Ghost Model Research Articles

A Ghost and Attention Mechanism-Based Deep Learning Approach for SAR Small Target Image Detection

Sy nthetic aperture radar (SAR) images play an important role in the task of small target imaging and detection. However, due to its blurred target imaging, complex image background, and challenging feature representation, SAR detection is difficult to get high precision in a complex imaging environment. Therefore, by integrating the ghost model and the Attention-Mechanism (AM), this paper presents a new feature network model based on the improved YOLOv5s for detecting small targets in SAR images efficiently. The key is to design a Ghost_AM module by integrating the ghost bottleneck with the simple AM. This Ghost_AM module is introduced into to the YOLOv5s’ network for improving the YOLOv5s’ extraction performance for small target’s feature from a complex SAR image. Furthermore, to improve the YOLOv5s’ recognition ability for the features extracted from the Ghost_AM, a bidirectional feature pyramid network is use to replace the YOLOv5s’ feature fusion network and get a precise detection. The experiments using some SAR ship image datasets have demonstrated the presented model is better than some traditional SAR target detection algorithms and the original YOLOv5s. Moreover, the presented model’s parameters are reduced by 25.1% compared with the original YOLOv5s, which contributes to its simpler implementation.

Face swapping in seizure videos for patient deidentification

ObjectiveThis study aimed to test different AI-based face-swapping models applied to videos of epileptic seizures, with the goal of protecting patient privacy while retaining clinically useful seizure semiology. We hypothesized that specific models would show differences in semiologic fidelity compared to the original clinical videos. MethodsThree open-source models, SimSwap, MobileFaceSwap and GHOST were adopted for face-swapping. For every model, an AI generated male and female image were used to replace the original faces. One representative seizure per patient from three patients with epilepsy was chosen (3 seizure videos x 3 AI models x 2 M/F swap) and remade to 18 transformed video clips. To evaluate the performance of the three models, we used both objective (AI-based) and subjective (expert clinician) evaluation. The objective assessment included four metrics for facial appearance and four metrics for facial expression changes. Four experienced epileptologists reviewed the clips and scoring according to deidentification and preservation of semiology. Kruskal-Wallis H test was used for statistical analysis among the models. ResultsIn the reproduced videos, the swapped face cannot be recognized as the original face, with no significant difference in scores of deidentification either by objective or subjective assessment. Regarding semiology preservation, no significant differences between models were observed in the objective evaluations. The subjective evaluations revealed that the GHOST model outperformed the other two models (p=0.028). ConclusionThis is the first study evaluating AI face swapping models in epileptic seizure video clips. Optimization of AI face-swapping models could enhance the accessibility of seizure videos for education and research while protecting patient privacy and maintaining semiology.

Study of dark energy models in [formula omitted] theory

This paper investigates the quantum chromodynamics ghost model of dark energy in the framework of f(R,T2) theory. The investigation centers on the non-interacting ghost dark energy model in a Friedmann–Robertson–Walker universe. Various cosmic parameters such as the Hubble parameter, equation of state and deceleration parameter are computed to examine the implications of the ghost dark energy model. Further, it is discovered that the equation of state parameter of ghost dark energy traverses the phantom regime. The stability analysis of this model is conducted using the squared sound speed. This study serves to reconstruct the model of gravity by incorporating dark matter and the ghost dark energy model. Special emphasis is placed on the scenario where the function is defined as f(R,T2)=f1(R)+f2(T2). In this context, our attention is focused on a specific model that allows for the use of the standard continuity equation in this modified theory. The conclusion drawn is that the reconstructed ghost dark energy model in the framework of f(R,T2) gravity exhibit phantom or quintessence eras of the cosmos, describing the accelerated expansion of the universe. This exploration sheds light on the complex interplay between quantum chromodynamics, dark energy and modified theories of gravity, offering valuable insights into the dynamics of the cosmos at large scales.

Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance.

The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600 . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes. Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.

