Traditional Inversion Research Articles

A novel approach to address source overprint and shadow effects in controlled-source audio-frequency magnetotelluric exploration

Source overprint and shadow effects can significantly affect controlled-source audio-frequency magnetotelluric (CSAMT) data and mask subsurface geologic structures; however, few solutions have been presented. Based on the traditional observation approach, we develop a new approach to solve this problem. An additional transmitter and measurement zone are introduced in the novel approach to constrain the anomalous body causing source overprint or shadow effects in the inversion. To test this approach, six experimental models are designed. Three-dimensional CSAMT responses on the six synthetic models have been simulated, and the results indicate that source overprint and shadow effects have a strong influence on the data. Strong source overprint and shadow effects are found at low frequencies. The distribution of the source overprint or shadow distortions is affected by the shape of the anomalies below the transmitter or between the transmitter and receiver. We evaluate our approach by comparing the inversion results obtained with the new and traditional approaches based on the six synthetic data sets. The results indicate that the new approach not only effectively resolves the anomalies causing source overprint or shadow distortions but also yields more accurate target results in terms of resistivity value, structure geometry, and position than the conventional scheme. The inversion of field data from Yanqing, China, further verifies the effectiveness of our approach. Compared with the traditional inversion, the new scheme inversion obtains results that are more consistent with the rock type revealed by a drillhole.

성경적 관점에서 바라본 매리언 무어의 「물소」와 「아홉 개의 승도복숭아」

This study aims to reinterpret Marianne Moore’s “The Buffalo” and “Nine Nectarines” from a Biblical perspective, unveiling her obscure poetic vision by repositioning her in the tradition of English poetry as a poet of a ‘line of vision’ including Sidney, Spenser, Milton, and Blake. Moore, well-known as one of the representative modernist poets alongside Pound and Eliot in the early 20th century, has long been recognized as a poet of the ‘line of wit,’ encompassing Donne, Dryden, and Pope, a viewpoint that Eliot advocated particularly fervently. However, considering the critical influence exerted by Blake on her identification as a poet, it becomes necessary to reconsider Moore as one of the visionary poets and analyze her work through the lens of the Bible. Just as the work of visionary poets can be understood through the great code of their art, the Bible, so can the work of Moore. Therefore, this paper attempts Biblical interpretations of Moore’s two poems “The Buffalo” and “Nine Nectarines,” which have been criticized from various perspectives, even to the extent of confusion and abstruseness. Both of them, which depict a wild Indian buffalo and nine wild nectarines as their respective poetic objects, are based on the Christian creationism, asserting that all of nature as ‘the work of God,’ reflects the invisible qualities of the Creator and His son, Christ. Firstly, “The Buffalo” praises not only the great power and prudent charity of the Creator but also the self-sacrificial service of Christ through the wild extinct Indian buffalo. Secondly, “Nine Nectarines” deals with such main themes of Christianity as immortality, faith, and the virtue of the Savior, through the main poetic images, nine wild nectarines as well as the ‘kylin.’ It is expected that this study will raise the significance of Biblical study in Moore’s poetry, expand the boundary of its interpretation, and delve into her profound poetic vision, which has not yet been clearly explored.

An Educational Virtual Reality Game from an Embodied Cognitive Perspective -Take the Game Poetry as an Example

With the rapid development of virtual reality (VR) technology, it has become a trend to apply VR technology to assist teaching in the field of education. At present, the learning of traditional poetry is often in the form of book learning. The lack of physical and sensory interaction in the learning process leads to a lack of initiative and enthusiasm for learning, resulting in low learning efficiency. Based on this background, this paper develops a poetry learning game based on VR technology, which creates interactive scenarios based on the depiction of poems and allows users to learn poems in an immersive experience. Users can complete their poetry learning objectives by experiencing the games plots and completing game tasks. Based on embodied cognition theory, the game applies the strong sense of presence and immersion of VR technology to enhance the interaction between the users body and the poetry scene, thus improving the users knowledge and understanding of the poetry, deepening their memory of the poetry and enabling them to learn in a more effective and interesting form. The game uses Cinema4D to design and model the poetry scenes and the game engine Unity to realize the interaction with the user. Through the study of the game scenes and user evaluation, the experimental results show that the game achieves the design purpose and has an application value.

