The Internet of Bio-Nano Things (IoBNT) integrates biosensors, nanorobots, and molecular communication, significantly extending the functionality of traditional IoT systems on a nano-scale. It holds promise for targeted drug delivery and real-time health monitoring applications. However, IoBNT faces critical challenges, including high delay, low network reliability, and congestion, primarily due to biological environments’ complex and dynamic nature. DNA emerges as an ideal information carrier for IoBNT due to its high information density, longevity, biocompatibility, and robustness against environmental interference. These properties make DNA uniquely suited for reliable and efficient communication within IoBNT, with additional functionalities in bio-sensing and DNA computing. This paper proposes AoI-OptiIoBNT, an innovative routing and packet forwarding strategy designed to optimize DNA-based information flow in IoBNT. AoI-OptiIoBNT combines an Age of Information (AoI)-driven approach with a Markov Decision Process (MDP)-based routing algorithm to mitigate delay and congestion. It incorporates a multi-retransmission strategy to enhance network reliability and introduces a Yin-Yang Coding (YYC) mechanism to reduce error rates and improve decoding accuracy. Simulation results demonstrate that AoI-OptiIoBNT substantially improves the efficiency, reliability, and overall performance of IoBNT networks. It offers a robust framework for addressing congestion, packet loss, and delay, making it a promising solution for advancing IoBNT applications.

The thriving domain of the Internet of Bio-Nano Things (IoBNT) promises revolutionary advances in biomedicine, enabling biosensing, health monitoring, and therapeutic capabilities at the cellular level. A pivotal challenge, however, lies in devising reliable, efficient communication mechanisms within this bio-nano realm. This article introduces an emerging DNA-based molecular communication (MC) system utilizing a novel track-hopper mechanism that significantly enhances precision and control in molecular cargo transport. By leveraging DNA strands for information encoding and cargo transport, our track-hopper-based MCs (THMCs) IoBNT system achieves a symbiosis of high reliability, low delay, and precise directional control, surpassing traditional diffusion and motor-based methods. Through extensive theoretical analysis and simulation of network topology’s link and node response functions, we demonstrate the system’s superior performance in network delay and reliability metrics, underpinning its potential to redefine communication within IoBNT for applications ranging from health monitoring to disease detection. Our findings illuminate a path forward in bio-nano information exchange, offering a robust framework for the next generation of IoBNT systems.

The time–frequency analysis methods are powerful tools in seismic interpretation and bright spot identification. The S-transform (ST), as a hybrid of the short-time Fourier transform (STFT) and continuous wavelet transform (CWT), can achieve progressive time–frequency resolution. However, limited by its linear-frequency-dependent term, the ST obtains a deviated frequency distribution. By removing this term, the frequency form of unscaled ST (FUST), as a variation of the ST, is proposed to preserve the reliable frequency distribution. The fly in the ointment is that the FUST decreases the temporal resolution of the low-frequency components, which may not be suitable for seismic reflection interpretation and reservoir location. In addition, the ST cannot tailor the time–frequency resolution for particular applications. To solve these problems, a simple and effective method is proposed by substituting the basis of the FUST. The proposed method can obtain the desired time resolution by adjusting two adjustable parameters. The corresponding inverse transform is also derived to guarantee its energy conservation and inevitable property. Numerical experiments and real data examples show better performance of the proposed method in improving temporal resolution and reservoir location over the ST and FUST.

Neural networks are commonly used for post-stack and pre-stack seismic inversion. With sufficient labelled data, the neural network-based seismic inversion results are more accurate than that use traditional seismic inversion methods. However, in the case of insufficient labeled data, the accuracy of neural networks-based seismic inversion results decreases and is even lower than those based on traditional inversion methods. In addition, the seismic inversion results based on neural networks generally suffer from lateral discontinuity. It further reduces the accuracy of the inversion results. To tackle these problems, we propose a pre-stack seismic amplitude variation with offset (AVO) inversion method based on Closed-Loop Multi-task conditional Wasserstein Generative Adversarial Network (CMcWGAN), which is a GAN-based AVO inversion method. CMcWGAN enables simultaneous and accurate inversion of P-wave velocity (Vp), S-wave velocity (Vs), and density ( ρ ). Moreover, it uses the low-frequency information of elastic parameters as a conditional input to alleviate the problem of lateral discontinuity in inversion results. Experimental results of simulated data show that the inversion results based on CMcWGAN have higher accuracy than those based on traditional AVO inversion methods. In addition, when the seismic angle gather is noisy, CMcWGAN has better robustness than the traditional methods. CMcWGAN can also obtain reasonable AVO inversion results in field seismic angle gather data.inversion results. Experimental results of simulated data show that the inversion results based on CMcWGAN have higher accuracy than those based on traditional AVO inversion method. In addition, when the seismic angle gather is noisy, CMcWGAN has better robustness than traditional method. CMcWGAN can also get reasonable AVO inversion results in field seismic angle gather data.