Detection of Broken Hongshan Buckwheat Seeds Based on Improved YOLOv5s Model

Breeding technology is one of the necessary means for agricultural development, and the automatic identification of poor seeds has become a trend in modern breeding. China is one of the main producers of buckwheat, and the cultivation of Hongshan buckwheat plays an important role in agricultural production. The quality of seeds affects the final yield, and improving buckwheat breeding technology is particularly important. In order to quickly and accurately identify broken Hongshan buckwheat seeds, an identification algorithm based on an improved YOLOv5s model is proposed. Firstly, this study added the Ghost module to the YOLOv5s model, which improved the model’s inference speed. Secondly, we introduced the bidirectional feature pyramid network (BiFPN) to the neck of the YOLOv5s model, which facilitates multi-scale fusion of Hongshan buckwheat seeds. Finally, we fused the Ghost module and BiFPN to form the YOLOV5s+Ghost+BiFPN model for identifying broken Hongshan buckwheat seeds. The results show that the precision of the YOLOV5s+Ghost+BiFPN model is 99.7%, which is 11.7% higher than the YOLOv5s model, 1.3% higher than the YOLOv5+Ghost model, and 0.7% higher than the YOLOv5+BiFPN model. Then, we compared the FLOPs value, model size, and confidence. Compared to the YOLOv5s model, the FLOPs value decreased by 6.8 G, and the model size decreased by 5.2 MB. Compared to the YOLOv5+BiFPN model, the FLOPs value decreased by 8.1 G, and the model size decreased by 7.3MB. Compared to the YOLOv5+Ghost model, the FLOPs value increased by only 0.9 G, and the model size increased by 1.4 MB, with minimal numerical fluctuations. The YOLOv5s+Ghost+BiFPN model has more concentrated confidence. The YOLOv5s+Ghost+BiFPN model is capable of fast and accurate recognition of broken Hongshan buckwheat seeds, meeting the requirements of lightweight applications. Finally, based on the improved YOLOv5s model, a system for recognizing broken Hongshan buckwheat seeds was designed. The results demonstrate that the system can effectively recognize seed features and provide technical support for the intelligent selection of Hongshan buckwheat seeds.

A phylogenomic perspective on the relationships of subfamilies in the family Geometridae (Lepidoptera)

AbstractGeometrid moths, the second largest radiation of Lepidoptera, have been the target of extensive phylogenetic studies. Those studies have flagged several problems in tree topology that have remained unanswered. We address three of those: (i) deep nodes of Geometridae (subfamilies Sterrhinae + Larentiinae, or Sterrhinae alone as sister to all other subfamilies), (ii) the taxonomic status of subfamily Orthostixinae and (iii) the systematic position of the genus Eumelea (classified in Desmobathrinae: Eumeleini or incertae sedis earlier). We address these questions by using a phylogenomic approach, a novel method on these moths, with up to 1000 protein‐coding genes extracted from whole‐genome shotgun sequencing data. Our datasets include representatives from all geometrid subfamilies and we analyse the data by using three different tree search strategies: partitioned models, GHOST model and multispecies coalescent analysis. Despite the extensive data, we found incongruences in tree topologies. Eumelea did not associate with Desmobathrinae as suggested earlier, but instead, it was recovered in three different phylogenetic positions, either as sister to Oenochrominae, Geometrinae or as sister to Oenochrominae + Geometrinae. Orthostixinae, represented by its type species, falls within Desmobathrinae. We propose the following taxonomic changes: we raise Eumeleini to subfamily rank as Eumeleinae stat. nov. and we treat Orthostixinae as a junior synonym of Desmobathrinae syn. nov.

A Small Object Detection Algorithm Based on Modulated Deformable Convolution and Large Kernel Convolution.

Object detection is one of the most critical areas in computer vision, and it plays an essential role in a variety of practice scenarios. However, small object detection has always been a key and difficult problem in the field of object detection. Therefore, considering the balance between the effectiveness and efficiency of the small object detection algorithm, this study proposes an improved YOLOX detection algorithm (BGD-YOLOX) to improve the detection effect of small objects. We present the BigGhost module, which combines the Ghost model with a modulated deformable convolution to optimize the YOLOX for greater accuracy. At the same time, it can reduce the inference time by reducing the number of parameters and the amount of computation. The experimental results show that BGD-YOLOX has a higher average accuracy rate in terms of small target detection, with mAP0.5 up to 88.3% and mAP0.95 up to 56.7%, which surpasses the most advanced object detection algorithms such as EfficientDet, CenterNet, and YOLOv4.