Normalized effect size (NES): a novel feature selection model for Urdu fake news classification

Social media has become an essential source of news for everyday users. However, the rise of fake news on social media has made it more difficult for users to trust the information on these platforms. Most research studies focus on fake news detection in the English language, and only a limited number of studies deal with fake news in resource-poor languages such as Urdu. This article proposes a globally weighted term selection approach named normalized effect size (NES) to select highly discriminative features for Urdu fake news classification. The proposed model is based on the traditional inverse document frequency (TF-IDF) weighting measure. TF-IDF transforms the textual data into a weighted term-document matrix and is usually prone to the curse of dimensionality. Our novel statistical model filters the most discriminative terms to reduce the data’s dimensionality and improve classification accuracy. We compare the proposed approach with the seven well-known feature selection and ranking techniques, namely normalized difference measure (NDM), bi-normal separation (BNS), odds ratio (OR), GINI, distinguished feature selector (DFS), information gain (IG), and Chi square (Chi). Our ensemble-based approach achieves high performance on two benchmark datasets, BET and UFN, achieving an accuracy of 88% and 90%, respectively.

Binary structure constrained gravity inversion based on seismic first arrival travel time data

Gravity exploration method is one of the important methods for deep mineral resource exploration, but gravity data inversion has limited resolution ability in the vertical direction. In order to improve the vertical resolution of gravity data inversion, we propose a binary structure constrained gravity inversion method based on seismic first arrival travel time data. This method effectively reconstructs a density model with high vertical resolution by transferring the structural information of a high-resolution velocity model reconstructed by seismic data inversion to gravity data inversion through the binary structure constrained technique. This strategy eliminates the need to integrate both gravity and seismic methods into a single inversion framework, avoiding both the difference in convergence speeds between the two methods, as well as getting rid of the complexity associated with calculating structural coupling terms. Theoretical simulations show that the fuzzy c-means cluster analysis technique can accurately extract the target region of the velocity model reconstructed by seismic data inversion. Under the constraint of seismic structural information, the resolution of reconstructed density model is much higher than that of separate gravity data inversion, which proves that high resolution seismic information can improve the vertical resolution of gravity data inversion. Compared with the traditional cross-gradient joint inversion, the binary structure constrained gravity inversion method can further improve the resolution of the density model, especially in the reconstruction of the anomaly interface, which verifies that the method has certain effectiveness.

Parameter inversion of the diffusive–viscous wave equation based on Gaussian process regression

Abstract The diffusive–viscous wave (DVW) equation is used to characterize the relationship between frequency-dependent seismic responses and saturated fluids by incorporating the frictional dissipation and viscous damping to the scalar wave equation. Simultaneous inversion of three model parameters in DVW equation is essential for seismic interpretations. Traditional inversion methods require continuous forward-modeling updates, resulting in low computational efficiency. Moreover, the traditional methods have limitations in simultaneously inverting multi-parameters of wave equations such as the DVW equation, usually fixing one parameter to invert the other two parameters. Gaussian process regression (GPR) is a kernel-based non-parametric probabilistic model that introduces prior variables through Gaussian processes (GP). We present a method for the inversion of the three parameters (velocity, diffusive and viscous attenuation coefficients) of the DVW equation based on GPR. The procedure consists of initially implementing the central finite difference approximation to discretize the DVW equation in the time domain. Subsequently, a Gaussian prior is provided on two snapshots of the DVW equation to obtain the corresponding kernel functions. Furthermore, the hyperparameters in kernel functions and the three model parameters are simultaneously trained by minimizing the negative logarithmic marginal likelihood with few training samples while incorporating the underlying physics in terms of encoding the DVW equation into the kernel functions. It is worth noting that it is the first time of implementing three-parameter simultaneous inversion based on the DVW equation. The numerical examples in homogeneous, layered and heterogeneous media demonstrate the effectiveness of this method.

