Haoran Ren

Marine seismic imaging is affected by limited acquisition aperture, nonuniform illumination, and complex features such as seafloor structures and low-velocity gas clouds. These factors can produce blurred reflectors, amplitude imbalance, and migration artifacts in conventional reverse-time migration (RTM). Point-spread-function (PSF)-based image-domain least-squares migration (ID-LSM) compensates for spatially varying imaging responses through deconvolution, but its ill-conditioned inverse problem can amplify weakly constrained spectral components. We propose HQS-LR, an HQS-inspired, physics-guided unrolled framework for PSF-based ID-LSM. Each of its T stages performs a closed-form Fourier-domain PSF-based data-consistency update, applies a U-Net-based module for learned structural regularization and reflectivity refinement, and fuses the two estimates using a relaxation weight. HQS-LR is trained end-to-end using supervised triplets of reflectivity patches, migrated-image patches, and PSF patches generated from the Marmousi model. Sequential calibration on the Sigsbee2A synthetic model selects a six-stage configuration for the subsequent experiments. In the Sigsbee2A calibration-set comparison, this configuration achieves a Pearson correlation coefficient (CC) of 0.681 and a normalized PSF forward-matching residual of 0.035, providing a favorable balance between reflectivity similarity and consistency with the PSF imaging model while reducing ringing and preserving reflector continuity and fault geometry. Applied to the Mobil AVO Viking Graben Line 12 dataset, HQS-LR improves reflector focusing and continuity, suppresses localized oscillatory artifacts, and exhibits broader effective wavenumber support than the evaluated alternatives. These results indicate the potential of combining PSF-based physical constraints with learned structural priors for marine seismic reflectivity inversion.

This paper presents a General Physics-Based Thermal Model (GPTM) for analyzing photothermal coupling and indoor temperature dynamics. The model discretizes indoor spaces into multiple thermal nodes using the lumped capacitance method and integrates a stochastic Monte Carlo radiation model to simulate solar shortwave propagation under complex shading conditions. Simulation results for a standard classroom reveal significant spatial non-uniformity in solar radiation distribution, with shortwave heat gains varying from approximately 12 kW in primary daylight zones to 7 kW in shaded areas. The model further captures the critical role of building envelope thermal inertia, demonstrating a three-to-four-hour phase delay in thermal mass temperature response that stabilizes diurnal fluctuations. Longwave radiative exchange, particularly nighttime infrared cooling, is identified as a key heat dissipation mechanism. GPTM provides physically reliable predictions for analyzing complex building thermal processes and supporting energy-efficient design strategies.

Seismic phase picking is a critical task for earthquake detection and localization, where traditional methods rely on manual parameter tuning and have great difficulty to capture complex temporal features. In this paper, we propose PhaseMamba, an automated seismic phase picking and detection model that leverages deep learning through a U-shaped architecture with skip connections for effective time-domain seismic signal analysis, while incorporating a state-space Mamba model to enhance long-term contextual dependency extraction capabilities. For training, validation, and testing, we utilize the open-source global seismic dataset, STEAD (STanford EArthquake Dataset), which provides a diverse range of high-quality seismic waveforms. Comprehensive experiments are conducted on this dataset to evaluate the model’s performance. The results demonstrate that PhaseMamba achieves superior performance in P-wave arrival picking compared to all state-of-the-art models (PhaseNet, EQTransformer, and SeisT), while showing comparable or slightly lower performance in S-wave arrival picking. These findings suggest that PhaseMamba is a promising tool for advancing seismic phase picking and contributing to broader seismic research applications.

