1,720,964 research outputs found
Monitoring Elastic Parameters Changes during Underground Hydrogen Storage Using Rock Physics Parametrized FWI
No full textSeasonal storage of hydrogen produced from renewable energy can become one of the key strategies tomeet the high energy demand of today’s society. The most suitable sites for seasonal hydrogen storageare depleted gas fields. This study proposes a workflow based on the integration of full-waveforminversion (FWI), rock physics modeling (RPM) and gas property modeling for monitoring changes inelastic medium parameters due to hydrogen injection. A rock physics model including the Gassmannequation and fluid mixing laws has been implemented, which accurately links rock physical propertiesto elastic properties. The parameterized approach is based on optimizing fluid saturation to reducecrosstalk between model parameters during the inversion process, while simultaneously providing aquantitative estimation of the fluid within the reservoir. The synthetic models show that parameterizedinversion produces higher accuracy and fewer artifacts than conventional FWI. Our results underscorethe importance of an appropriate RPM to reflect real subsurface conditions and proper fluid mixinglaws. Therefore, FWI parameterization provides an efficient technique for monitoring hydrogen storagesites in depleted gas fields to ensure efficient storage
Deep attributes: innovative LSTM-based seismic attributes
Full text linkSeismic attributes are derived measures from seismic data that help characterize subsurface geological features and enhance the interpretation of subsurface structures: we propose to exploit the hidden layers of Long-Short Time Memory neural network predictions as possible new reflection seismic attributes. The idea is based on the inference process of a neural network, which in its hidden layers stores information related to different features embedded in the input data and which usually are not considered. Neural network applications typically ignore such intermediate steps because the main interest lies in the final output, which is considered as the exclusive exploitable feature of the process. On the contrary, here we analyse the possibility to exploit the intermediate prediction steps, hereafter referred as “Deep Attributes” because they are produced by a deep learning algorithm, to highlight features and emphasize characteristics embedded in the data but neither recognizable by traditional interpretation, nor evident with classical attributes or multi-attribute approaches. Nowadays, classical signal attributes are numerous and used for different purposes; we here propose an original strategy to calculate attributes previously never exploited, which are potentially complementary or a good alternative to the classical ones. We tested the proposed procedure on synthetic and field 2-D and 3-D reflection seismic data sets to test and demonstrate the stability, affordability and versatility of the entire approach. Furthermore, we evaluated the performance of deep attributes on a 4-D seismic dataset to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute
Metal Ion Isotope Ratio Using ESI-Orbitrap HRMS: Proof of Concept and Initial Performance Evaluation for Lead Isotopic Ratios
No full textThis study introduces a novel approach using an electrospray source coupled to an Orbitrap MS instrument to determine metal isotope ratios. The procedure involves forming a complex between the ion of interest and an appropriate ligand, generating gas-phase ions via electrospray ionization, selecting the complex mass by quadrupole filtering, and performing collisional fragmentation to yield free metal ions. The isotopic pattern of the free ion is then analyzed by high-resolution MS. The approach ensures high selectivity and interference-free spectra. A proof-of-concept study was conducted to determine Pb isotope ratios, focusing on identifying the factors that influence the accuracy and precision of the procedure. At this early stage, optimal accuracy was achieved even in the presence of matrix components by applying mass bias correction methods originally developed for other isotope ratio techniques; precision is comparable to that achieved by single-collector ICP-MS instrumentation. This approach may complement conventional methods that suffer from limited mass resolution and usually require extensive sample preparation
Efficient extraction of seismic reflection with Deep Learning
Full text linkWe propose a procedure for the interpretation of horizons in seismic reflection data based on a Neural Network (NN) approach, which can be at the same time fast, accurate and able to reduce the intrinsic subjectivity of manual or control-points based methods. The training is based on a Long Short Term Memory architecture and is performed on synthetic data obtained from a convolutional model-based scheme, while the extraction step can be applied to any type of field seismic dataset. Synthetic data are contaminated with different types of noise to improve the performance of the NN in a large variety of field conditions. We tested the proposed procedure on 2-D and 3-D synthetic and field seismic datasets. We have successfully applied the procedure also to Ground Penetrating Radar data, verifying its versatility and potential. The proposed algorithm is based on a fully 1-D approach and does not require the input of any interpreter, because the necessary thresholds are automatically estimated. An added benefit is that the prediction has an associated probability, which automatically quantifies the reliability of the results
Synthetic seismic data generation with deep learning
Full text linkWe study the applicability of deep learning (DL) methods to generate acoustic synthetic data from 1D models of the subsurface. We designed and implemented a Neural Network (NN) and we trained it to generate synthetic seismograms (common shot gathers) from 1-D velocity models on two different datasets: one obtained from published results and the other generated by Finite Differences (FD) numerical simulation. We furthermore compared the results from the proposed model with the published one. Moreover, we tried to to add more flexibility to this methodology by allowing change of wavelet and the acquisition geometry. We obtained good results in terms of both computation efficiency and quality of prediction. The main potentialities of the work are the low computational cost, a high prediction speed and the possibility to solve complex non-linear problems without knowing the physical law behind the phenomenon, which could led great advantages in the solution also of the inverse problem. DL to generate 1-D acoustic synthetic seismograms without solving wave equation Solution to the 1-D problem through custom Recurrent Neural Network Retraining strategy to improve flexibility and applicability Computational complexity analysis
Deep learning-based multifrequency ground penetrating radar data merging
Full text linkGround-penetrating radar systems with a single central frequency suffer limitations due to the unavoidable tradeoff between resolution and penetration depth that multifrequency equipments can overcome. A new semisupervised multifrequency merging algorithm was developed based on deep learning and specifically on bi-directional longshort term memory to automatically merge varying numbers of data sets collected at different frequencies. A new training strategy, based only on the data set of interest, without synthetic or real training data sets was implemented. The proposed methodology is tested on synthetic and field data, to evaluate its performance and robustness. The merging algorithm can manage the complementarity of information at different central frequencies, properly merging different types of data. Results indicate not only a smooth transition in time, but, even more important, a remarkable broadening of the bandwidth thus increasing the overall resolution. Our approach is not limited to specific frequency components or geologic settings but can be potentially exploited to merge any type of data set with different spectral components
Polarity assessment of reflection seismic data: a Deep Learning approach
Full text linkWe propose a procedure for the polarity assessment in reflection seismic data based on a Neural Network approach. The algorithm is based on a fully 1D approach, which does not require any input besides the seismic data since the necessary parameters are all automatically estimated. An added benefit is that the prediction has an associated probability, which automatically quantifies the reliability of the results. We tested the proposed procedure on synthetic and real reflection seismic data sets. The algorithm is able to correctly extract the seismic horizons also in case of complex conditions, such as along the flanks of salt domes, and is able to track polarity inversions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
