Fake news detection (FND) is a critical task in natural language processing (NLP) focused on identifying and mitigating the spread of misinformation. Large language models (LLMs) have recently shown remarkable abilities in understanding semantics and performing logical inference. However, their tendency to generate hallucinations poses significant challenges in accurately detecting deceptive content, leading to suboptimal performance. In addition, existing FND methods often underutilize the extensive prior knowledge embedded within LLMs, resulting in less effective classification outcomes. To address these issues, we propose the CAPE–FND framework, context‐aware prompt engineering, designed for enhancing FND tasks. This framework employs unique veracity‐oriented context‐aware constraints, background information, and analogical reasoning to mitigate LLM hallucinations and utilizes self‐adaptive bootstrap prompting optimization to improve LLM predictions. It further refines initial LLM prompts through adaptive iterative optimization using a random search bootstrap algorithm, maximizing the efficacy of LLM prompting. Extensive zero‐shot and few‐shot experiments using GPT‐3.5‐turbo across multiple public datasets demonstrate the effectiveness and robustness of our CAPE–FND framework, even surpassing advanced GPT‐4.0 and human performance in certain scenarios. To support further LLM–based FND, we have made our approach’s code publicly available on GitHub (our CAPE–FND code: https://github.com/albert-jin/CAPE-FND [Accessed on 2024.09]).

Accurately picking the first arrivals of acoustic logging curves (e.g., P-, S- and Stoneley waves) is crucial for stratigraphic lithology characterization. The conventional interpreter-dominated first arrivals identification methods frequently lead to an interpretation uncertainty and time burden. To reduce these deficiencies, we developed a dual attention PhaseNet (DA-PhaseNet) network to intelligently identify the first arrivals of acoustic logging curves. The field data test demonstrates that the DA-PhaseNet network can dramatically improve the result accuracy and its generality compared to other PhaseNet-based methods. Specifically, the DA-PhaseNet strategy can capture both local and global features of input logging curves simultaneously, resulting in a high identification accuracy of 99.4% and 94.5% for P- and S-wave respectively. Moreover, the proposed DA-PhaseNet network dramatically improves the accuracy of first arrival identification from 72.3% to 87.6% for the noise-contaminated Stoneley wave. Furthermore, it is important to mention that the DA-PhaseNet has a maximum noise tolerance of 0 dB for P- and Stoneley waves to ensure accuracy of first arrival identification, while has a maximum noise tolerance level of 10 dB for S-wave if the result F1 score limit is set at a level of > 0.8.

Petrophysical seismic inversion is a challenging problem due to its intrinsic nonlinearity and ill-posedness. Deep learning emerges as a promising solution to tackle this intricate problem, with semi-supervised learning proving particularly valuable in scenarios with limited labeled data. However, many existing semi-supervised learning approaches applied to reservoir parameters inversion are unidimensional or focus on single model parameters, potentially hindering the attainment of highly accurate predictions for multiple petrophysical parameters. To this end, we introduce a novel knowledge-driven semi-supervised deep learning approach for multidimensional petrophysical seismic inversion. This framework features a lightweight 2-D UNet, incorporating prior knowledge about the range of model parameters, to parameterize the set of pseudo-inverse operators, enabling effective multitask learning. By leveraging the low-frequency porosity as the sole initial model input, our approach enhances the information-sharing capabilities of the neural network. We also introduce Hermite cubic splines to parameterize source wavelets varying with angles, ensuring smooth and compactly supported waveforms. In addition, we develop a semi-supervised training loss function that integrates deterministic forward operators and sampling operators, allowing simultaneous updating of weights in both forward and pseudo-inverse operators. The proposed method facilitates the simultaneous inversion of wavelets, porosity, water saturation, and clay volume. Synthetic and field data tests are conducted to validate our approach, demonstrating that it significantly enhances inversion accuracy compared to 1-D semi-supervised deep learning methods.

Accurate classification and characterization of pore-crack structures are substantial to carbonate reservoirs in terms of reservoir exploration and development. Although experience-dominated manually classifying pore-crack structures achieves a milestone, these methods usually encounter significant uncertainties and heavily rely on the interpreter’s experience. Nevertheless, as a classification problem, using the 2D image input dataset, instead of 1D logging data, could achieve a higher accuracy. Consequently, we developed a Swin Transformer-Convolutional Neural Network (SWT-CNN) hybrid network to capture multi-level features of the pore-crack structure simultaneously using 2D resistivity imaging logging images as an input, thereby eliminating the uncertainty of manual interpretation and enabling automatic feature extraction. Furthermore, to fully utilize rare and valuable dataset, the proposed SWT-CNN model incorporates the data augmentation strategy which has been modified to fit the dataset. Also, the idea of transfer learning is introduced to improve the accuracy of pore-crack types classification in carbonate rock and accelerate convergence. Lastly, the field validation data test shows that the proposed SWT-CNN can achieve an accuracy rate of 95.92%. Moreover, the visualization of the feature map indicates the proposed SWT-CNN is more accurate in recognizing the position of pore-crack structures.

