Microseismic source imaging using physics-informed neural networks with hard constraints

Microseismic source imaging plays a significant role in passive seismic monitoring. However, such a process is prone to failure due to the aliasing problem when dealing with sparse measured data. Thus, we propose a direct microseismic imaging framework based on physics-informed neural networks (PINNs), which can generate focused source images, even with very sparse recordings. We use the PINNs to represent a multi-frequency wavefield and then apply the inverse Fourier transform to extract the source image. Specially, we modify the representation of the frequency-domain wavefield to inherently satisfy the boundary conditions (the measured data on the surface) by means of the hard constraint, which helps to avoid the difficulty in balancing the data and PDE losses in PINNs. Furthermore, we propose the causality loss implementation with respect to depth to enhance the convergence of PINNs. The numerical experiments on the Overthrust model show that the method can admit reliable and accurate source imaging for single- or multiple- sources and even in passive monitoring settings. Then, we further apply our method on the hydraulic fracturing field data, and demonstrate that our method can correctly image the source

GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks

The computation of the seismic wavefield by solving the Helmholtz equation is crucial to many practical applications, e.g., full waveform inversion. Physics-informed neural networks (PINNs) provide functional wavefield solutions represented by neural networks (NNs), but their convergence is slow. To address this problem, we propose a modified PINN using multiplicative filtered networks, which embeds some of the known characteristics of the wavefield in training, e.g., frequency, to achieve much faster convergence. Specifically, we use the Gabor basis function due to its proven ability to represent wavefields accurately and refer to the implementation as GaborPINN. Meanwhile, we incorporate prior information on the frequency of the wavefield into the design of the method to mitigate the influence of the discontinuity of the represented wavefield by GaborPINN. The proposed method achieves up to a two-magnitude increase in the speed of convergence as compared with conventional PINNs

Efficient physics-informed neural networks using hash encoding

Physics-informed neural networks (PINNs) have attracted a lot of attention in scientific computing as their functional representation of partial differential equation (PDE) solutions offers flexibility and accuracy features. However, their training cost has limited their practical use as a real alternative to classic numerical methods. Thus, we propose to incorporate multi-resolution hash encoding into PINNs to improve the training efficiency, as such encoding offers a locally-aware (at multi resolution) coordinate inputs to the neural network. Borrowed from the neural representation field community (NeRF), we investigate the robustness of calculating the derivatives of such hash encoded neural networks with respect to the input coordinates, which is often needed by the PINN loss terms. We propose to replace the automatic differentiation with finite-difference calculations of the derivatives to address the discontinuous nature of such derivatives. We also share the appropriate ranges for the hash encoding hyperparameters to obtain robust derivatives. We test the proposed method on three problems, including Burgers equation, Helmholtz equation, and Navier-Stokes equation. The proposed method admits about a 10-fold improvement in efficiency over the vanilla PINN implementation

A prior regularized full waveform inversion using generative diffusion models

Full waveform inversion (FWI) has the potential to provide high-resolution subsurface model estimations. However, due to limitations in observation, e.g., regional noise, limited shots or receivers, and band-limited data, it is hard to obtain the desired high-resolution model with FWI. To address this challenge, we propose a new paradigm for FWI regularized by generative diffusion models. Specifically, we pre-train a diffusion model in a fully unsupervised manner on a prior velocity model distribution that represents our expectations of the subsurface and then adapt it to the seismic observations by incorporating the FWI into the sampling process of the generative diffusion models. What makes diffusion models uniquely appropriate for such an implementation is that the generative process retains the form and dimensions of the velocity model. Numerical examples demonstrate that our method can outperform the conventional FWI with only negligible additional computational cost. Even in cases of very sparse observations or observations with strong noise, the proposed method could still reconstruct a high-quality subsurface model. Thus, we can incorporate our prior expectations of the solutions in an efficient manner. We further test this approach on field data, which demonstrates the effectiveness of the proposed method

Genetic Maps of Diploid Orchardgrass (\u3cem\u3eDactylis glomerata\u3c/em\u3e L.)

Orchardgrass (Dactylis glomerata L.) is indigenous to Eurasia and northern Africa. It has been naturalized on nearly every continent and is one of the top four economically important perennial forage grasses grown worldwide (Stewart and Ellison 2010). It has been used widely as forage due to its quality, biomass production and good shade tolerance. Despite its various agricultural uses, little information is available for functional and comparative genetic analysis and concomitant genetic improvement of this species. To date, a number of linkage maps have been constructed for forage grasses such as perennial ryegrass (Lolium perenne L.) (Jones et al. 2002), tall fescue (Lolium arundinaceum (Schreb.) Darbysh.) (Saha et al. 2005), and Zoysiagrass (Zoysia japonica) (Cai et al. 2005). Until recently, there have been no reports of genetic linkage studies in orchardgrass. In the present study, a genetic linkage map of diploid orchardgrass based on two-way pseudo-testcross mapping strategy was constructed using SRAP and SSR markers. This is the first step towards genomic mapping for this species

