approach significantly improves performance, enhancing signal-to-noise ratio and coherently filling missing traces. We show that the global common-offset common-reflection-surface method effectively addresses spatial irregularities, reconstructing reflections without artefacts, filling data gaps and highlighting geological details even in complex areas. In contrast, the sequential common-offset common-reflection-surface method, while capable of reconstructing missing traces, shows lower interpolation quality and fails to adequately highlight complex geological features such as high-dip reflections.