An Enhanced Atmospheric Pre-Corrected Differential Absorption (APDA) Algorithm by Extending LUTs Applied to Analyze ZY1-02D Hyperspectral Images

Water vapor is a crucial component of the atmosphere. Its absorption significantly influences remote sensing by impacting radiation signals transmitted through the atmosphere. Determining columnar water vapor (CWV) from hyperspectral remote sensing data is essential during the imagery atmospheric correction process. Over the past 40 years, numerous CWV inversion algorithms have been developed, with refinements to enhance retrieval accuracy and reliability. In this study, we proposed an enhanced atmospheric pre-corrected differential absorption (APDA) algorithm. This enhancement was achieved by thoroughly analyzing water vapor absorption in relation to elevation and aerosol optical depth and extending look up tables (LUTs). The enhanced method utilizes a pre-built MODTRAN lookup table and is applied to ZY1-02D hyperspectral data from a satellite launched in 2020. We compared the inversion results of 10 ZY1-02D scenes obtained using the improved method with AERONET measurements and inversion results from commonly used atmospheric correction software, namely, FLAASH and ATCOR. The updated algorithm demonstrated a lower average error (0.0568 g·cm−2) and relative average error (10.49%) compared to the ATCOR software (0.17 g·cm−2 and 40.78%, respectively) and the FLAASH module (0.13 g·cm−2 and 30.82%, respectively). Consequently, the enhanced method outperforms traditional CWV inversion algorithms, especially at high altitudes.

Three-Dimensional Limited-Memory BFGS Inversion of Magnetic Data Based on a Multiplicative Objective Function

At present, the traditional magnetic three-dimensional inversion method has been fully developed and is widely used. Magnetic exploration is a kind of geophysical exploration method that uses the magnetic field changes (magnetic anomalies) caused by the magnetic differences between various rocks in the crust to find useful mineral resources and study the underground structure. Traditional magnetic three-dimensional inversion is relatively inefficient. Moreover, the traditional additive objective function (data fitting difference term plus regularization term and logarithmic obstacle term), which causes the regularization factor selection to be more complicated, is implemented in this method. Therefore, it is necessary to establish a new efficient three-dimensional magnetic inversion algorithm and optimize the selection of regularization factors. In this paper, based on the limited-memory BFGS (L-BFGS) method, the three-dimensional magnetic inversion of a multiplicative objective function is realized. The inversion test is conducted in this paper using both theoretical synthesis data and measured data. The results demonstrate that the limited-memory BFGS method significantly enhances the inversion efficiency and yields superior inversion outcomes compared to traditional magnetic three-dimensional inversion methods. Additionally, the multiplicative objective function-based three-dimensional magnetic inversion method simplifies the process of selecting weight factors for regularization terms.

Improved Gravity Inversion Method Based on Deep Learning with Physical Constraint and Its Application to the Airborne Gravity Data in East Antarctica

This paper aims to solve the limitations of traditional gravity physical property inversion methods such as insufficient depth resolution and difficulties in parameter selection, by proposing an improved 3D gravity inversion method based on deep learning. The deep learning network model is established using the fully convolutional U-net network. To enhance the generalization ability of the sample set, the large-scale training set and test set are generated by the random walk, based on the forward theory. Founded on the traditional loss function’s definition, this paper introduces an improvement incorporating a physical constraint to measure the degree of data fitting between the predicted and the real gravity data. This improvement significantly boosted the accuracy of the deep learning inversion method, as verified through both a single model and an intricate combination model. Finally, we applied this improved inversion method to the gravity data from the Gamburtsev Subglacial Mountains in the interior of East Antarctica, obtaining a comprehensive 3D crustal density structure. The results provide new evidence for the presence of a dense crustal root situated beneath the central Gamburtsev Province near the Gamburtsev Suture.

Learning-Based Seismic Velocity Inversion with Synthetic and Field Data.