Stochastic Dilated Residual Ghost Model for Breast Cancer Detection.

One of the most contentious issues in modern medicine is how to effectively standardise breast cancer screening. Deep learning models are already saving lives in the medical field due to their capacity to distinguish between benign and malignant tumours. Histopathology imaging poses difficulties due to the possibility of large colour variations caused by the staining technique and the biopsy material used to make the image; this problem leads to inaccurate breast cancer diagnoses. Our primary focus in this assessment is on the four main research concerns listed in the following: Overfitting and colour divergence must be rectified before moving on to other aspects of breast cancer categorisation. To overcome this issue, strain normalisation is utilised, and adding extra components is used to cope with overfitting; both techniques yielded positive results. The multiscale stochastic and dilation unit was then created to extract and enhance fine-grained characteristics such as edges, contours, and colour accuracy. To achieve this, the image is scaled to various different levels. The last challenge is to overcome the stochastic dilated residual ghost model's unreliability when used to recognise very tiny objects. The stochastic pooling block in this model makes effective use of downsampling to simplify the process without compromising the capacity to retrieve deep information. This upgrade was done as part of a bigger endeavour to eliminate unneeded or redundant components. In this case, we use convolution and identity mapping to create and maintain accurate mappings of the object's inherent characteristics. Upsampling is frequently used in conjunction with stochastic pooling to reduce feature dimensionality. The results of the experiments show that the suggested method is better than some of the current methods, with a network performance measurement area under the curve of 96.15 and percentages of 98.50 and 97.36.

Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model.

Image segmentation and computer vision are becoming more important in computer-aided design. A computer algorithm extracts image borders, colours, and textures. It also depletes resources. Technical knowledge is required to extract information about distinctive features. There is currently no medical picture segmentation or recognition software available. The proposed model has 13 layers and uses dilated convolution and max-pooling to extract small features. Ghost model deletes the duplicated features, makes the process easier, and reduces the complexity. The Convolution Neural Network (CNN) generates a feature vector map and improves the accuracy of area or bounding box proposals. Restructuring is required for healing. As a result, convolutional neural networks segment medical images. It is possible to acquire the beginning region of a segmented medical image. The proposed model gives better results as compared to the traditional models, it gives an accuracy of 96.05, Precision 98.2, and recall 95.78. The first findings are improved by thickening and categorising the image's pixels. Morphological techniques may be used to segment medical images. Experiments demonstrate that the recommended segmentation strategy is effective. This study rethinks medical image segmentation methods.

Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model.

Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method's accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.

Dilated residual grooming kernel model for breast cancer detection

• Solves the overfitting issue in the Convolution Neural Networks . • The proposed fully connected dilation unit enhances small and low-level features. • Accurate cancerous cell Detection using dilated model. • The proposed model solves the issue of desegregation. • Efficiently removal of redundant and similar features through proposed model. Breast cancer is the most significant cause of mortality among women. When detected and treated early, it saves lives. Breast cancer detection is becoming more accessible and accurate thanks to machine learning and deep learning models. This research aims to enhance medical science and technology by employing a deep learning model to detect small cancer cells with pinpoint accuracy. The proposed model uses datasets from the Breast Cancer Histopathological Image Classification (BreakHis) and Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD). Next, the image processing procedure employs strain normalization to rectify color divergence caused by using different slide scanners, staining processes, and biopsy materials. The data augmentation with nineteen parameters such as scaling, rotation, flip, resize, and gamma value tackles the overfitting problems. The augmented images are then processed using the dilated residual (DR) model; the DR model combines the proposed dilated spatial convolution unit, fully connected dilation unit, and dilated channel convolution. The first unit is the dilated spatial convolution, which handles all channels equally and amplifies the valuable aspects. The second unit is a fully connected dilation unit; it displays low-level properties such as edges, contours, and color. The third unit is dilated channel convolution, which detects tiny objects and thin boundaries without adding complexity. The proposed dilated residual grooming kernel (DRGK) model is a 14-layer deep learning model that stretches the receptive field while retaining feature information, using the proposed DR unit and ghost model, as well as convolution, pooling, downsampling, and dilated convolution. Dilated convolutions are extensively used in the proposed model to extract features. Accuracy, the area under the curve, average precision score, precision, sensitivity, and f1 score all improve with a learning rate of 0.001. With 96.33% and 93.35% marks, the proposed approach surpasses several state-of-the-art methods.