The simulation of seismic wave propagation through the solution of wave equations is considered one of the fundamental topics in applied geophysics. Physics-informed neural networks (PINNs) have been widely used in geophysics as an alternative to traditional numerical methods for solving wave equations. In this work, we introduce a novel deep learning-based framework named Particle Weak-form-based Neural Networks (ParticleWNN) to achieve better wavefield solutions in seismic modeling. The ParticleWNN is developed for solving partial differential equations (PDEs) in the weak form where the trial space is the space of DNNs and the test space is constructed by functions compactly supported in small regions. Compared to vanilla PINN, ParticleWNN offers several advantages, such as requiring less regularity, allowing local training, and parallel implementation via domain decomposition. The efficiency and accuracy of the proposed method in simulating seismic wave propagation are demonstrated by several numerical examples.

In the scenario of target impact detection and localization, seismic stations play a crucial and primary role in reconnaissance. Acoustic arrays further enhance the capability of microseismic detection. Therefore, traditional natural earthquake location methods can be applied to this new scenario. We conducted field experiments to verify the testing effectiveness of earthquake location logic in target impact point localization. Time–frequency characteristic analysis was applied to different types of signals. In this scenario, we primarily utilized impact explosion waves to locate the impact points. However, the presence of new strong-amplitude interference signals (ballistic shock waves) in the seismic records significantly impacted the results of traditional earthquake location methods. We combined the detection functions STA/LTA (short-term average to long-term average) and wavelet denoising techniques to maximize the enhancement of the characteristics of the impact blast waves. Following the paradigm of earthquake location, we successfully localized the target impact points at field scale. The phases of the impact blast waves corresponding to each impact point were also associated.

Applying deep neural networks (DNNs) to broadband seismic wave impedance inversion is challenging, especially in generalizing from synthetic to field data, which limits the exploitation of their nonlinear mapping capabilities. While many research studies are about advanced and enhanced architectures of DNNs, this article explores how variations in input data affect DNNs and consequently enhance their generalizability and inversion performance. This study introduces a novel data pre-processing strategy based on histogram equalization and an iterative testing strategy. By employing a U-Net architecture within a fully convolutional neural network (FCN) exclusively trained on synthetic and monochrome data, including post-stack profile, and 1D linear background impedance profiles, we successfully achieve broadband impedance inversion for both new synthetic data and marine seismic data by integrating imaging profiles with background impedance profiles. Notably, the proposed method is applied to reverse time migration (RTM) data from the Ceduna sub-basin, located in offshore southern Australia, significantly expanding the wavenumber bandwidth of the available data. This demonstrates its generalizability and improved inversion performance. Our findings offer new insights into the challenges of seismic data fusion and promote the utilization of deep neural networks for practical seismic inversion and outcomes improvement.

Earthquake location is of great significance for earthquake early warning and earthquake research. Accurately determining the location of earthquake clusters provides valuable insights into understanding fault zone distributions. The traditional methods require more than four seismic stations, but it can be alleviated by the master events. Therefore, we proposed a unified spherical interpolation (SI) location framework, which can realize earthquake cluster relocation according to different observation conditions (the number of available stations and master events). We have validated the efficacy of SI relative locator through its successful application to 2019 Ridgecrest earthquake and 2023 Jishishan earthquake within a sparse network featuring only two seismic stations and a known set of master events. Our results provide good initial results for horizontal locations and are well suited for subsequent insertions of ray-tracing techniques based on finer models to determine depths more accurately.

The emerging distributed fiber-optic acoustic sensing (DAS) technology has broad prospects for application in vertical seismic profiles (VSP). However, the acquired DAS-VSP data often suffers from coupled noise that seriously affects data quality. Traditional methods for suppressing coupled noise are usually time-consuming and not suitable for the large-scale denoising of DAS-VSP data. To address this, a coupled noise suppression method based on the U-Net network is proposed, and a self-attention (SA) block is introduced to enhance the denoising ability of the network. Transfer learning is employed to achieve coupled noise suppression from synthetic data to field data. Denoising results demonstrate that the network can effectively suppress coupled noise in DAS-VSP data while preserving signal energy to a certain extent, exhibiting strong generalization capability. Upon completion of network training, denoising results can be obtained within seconds, making it more convenient and efficient compared to traditional methods.