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We present the joint astrometric and direct imaging discovery, mass measurement, and orbital analysis of HD 63754 B (HIP 38216 B), a companion near the stellar-substellar boundary orbiting |$\sim$| 20 au from its Sun-like host. HD 63754 was observed in our ongoing high-contrast imaging survey targeting stars with significant proper-motion accelerations between Hipparcos and Gaia consistent with wide-separation substellar companions. We utilized archival High Resolution Echelle Spectrograph and High Accuracy Radial Velocity Planet Searcher radial velocity (RV) data, together with the host star's astrometric acceleration extracted from the Hipparcos – Gaia Catalog of Accelerations, to predict the location of the candidate companion around HD 63754 A. We subsequently imaged HD 63754 B at its predicted location using the Near Infrared Camera 2 (NIRC2) in the |$L^{\prime }$| band at the W. M. Keck Observatory. We then jointly modelled the orbit of HD 63754 B with RVs, Hipparcos – Gaia accelerations, and our new relative astrometry, measuring a dynamical mass of |${81.9}_{-5.8}^{+6.4} {M_{\rm Jup}}$|⁠ , an eccentricity of |${0.260}_{-0.059}^{+0.065}$|⁠ , and a nearly face-on inclination of |$174.\!\!^\circ 81_{-0.50}^{+0.48}$|⁠. For HD 63754 B, we obtain an |$L^{\prime }$| -band absolute magnitude of |$L^{\prime } = 11.39\pm 0.06$| mag, from which we infer a bolometric luminosity of |$\rm{log(L_{\rm bol}/{\rm{L}_{\odot}})= -4.55 \pm 0.08}$| dex using a comparison sample of L and T dwarfs with measured luminosities. Although uncertainties linger in age and dynamical mass estimates, our analysis points towards HD 63754 B's identity as a brown dwarf on the L/T transition rather than a low-mass star, indicated by its inferred bolometric luminosity and model-estimated effective temperature. Future RV, spectroscopic, and astrometric data such as those from JWST and Gaia Data Release 4 will clarify HD 63754 B's mass, and enable spectral typing and atmospheric characterization. [ABSTRACT FROM AUTHOR]

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology. [ABSTRACT FROM AUTHOR]

SUMMARY: The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation. [ABSTRACT FROM AUTHOR]

In a strong gravitational lensing system, the distorted light from a source is analysed to infer the properties of the lens. However, light emitted by the lens itself can contaminate the image of the source, introducing systematic errors in the analysis. We present a simple and efficient lens light model based on the well-tested multi-Gaussian expansion (MGE) method for representing galaxy surface brightness profiles, which we combine with a semi-linear inversion scheme for pixelized source modelling. Testing it against realistic mock lensing images, we show that our scheme can fit the lensed images to the noise level, with relative differences between the true input and best-fitting lens light model remaining below 5 per cent. We apply the MGE lens light model to 38 lenses from the HST SLACS sample. We find that the new scheme provides a good fit for the majority of the sample with only 3 exceptions – these show clear asymmetric residuals in the lens light. We examine the radial dependence of the ellipticity and position angles and confirm that it is common for a typical lens galaxy to exhibit twisting non-elliptical isophotes and boxy / disky isophotes. Our MGE lens light model will be a valuable tool for understanding the hidden complexity of the lens mass distribution. [ABSTRACT FROM AUTHOR]

SUMMARY: To address complex subsurface structures, a high-resolution velocity model must be constructed. Conventionally, algorithms such as full waveform inversion (FWI) have been used to derive accurate high-resolution velocity structures, but obstacles such as high computational costs remain. Therefore, we propose a high-resolution U-NET (HR U-NET) machine learning model to derive a high-resolution velocity model from a low-resolution velocity model. The low-resolution velocity model and migration data obtained through the corresponding velocity information were used as input data for training. In addition, we tried to improve the accuracy of the high-resolution velocity model by using prior information containing accurate velocity values. A prior model generated through geophysical logging data and a weight model including the reliability information of the prior model were also utilized. Therefore, the HR U-NET model was trained using the low-resolution velocity model, the migration data, the prior model and the weight model. Numerical experiments conducted using synthetic and field data demonstrated that the proposed model could accurately construct a high-resolution velocity model and verified that the prior model and weight model play important roles in the training process. Additionally, we confirmed that the proposed method derived almost similar results using only 8.2 percent of the computational cost of the conventional inversion method. In other words, there is an advantage that it is possible to predict high-resolution velocity information more efficiently in terms of computational cost. [ABSTRACT FROM AUTHOR]