Cosmological analysis of noninteracting and interacting generalized ghost dark energy in Einstein–Aether gravity theory

We have investigated the generalized QCD ghost model of dark energy (DE) in the framework of Einstein–Aether gravity. At first we have discussed the noninteracting generalized ghost DE (GGDE) model in flat Friedmann–Robertson–Walker (FRW) universe and then we have generalized the discussion to the interacting GGDE model in flat and nonflat universe. According to this, we have analyzed the nature of the equation of state (EoS) parameter, deceleration parameter and slow-roll parameters. We have used the sign of square speed of sound to determine the stability of the model in different cases. Also, we have analyzed [Formula: see text] plane in graphically and pointed out freezing region and thawing region in that plane. Then we enlarge our discussion to verify the effectiveness of this model under investigation by analyzing the statefinder diagnosis parameters for the present cosmological setup. The nature of slow-roll parameters in both scenarios with and without interactions has been shown diagrammatically.

Breast Cancer Histopathological Image Classification Using Stochastic Dilated Residual Ghost Model

A new deep learning-based classification model called the Stochastic Dilated Residual Ghost (SDRG) was proposed in this work for categorizing histopathology images of breast cancer. The SDRG model used the proposed Multiscale Stochastic Dilated Convolution (MSDC) model, a ghost unit, stochastic upsampling, and downsampling units to categorize breast cancer accurately. This study addresses four primary issues: first, strain normalization was used to manage color divergence, data augmentation with several factors was used to handle the overfitting. The second challenge is extracting and enhancing tiny and low-level information such as edge, contour, and color accuracy; it is done by the proposed multiscale stochastic and dilation unit. The third contribution is to remove redundant or similar information from the convolution neural network using a ghost unit. According to the assessment findings, the SDRG model scored overall 95.65 percent accuracy rates in categorizing images with a precision of 99.17 percent, superior to state-of-the-art approaches.

Evolution of histopathological breast cancer images classification using stochastic dilated residual ghost model

Breast cancer detection is a complex problem to solve, and it is a topic that is still being studied. Deep learning-based models aid medical science by helping to classify benign and malignant cancers and saving lives. Breast cancer histopathological image classification (BreakHis) and breast cancer histopathological annotation and diagnosis (BreCaHAD) datasets are used in the proposed model. The study led to the resolution of four essential issues: 1) Addresses the color divergence issue caused by strain normalization during image generation 2) Data augmentation uses several factors like as flip, rotation, shift, resize, and gamma value in order to overcome overfitting concerns caused by a lack of histopathology images. 3) Using the proposed stochastic dilated convolution (SDC) model, able to find missing features and improve tiny and low-level features such as edge, contour, and color correctness. This model effectively solves depth issues with stochastic pooling and max pooling by combining three proposed units: spatial dilation convolution, channel dilation convolution, and dilated convolution to detect missing features and obtain more information on images. Stochastic pooling may choose any value since it is classified as a heterogeneous approach that can determine tiny and large values. When the max pooling and stochastic pooling are combined, a decent feature map is produced, processed by the dilatation unit, detecting cancer cells without adding to the complexity. 4) To correctly identify breast cancer and extract depth characteristics, the stochastic dilated residual ghost (SDRG) model uses the proposed SDC model, a ghost unit, stochastic upsampling, and downsampling units. The proposed feature selection unit employs linear transformations to produce feature maps to show information based on intrinsic characteristics to eliminate redundant or similar features in convolutional neural networks. Convolution and identity mapping are used to construct and retain intrinsic feature mappings in this skip unit while upsampling with stochastic pooling is used to minimize feature dimensions. The proposed model?s performance was assessed with 150,271 augmented and original histopathology images. The proposed method?s findings compare favorably to eight popular approaches. In addition, the suggested model outperforms the competition in terms of accuracy, average precision score, precision, sensitivity, and f1 score. With an accuracy of 98.41% with BreakHis and 98.60% with the BreCaHAD dataset with enhanced images, the proposed method exceeds numerous state-of-the-art methods.