Building accurate acoustic subsurface velocity models is essential for successful industrial exploration projects. Traditional inversion methods from field-recorded seismograms struggle in regions with complex geology. While deep learning (DL) presents a promising alternative, its robustness using field data in these complicated regions has not been sufficiently explored. In this study, we present a thorough analysis of DL's capability to harness labeled seismograms, whether field-recorded or synthetically generated, for accurate velocity model recovery in a challenging region of the Gulf of Mexico. Our evaluation centers on the impact of training data selection and data augmentation techniques on the DL model's ability to recover velocity profiles. Models trained on field data produced superior results to data obtained using quantitative metrics like Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and R2 (R-squared). They also yielded more geologically plausible predictions and sharper geophysical migration images. Conversely, models trained on synthetic data, while less precise, highlighted the potential utility of synthetic training data, especially when labeled field data are scarce. Our work shows that the efficacy of synthetic data-driven models largely depends on bridging the domain gap between training and test data through the use of advanced wave equation solvers and geologic priors. Our results underscore DL's potential to advance velocity model-building workflows in industrial settings using previously labeled field-recorded seismograms. They also highlight the indispensable role of earth scientists' domain expertise in curating synthetic data when field data are lacking.

SCENERY TRADITION IN AZERBAIJAN POETRY (FROM VI-VII CENTURES BC TILL THE MODERN AGES)

The main aim of the article is to study the scenery tradition of Azerbaijan poetry. Literary studies as a science in Azerbaijan began to emerge in the Middle Ages. Travelogue, historical chronicle, dictionary, etc. there are many elements of literary studies in the works, biographical information about the writers is given, quotes from the works are given, the date of their creation, the plot, etc. is talked about. However, the development of national literary studies began directly at the beginning of the 20th century. The subject of the research is to explore the period from VII-VI centuries BC till the modern period of Azerbaijani poetry. The task of the research is to clarify the possibilities of folklore symbolism in scenery lyrics. Research methods: the theoretical-methodological basis of the article is the comparative-historical method. Depending on the nature of the research and investigation, the theoretical-analytical method was also used in the explanation and interpretation of the problems that were attempted to be solved scientifically. Main provision: evaluation of poetic themes based on factual materials that give rise to conditioning of landscape lyrics as an artistic practice. Application importance is the material can be used in lecturers and seminars in Higher Education Institutions, and it can be useful whom is interested in literature, poetry and the history of culture. Keywords: Azerbaijan, literature, scenery, poetry, well-known writers of Azerbaijan

A milestone in the history of Chinese modern landscape design: the Fangta Park in Shanghai

Fangta Park in Shanghai is a widely admired Chinese modern park and a representative work by Professor Feng Jizhong. A literature review is first used to explain the construction process of Fangta Park, its design philosophy, master plan, and detailed design, followed by an analysis of the park’s design. Previous studies have shown how Feng Jizhong ‘deconstructed’ Western architectural forms using traditional Chinese space concepts, thus generating novel Chinese architectural forms underpinned by Western construction concepts. He adapted rhetorical devices of Chinese traditional poetry and used them in the spatial arrangement and landscape design of Fangta Park, a modern landscape and architectural masterpiece with distinctive Chinese characteristics. Feng Jizhong promoted blending Chinese culture with modern landscape design and illustrated the process of ‘space-time transformation’ with ‘conative space’. Through the design of Fangta Park, he exhibited his individuality and profound scholarship, charting a unique course for modern landscape architecture in China.

Impact on quality and diversity from integrating a reconstruction loss into neural audio synthesis

In digital media or games, sound effects are typically recorded or synthesized. While there are a great many digital synthesis tools, the synthesized audio quality is generally not on par with sound recordings. Nonetheless, sound synthesis techniques provide a popular means to generate new sound variations. In this research, we study sound effects synthesis using generative models that are inspired by the models used for high-quality speech and music synthesis. In particular, we explore the trade-off between synthesis quality and variation. With regard to quality, we integrate a reconstruction loss into the original training objective to penalize imperfect audio reconstruction and compare it with neural vocoders and traditional spectrogram inversion methods. We use a Wasserstein GAN (WGAN) as an example model to explore the synthesis quality of generated sound effects, such as footsteps, birds, guns, rain, and engine sounds. In addition to synthesis quality, we also consider the range of sound variation that is possible with our generative model. We report on the trade-off that we obtain with our model regarding the quality and diversity of synthesized sound effects.