Traditional optimization algorithms are usually applied to estimate the elastic parameters of the subsurface by using field seismic data. However, these optimization algorithms highly depend on prior knowledge (e.g., the initial model setup and sparsity), leading to serious inversion uncertainties. Nowadays, with the rapid development of neural networks, convolutional neural networks (CNNs) have been widely imposed on estimating elastic parameters from field data. However, the deficiency of labeled seismic data impedes the CNNs application in seismic inversion. Moreover, both the size and diversity of labeled datasets are also critical factors influencing the accuracy and resolution of predicted parameters when using the CNNs-based inversion techniques. In this work, taking the unconventional tight sandstone formation as an example, we develop a geological and geophysical model driven CNNs (GGCNNs), named as GGCNNs. The proposed GGCNNs allow us to take advantage of both the prior geological information and basic geophysical model from the generated synthetic labeled prestack seismic datasets, representing essential characteristics of the subsurface. Moreover, under the consideration of data diversity, the GGCNNs model enables us to make a tradeoff between the inversion accuracy and labeled data size. Applications on both synthetic and field data clearly demonstrate the effectiveness of the proposed GGCNNs model for predicting elastic parameters by using prestack seismic data, i.e., its predicted results are with high accuracy in the vertical profile and continuity and smooth in the horizon slice.

Accurate estimation of petrophysical properties (e.g., porosity, clay volume) of subsurface rock from seismic data/elastic properties is significant to reservoir characterization. Conventional model-driven inversion strategies for estimating petrophysical parameters confront with the deficiency of prior knowledge. In contrast, machine learning-based approaches are adapted to account for reservoir parameter estimation through developing nonlinear mapping and quantifying uncertainty. However, most of the current researches mainly concentrates on the single parameter prediction with different neural network architectures, which, in turn, conflicts with the truth of coupling multiple reservoir properties. To quantify the sensitivity of input-output parameters and the effects of network architecture on the accuracy of petrophysical parameter inversion, we propose a CNN-based network strategy to estimate multiple reservoir parameters simultaneously. The results from both synthetic labeled data and field data and uncertainty analysis strongly demonstrate that SopenCNN, abbreviated from multiple input and single output openCNN, exhibits the highest prediction accuracy, while the cycleCNN with multiple input and multiple output, referred to as McycleCNN, is superior to the MopenCNN, which means that an openCNN contains multiple input and multiple output. It means that, for similar network architecture, the number of input and output parameters makes a significant impact on prediction accuracy. Moreover, for similar multiple inputs and outputs, the fine-tuning McycleCNN, parallelly updated in each intermediate closed-loop step, behaves much better accordingly. The application of the three workflows with varying architecture on field tight sandstone reservoirs demonstrates that network based inversion strategy could establish a mapping function to characterize spatially varying reservoir parameters.

Deep learning has achieved promising results for impedance inversion via seismic data. Generally, these networks, composed of convolution layers and residual blocks, tend to deliver good results with deep architectures. Nevertheless, deep networks accompany a large number of parameters and longer training time. The volume of seismic data, especially 3D scenarios, is very large. Therefore, it is particularly important to improve the accuracy while ensuring the model efficiency for practical implementation. With the flourishing new modules and techniques, deep learning has set the state-of-the-art in many applications across wide range of scientific and engineering disciplines. In this paper, we present Residual Attention Network (ResANet), a CNN incorporating with residual modules and two attention mechanisms: channel-wise attention and feature-map attention, for seismic impedance inversion. The proposed network can fuse multi-scale channel information and recalibrate channel-wise feature responses as well as receptive fields adaptively. At the same time, ResANet adopts grouped convolution, dilated convolution and dropout techniques to improve the computation efficiency and stability. Marmousi2 synthetic model and field data test results show that the proposed network outperforms several comparable neural networks in accuracy and generalization ability while ensuring efficiency for seismic data impedance inversion. For the field data test, transfer learning is also evoked to further improve the performance. ResANet tends to predict impedance with high resolution and strong lateral continuity compare with three closely related networks. The accuracy of ResANet is improved by 1 to 2 orders of magnitude on the 6 well logs provided in field dataset tests compare with commercial software (InverTrace Plus module in Jason) using Constrained Sparse Spike Inversion (CSSI) method.