Due South: The evolutionary history of Sub-Antarctic and Antarctic Tritoniidae nudibranchs

The Tritoniidae provides one of the most famous model species for neurophysiology and behaviour, yet a well-developed phylogenetic framework for this family is still incomplete. In this study, we explored the species-level taxonomy, phylogenetic relationships, and geographic distributions of the tritoniid nudibranchs. During numerous expeditions, specimens from southern South America, Sub-Antarctic Islands, and Antarctica were collected, documented alive, and fixed for anatomical descriptions and genetic sequencing. DNA from 167 specimens were extracted and sequenced for mitochondrial (COI, 16S) and nuclear (H3) markers. An additional 109 sequences of all available tritoniids plus additional outgroups were downloaded from GenBank for comparative purposes. Maximum Likelihood under the GHOST model of evolution and Bayesian inference using the GTR + GAMMA model produced congruent topologies from concatenated alignments. The results of ABGD, GMYC, bPTP, and mPTP species delimitation analyses suggest many separately evolving units that do not coincide with traditionally recognized species limits. Southern Ocean Tritoniella and Tritonia species split into several previously unrecognized species. This result is in accordance with the limited dispersal abilities of some southern tritoniids. Along with the most complete phylogeny of Tritoniidae to date, we also provided many taxonomic notes at the species and genus level. Tritoniidae species are yet another example of under-recognized diversity in the Southern Ocean.

Interacting ghost dark energy in complex quintessence theory

We employ a ghost model of interacting dark energy to obtain the equation of state omega for ghost energy density in an FRW universe in complex quintessence theory. We reconstruct the potential and study the dynamics of the scalar field that describes complex quintessence cosmology. We perform omega -omega ' analysis and stability analysis for both non-interacting and interacting cases and find that the same basic conclusion as for the real model, where omega ' = domega / d ln a. Taking account of the effect of the complex part and assuming the real part of the quintessence field to be a slow-rolling field, we conclude that the non-interacting model cannot describe the real universe since this will lead to fractional energy density Omega _D > 1, where Omega _D can be defined as the ratio of rho _D to rho _{cr}. However, for the interacting case, if we take present Omega _D =0.73, then we can determine that b^2 = 0.0849, where b^2 is the interaction coupling parameter between matter and dark energy. In the real quintessence model, Omega _D and b^2 are independent parameters, whereas in the complex quintessence model, we conclude that there is a relationship between these two parameters.

Motile ghosts of the halophilic archaeon, Haloferax volcanii

Archaea swim using the archaellum (archaeal flagellum), a reversible rotary motor consisting of a torque-generating motor and a helical filament, which acts as a propeller. Unlike the bacterial flagellar motor (BFM), ATP (adenosine-5'-triphosphate) hydrolysis probably drives both motor rotation and filamentous assembly in the archaellum. However, direct evidence is still lacking due to the lack of a versatile model system. Here, we present a membrane-permeabilized ghost system that enables the manipulation of intracellular contents, analogous to the triton model in eukaryotic flagella and gliding Mycoplasma We observed high nucleotide selectivity for ATP driving motor rotation, negative cooperativity in ATP hydrolysis, and the energetic requirement for at least 12 ATP molecules to be hydrolyzed per revolution of the motor. The response regulator CheY increased motor switching from counterclockwise (CCW) to clockwise (CW) rotation. Finally, we constructed the torque-speed curve at various [ATP]s and discuss rotary models in which the archaellum has characteristics of both the BFM and F1-ATPase. Because archaea share similar cell division and chemotaxis machinery with other domains of life, our ghost model will be an important tool for the exploration of the universality, diversity, and evolution of biomolecular machinery.