New lyric writing: Blurring the boundary between popular and high culture

This article aims to give an overview of the new forms of lyric writing by minorities in the United Kingdom, France and beyond. In the context of a globalized, postcolonial, internet era, a variety of forms and modes of broadcasting – from hip hop lyrics to instapoetry via verse novels and more traditional poetry collections – are characterized by an intersubjective lyricism that aims to transform and valorize the experiences of marginalized and minority writers. Out of the diversity of voices and forms, this article shows the shared poetics and features of this lyric writing as well as the solidarities across borders and different forms of marginalization that are often present and opens up lines of reflection and theorization, notably in relation to the role of internet and the developing cultural context of converging popular cultures and national and postcolonial literary traditions.

Wanderers – Pilgrims – Soldiers – Emigrants

The main purpose of the article is to review imageries and experiences of a specific group of poets organized within the frame of the Second Polish Corps during World War II, and to analyze its most characteristic motifs within a deeper historical context of Polish romanticism, as “replayed” in the paths and roles of the Corps poets-soldiers. The study is based on the extensive use of all available books of verses and other literary publications of the Corps, which were produced on the long way from the Soviet Union, where the bulk of Corps was formed in 1941–1942, through the Middle East, Italy, to the final exile station, London in most cases. This material is analyzed within the reference framework of the Polish poetry of World War II studies, as well as comparative studies on the Polish romantic literary tradition, especially that of the Great Emigration. This comparative point of view and method of analysis is used in all parts of the chronologically organized text. From a short presentation of the history of the Corps variegated cultural life which accompanied its development from 1941 to 1945, it proceeds to similarly succinct picture of the group of more than 80 active poets publishing in the Corps. The study centers its analysis on the main theme forming the time-perception of the world presented in soldier poetry: the motif of the road or path. It reveals a phenomenon of uniting very different poetic “schools” and styles (from extreme avant-gardist and futurist, through neoromanticism, to cabaret texts) under the pressure of the war and exile experiences, as well as the strength of the great romantic tradition of Polish poetry formed along a very similar path: from Russia, Siberia, through the Middle East, to Italy, where Polish soldiers had fought under Napoleon, to London and Paris, centers of the Great Emigration. Topoi of martyrdom, strengthened through Siberia and the Holy Land connections, homelessness, martial experiences in Italy, a “treason” of Western allies (Teheran and Yalta conferences) are effectively “refreshed” in this comparative approach.

Processing of electrical resistivity tomography data using convolutional neural network in ERT-NET architectures

Electrical resistivity tomography (ERT) inversion has emerged as an effective method for predicting resistivity in complex geological structures. In most cases, traditional ERT inversion problems are posed as nonlinear optimization problems. Solving distribution resistivity inversion can be computationally challenging for two reasons: one is the significant cost of software and the other is the issue of local minima. The ERT-NET architecture was developed in this study to learn the parameter regression relationship between geophysical ERT datasets and subsurface models. We developed a novel convolutional neural network (CNN) technique that comprised of a fully connected network (ERT-INET) and a fully convolutional network (ERT-UNET); both train ambiguity information of the inverted resistivity based on processing ERT datasets. We also output our network segmentation of pixel-wise prediction for ERT-INET and structured prediction segmentation. The noise assessments of our network inversion were managed by employing depth of investigation (DOI) and statistical analysis for evaluation performance. The DOI appeared to be effective in conveying the breadth of possibility within our networks. Moreover, the performances are either the synthetic resistivity model or the field resistivity data, both of which have an average of greater than 95%. The inversion results of both architectures are precisely and accurately expressed, containing approximately the ground truth models and thereby also the field observation models. We conclude that these ERT-NET architectures could be one approach to ERT interpretation handling, and we strongly suggest alternatives that promote the geoelectrical method of interpretation.