Amplitude variation with offset (AVO) refers to the amplitude variation with offset. This relationship can be used to analyze lithology and identify the oil and gas reservoirs in seismic exploration. Traditional AVO inversion is a typical ill-posed problem. When deep learning is directly used for seismic inversion, there are three main issues. First, the label data are insufficient. Second, a network trained for one working area is not applicable to other working areas. Third, there are spatial discontinuities and instability problems in the inversion results. In this letter, we propose the AVO inversion method that combines transfer learning and low-frequency component constraints. Transfer learning strategy is introduced to solve two main problems: The label data are insufficient to train the network, and the trained network is not applicable to other regions. Taking the low-frequency component as the constraint term makes the solution easier to converge to the true value. The experimental results of a typical example show that our method not only effectively improves the prediction accuracy and spatial continuity of the inversion results, but also reduces dependence on logging data.

Seismic attenuation compensation is a widely used technique for enhancing the resolution of non-stationary seismic data. An accurate and reliable quality factor (Q) is a prerequisite for attenuation compensation and an important indicator of oil and gas. Q-factor estimation and attenuation compensation are ill-posed inverse problems, and they are poorly robust when noise is present. Moreover, the process is usually performed in steps, which leads to an accumulation of errors. Deep learning-based methods have gained great popularity because of their powerful ability to obtain exact solutions for inverse problems. However, most deep learning frameworks rely heavily on huge training datasets and lack clear physical meaning. For these reasons, we propose a fusion neural network architecture to build a physically meaningful network to simultaneously implement interval-Q estimation and seismic attenuation compensation. The fusion neural network consists of two sub-networks based on spatio-temporal neural network. And, the method ensures the coupling relationship between Q-factor and non-attenuated/attenuated seismic data by building a loss function that contain both Q loss term and seismic data loss term. The training dataset is then used to update the sub-networks simultaneously to obtain a fusion network that achieves both interval-Q estimation and seismic attenuation compensation. We demonstrate the effectiveness of the proposed simultaneous interval-Q estimation and seismic attenuation compensation algorithm by applying both synthetic and field data examples.

Absorption attenuation compensation is an important part of seismic data processing. It enhances the resolution of non-stationary seismic data by compensating the amplitude energy and correcting phase distortion. The stabilized inverseQ-filter method, a widely used attenuation compensation method, constructs compensation operators based on stratigraphy-related assumptions and compensates seismic data using time-window analysis, which is computationally complex and sensitive to noise. The essence of attenuation compensation lies in the establishment of a nonlinear mapping relationship between attenuated and non-attenuated seismic traces, which strongly benefits from deep learning. This paper proposes a new method for attenuation compensation based on an end-to-end deep neural network to reduce the hand-crafted step of time-window analysis. Instead, the convolutional blocks of the network automatically learn and process seismic data features to achieve simultaneous amplitude and phase compensation. We have constructed two end-to-end network architectures for attenuation compensation: a fully convolutional network (FCN) and a U-Net. As an effective spectrum-broadening method, the compensation method based on the U-Net is shown to enhance vertical resolution with good lateral continuity, to provide reliable compensation results without complex calculations, and to exhibit high noise robustness. Synthetic data tests indicate that the compensation results from the U-Net are better than those from either the FCN or the stabilized inverseQ-filter method at different noise levels. Moreover, the correlation coefficient between the U-Net compensation results of the synthetic profile and the reference non-attenuated profile is higher than that of the FCN and the stabilized inverseQ-filter method. A field data application further verifies the effectiveness of this method.