The Organelle Genomes in the Photosynthetic Red Algal Parasite Pterocladiophila hemisphaerica (Florideophyceae, Rhodophyta) Have Elevated Substitution Rates and Extreme Gene Loss in the Plastid Genome.

Comparative organelle genome studies of parasites can highlight genetic changes that occur during the transition from a free-living to a parasitic state. Our study focuses on a poorly studied group of red algal parasites, which are often closely related to their red algal hosts and from which they presumably evolved. Most of these parasites are pigmented and some show photosynthetic capacity. Here, we assembled and annotated the complete organelle genomes of the photosynthetic red algal parasite, Pterocladiophila hemisphaerica. The plastid genome is the smallest known red algal plastid genome at 68,701bp. The plastid genome has many genes missing, including all photosynthesis-related genes. In contrast, the mitochondrial genome is similar in architecture to that of other free-living red algae. Both organelle genomes show elevated mutation rates and significant changes in patterns of selection, measured as dN/dS ratios. This caused phylogenetic analyses, even of multiple aligned proteins, to be unresolved or give contradictory relationships. Full plastid datasets interfered by selected best gene evolution models showed the supported relationship of P.hemisphaerica within the Ceramiales, but the parasite was grouped with support as sister to the Gracilariales when interfered under the GHOST model. Nuclear rDNA showed a supported grouping of the parasite within a clade containing several red algal orders including the Gelidiales. This photosynthetic parasite, which is unable to photosynthesize with its own plastid due to the total loss of all photosynthesis genes, raises intriguing questions on parasite-host organelle genome capabilities and interactions.

Adaptive estimation of the upgoing wavefield from a variable-depth recording in the case of a dynamic sea surface

The estimation of the upgoing wavefield based on a ghost model is well-known as receiver deghosting. The shape of the rough and dynamic (time-varying) sea surface and the varying receiver depth should be included to carry out this step correctly. However, the exact shape of the dynamic sea surface is not commonly measured. Therefore, the ghost model is often inaccurate, introducing artifacts in the deghosted result. We have developed a data-driven, iterative, receiver deghosting method that includes the estimation of the dynamic shape of the sea surface and the varying receiver depth. The deghosting algorithm is based on, respectively, wavefield propagation from the varying receiver level up to the dynamic sea surface and wavefield propagation from the dynamic sea surface down to the varying receiver level. The depth of the receivers relative to the height of the dynamic sea surface is estimated and updated at each iteration. We validate the deghosting method on 2D simulated data recorded on a variable depth and 2D simulated data recorded with a dynamic sea surface. Our deghosting method gives a significant uplift on the simulated data given that the initial ghost model is inaccurate. On the field data, recorded with a midwater, quasistationary cable, our deghosting method is able to correct the receiver depth and provides an accurate deghosting result. The performance of the deghosting method is similar to the performance of a multicomponent decomposition method in this realistic scenario.

Source ghost generation: observations from a dual near-field hydrophone test

In this paper, we investigate source ghost generation using experimental data acquired using a dual-string air-gun array instrumented with two near-field hydrophone (NFH) channels per source element instead of the usual single NFH per source element. The extra NFHs enable us to solve directly for source ghosts, sidestepping the requirement for a ghost model. Experience has shown that the standard ghost model does not always represent well the physics of ghost generation for an air-gun array. In practice this model is typically parameterized via a frequency-dependent effective reflectivity, the magnitude of which often needs to be reduced more than expected for the effects of rough sea-surface scattering. The NFH recordings in this test are compared with synthetic data to highlight problems with the standard model of ghost generation and the results suggest that acoustically induced cavitation is responsible for the observed reduction in the amplitude of the ghost. We show examples of de-signature on seismic data using operators derived with and without a ghost model and discuss the merits and potential issues going forward.