Seismic Elastic Parameter Inversion via a FCRN and GRU Hybrid Network with Multi-Task Learning

Seismic elastic parameter inversion translates seismic data into subsurface structures and physical properties of formations. Traditional model-based inversion methods have limitations in retrieving complex geological structures. In recent years, deep learning methods have emerged as preferable alternatives. Nevertheless, inverting multiple elastic parameters using neural networks individually is computationally intensive and can lead to overfitting due to a shortage of labeled data in field applications. Multi-task learning can be employed to invert elastic parameters simultaneously. In this work, a hybrid network that leverages the fully convolutional residual network (FCRN) and the gated recurrent unit network (GRU) is designed for the simultaneous inversion of P-wave velocity and density from post-stack seismic data. The FCRN efficiently extracts local information from seismic data, while the GRU captures global dependency over time. To further improve the horizontal continuity and inversion stability, we use a multi-trace to single-trace (M2S) inversion strategy. Consequently, we name our proposed method the M2S multi-task FCRN and GRU hybrid network (M2S-MFCRGRU). Through anti-noise experiments and blind well tests, M2S-MFCRGRU exhibits superior anti-noise performance and generalization ability. Comprehensive experimental inversion results also showcase the excellent lateral continuity, vertical resolution, and stability of the M2S-MFCRGRU inversion results.

Modern Türk ve Arap Şiirinde Anka Kuşu Motifine Batı Etkisi: Karşılaştırmalı Bir İnceleme

This article conducts a comparative analysis of the Phoenix motif in Turkish and Arabic poetry, focusing on how Western literary traditions have influenced its evolution and manifestation. The Phoenix motif, a potent symbol of rebirth and transformation, is a key area of study within these distinct but intersecting literary traditions. This research’s purpose is to identify and compare the historical, cultural, and political factors that have shaped the motif’s use and evolution within each context. Employing a comparative methodology, the research dissects the multi-layered influences of Western literary traditions on the Phoenix motif’s use in these two vibrant poetry traditions. This examination has a broad scope, spanning centuries of Turkish and Arabic poetry, and it seeks to underscore the importance of cross-cultural literary influences in shaping poetic motifs. The aim of this research is to deepen our understanding of how a shared symbol, such as the Phoenix, can diverge in meaning and usage due to varying cultural and literary contexts, demonstrating the richness and complexity of these poetic traditions. Through its exploration, this paper contributes significantly to our knowledge of the interconnectedness of world literary traditions and their capacity for cross-cultural symbiosis.

2D magnetotelluric inversion based on ResNet

In this study, a deep learning algorithm was applied to two-dimensional magnetotelluric (MT) data inversion. Compared with the traditional linear iterative inversion methods, the MT inversion method based on convolutional neural networks (CNN) does not rely on the selection of the initial model parameters and does not fall into the local optima. Although the CNN inversion models can provide a clear electrical interface division, their inversion results may remain prone to abrupt electrical interfaces as opposed to the actual underground electrical situation. To solve this issue, a neural network with a residual network architecture (ResNet-50) was constructed in this study. With the apparent resistivity and phase pseudo-section data as the inputs and with the resistivity parameters of the geoelectric model as the training labels, the modified ResNet-50 model was trained end-to-end for producing samples according to the corresponding production strategy of the study area. Through experiments, the training of the ResNet-50 with the dice loss function effectively solved the issue of over-segmentation of the electrical interface by the cross-entropy function, avoided its abrupt inversion, and overcame the computational inefficiency of the traditional iterative methods. The proposed algorithm was validated against MT data measured from a geothermal field prospect in Huanggang, Hubei Province, which showed that the deep learning method has opened up broad prospects in the field of MT data inversion.

The Possibilities of Plant Vocalization

The poetic possibilities of plant vocalization take new directions in the poetry of Lőrinc Szabó and Attila József, feeding on the traditions of romantic and modern poetry, but also renewing them, as we see in the translations and reading methods. They come into contact with the poems of Percy Shelley, Charles Baudelaire, and George Meredith. The study focuses on two poems: Attila József’s “Trees” [Fák] and Lőrinc Szabó’s “In the Black Forest” [A Fekete Erdőben] contextualized with other poems in the oeuvre, the antecedents of the poetic forms, and the comparative influences. In the case of “Trees”, plant thinking and biological operations are paralleled, creating the sound effects of the poem together; in the poem “In the Black Forest”, the equivalence of human and plant life is emphasized through sensual-poetic communication.