Nasopharyngeal carcinoma with non-squamous phenotype may be a variant of nasopharyngeal squamous cell carcinoma after inhibition of EGFR/PI3K/AKT/mTOR pathway

Nasopharyngeal carcinoma (NPC) is a cancerous tumor that develops in the nasopharynx epithelium and typically has squamous differentiation. The squamous phenotype is evident in immunohisto-chemistry, with diffuse nuclear positivity for p63 and p40. Nonetheless, a few NPCs have been identified by clinicopathological diagnosis that do not exhibit the squamous phenotype; these NPCs are currently referred to as non-squamous immuno-phenotype nasopharyngeal carcinomas (NSNPCs). In a previous work, we have revealed similarities between the histological appearance, etiology, and gene alterations of NSNPC and conventional NPC. According to ultrastructural findings, NSNPC still falls under the category of non-keratinized squamous cell carcinoma that is undifferentiated. NSNPC has an excellent prognosis and a low level of malignancy, according to a retrospective investigation. Based on prior research, we investigated the molecular mechanism of NSNPC not expressing the squamous phenotype and its biological behavior. IHC was used to determine the expression of EGFR, PI3K, AKT, p-AKT, mTOR, p-mTOR, Notch, STAT3 and p-STAT3 in a total of 20 NSNPC tissue samples and 20 classic NPC tissue samples. We obtained human NPC cell lines (CNE-2,5-8F) and used EGFR overexpression plasmid and shRNAs to transfect them. To find out whether mRNA and proteins were expressed in the cells, we used Western blotting and qRT-PCR. Cell biological behavior was discovered using the CCK-8 assay, cell migration assay, and cell invasion assay. EGFR, PI3K, p-AKT and p-mTOR proteins were lowly expressed in NSNPC tissues by immunohistochemistry, compared with classical NPC. In the classical NPC cell lines CNE-2 and 5-8F, overexpression EGFR can up-regulate the expression of p63 through the PI3K/AKT/mTOR pathway, and promote the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. At the same time, knockout of EGFR can down-regulate p63 expression through the PI3K/AKT/mTOR pathway, and inhibit the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. The lack of p63 expression in NSNPC was linked with the inhibition of the EGFR/PI3K/AKT/mTOR pathway, and NSNPC may be a variant of classical NPC

NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition

Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs). Recently, there has been a growing interest in introducing physics constraints into training neural PDE solvers to reduce the use of costly data and improve the generalization ability. However, these physics constraints, based on certain finite dimensional approximations over the function space, must resolve the smallest scaled physics to ensure the accuracy and stability of the simulation, resulting in high computational costs from large input, output, and neural networks. This paper proposes a general acceleration methodology called NeuralStagger by spatially and temporally decomposing the original learning tasks into several coarser-resolution subtasks. We define a coarse-resolution neural solver for each subtask, which requires fewer computational resources, and jointly train them with the vanilla physics-constrained loss by simply arranging their outputs to reconstruct the original solution. Due to the perfect parallelism between them, the solution is achieved as fast as a coarse-resolution neural solver. In addition, the trained solvers bring the flexibility of simulating with multiple levels of resolution. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations, which leads to an additional $10\sim100\times$ speed-up. Moreover, the experiment also shows that the learned model could be well used for optimal control.Comment: ICML 2023 accepte

Phylogenetic Relationships in the Festuca-Lolium Complex (Loliinae; Poaceae): New Insights from Chloroplast Sequences

The species within the Lolium/Festuca grass complex have dispersed and colonized large areas of temperate global grasslands both naturally and by human intervention. The species within this grass complex represent some of the most important grass species both for amenity and agricultural use worldwide. There has been renewed interest by grass breeders in producing hybrid combinations between these species and several countries now market Festulolium varieties as a combination of genes from both genera. The two genera have been differentiated by their inflorescence structure, but controversy has surrounded the taxonomic classification of the Lolium-Festuca complex species for several decades. In order to better understand the complexities within the Lolium/Festuca complex and their genetic background, the phylogeny of important examplers from the Lolium-Festuca complex were reconstructed. In total 40 taxa representing the Festuca and Lolium species with Vulpia myuros and Brachypodium distachyon as outgroups were sampled, using two noncoding intergenic spacers (trnQ-rps16, trnH-psbA) and one coding gene (rbcL). Maximum parsimony (MP), Bayesian inference (BI) analyses based on each partition and combined plastid DNA dataset, and median-jointing network analysis were employed. The outcomes strongly suggested that the subgen. Schedonorus has a close relationship to Lolium, and it is also proposed to move the sect. Leucopoa from subgen. Leucopoa to Subgen. Schedonorus and to separate sect. Breviaristatae from the subgen. Leucopoa. We found that F. californica could be a lineage of hybrid origin because of its intermediate placement between the broad-leaved and fine-leaved clade