We present a search for transient radio sources on time-scales of seconds to hours at 144 MHz using the LOFAR Two-metre Sky Survey (LoTSS). This search is conducted by examining short time-scale images derived from the LoTSS data. To allow imaging of LoTSS on short time-scales, a modern imaging procedure and fast filtering strategy are introduced. This includes sky model source subtraction, no cleaning or primary beam correction, a simple source finder, fast filtering schemes, and source catalogue matching. This new strategy is first tested by injecting simulated transients, with a range of flux densities and durations, into the data. We find the limiting sensitivity to be 113 and 6 mJy for 8 s and 1 h transients, respectively. The new imaging and filtering strategies are applied to 58 fields of the LoTSS survey, corresponding to LoTSS-DR1 (2 per cent of the survey). One transient source is identified in the 8 s and 2 min snapshot images. The source shows 1 min duration flare in the 8 h observation. Our method puts the most sensitive constraints on/estimates of the transient surface density at low frequencies at time-scales of seconds to hours; <4.0 × 10−4 deg−2 at 1 h at a sensitivity of 6.3 mJy; 5.7 × 10−7 deg−2 at 2 min at a sensitivity of 30 mJy; and 3.6 × 10−8 deg−2 at 8 s at a sensitivity of 113 mJy. In the future, we plan to apply the strategies presented in this paper to all LoTSS data. [ABSTRACT FROM AUTHOR]

Summary perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. Availability and implementation The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. [ABSTRACT FROM AUTHOR]

We present extended point spread function (PSF) models for the Hyper Suprime-Cam Subaru Strategic Program Public Data Release 3 (HSC-SSP PDR3) in all g, r, i, Z , and Y -bands. Due to its 8.2 m primary mirror and long exposure periods, HSC combines deep images with wide-field coverage. Both properties make HSC one of the most suitable observing facilities for low-surface brightness (LSB) studies, which are particularly sensitive to the PSF. By applying a median stacking technique of point-like sources with different brightness, we show how to construct the HSC-SSP PDR3 PSF models to an extent of R ∼ 5.6 arcmin. These models are appropriate for the HSC-PDR3 intermediate-state data which do not have applied the final aggressive background subtraction. The intermediate-state data is especially stored for users interested in large extended objects, where our new PSFs provide them with a crucial tool to characterise LSB properties at large angles. We demonstrate that our HSC PSFs behave reasonably in two scenarios. In the first one, we generate 2D models of a bright star, showing no evidence of residual structures across the five bands. In the second scenario, we recreate the PSF-scattered light on mock images with special consideration of the effect of this additional flux on LSB measurements. Despite the well-behaved nature of the HSC-PDR3 PSFs, there is a non-negligible impact on the faint light present in the mock images. This impact could lead to incorrect LSB measurements if a proper star subtraction is not applied. The PSF models are publicly available to the astronomical community at github.com/luciagarate/HSC_PSFs (DOI: 10.5281/zenodo.11273118). [ABSTRACT FROM AUTHOR]

We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure (∼0.2 s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image. [ABSTRACT FROM AUTHOR]

Adaptive optics (AO) systems correct optical phase aberrations of the incoming light generated by the atmosphere. To do so simultaneous estimators of the atmospheric turbulence parameters are required. For the family of wide-field AO (WFAO) systems, this information must be stratified in altitude. Among these vectorized estimations, wind profiling in altitude is needed for the reduction of temporal errors in AO loops or for the estimation of turbulence coherence time. This paper proposes a turbulence wind profiler called image processing based peak tracking algorithm (ipta). ipta is an image-processing based approach that automatically and reliably estimates wind velocity for several turbulent layers along the line of sight. The estimation of each wind layer is achieved by tracking peaks produced in cross-correlation maps from pairs of wavefront sensors (WFSs) slopes using the technique known as SLODAR (slope detection and ranging). Results for simulated and on-sky WFS data sets demonstrate that ipta outperforms one of the state of the art wind profiler methods [the profiler covariance parametrization of wind velocity (CAW)] in terms of accuracy and speed. Results also show that, in terms of execution time, our method scales better when the number of WFS lenslets is increased. Being an open source and reliable tool, we believe ipta can be a useful wind profiler for the AO community. [ABSTRACT FROM AUTHOR]

The physical mechanisms controlling deep-focus earthquakes, or those observed at depths greater than 300 km, remain enigmatic. The leading processes by which deep-focus earthquakes are thought to occur include transformational faulting, thermal runaway and dehydration embrittlement, but distinguishing observations in support of one or more prevailing mechanisms are needed. In this study, we use a modified back-projection method, data recorded by the Hi-net array in Japan and a 3-D velocity model to produce source images of 19 deep-focus earthquakes within the Izu-Bonin subduction zone. We find that the rupture properties and fault plane orientations of imaged events separate according to reported moment magnitude, indicating the distinct operation of two moment-dependent causal mechanisms of deep-focus earthquakes in this region. We discuss these results in the context of previous observational, laboratory and numerical studies and emphasize the importance of continued research to validate the dual-mechanism hypothesis both in and outside Izu-Bonin. Such work may not only improve our understanding of the nucleation and propagation of deep-focus earthquakes, but also help clarify slab structure and subduction zone dynamics. [ABSTRACT FROM AUTHOR]

Seismic 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 data set to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) decorrelation that creates great challenges to phase unwrapping has been a critical issue for mapping large earthquake deformation. Some studies have proposed a 'remove-and-return model' solution to tackle this problem, but it has not been fully validated yet, and therefore has rarely been applied to real earthquake scenarios. In this study, we use the 2023 M w 7.8 and 7.6 earthquake doublet in Turkey and Syria as a case example to develop an iterative modelling method for InSAR-based coseismic mapping. We first derive surface deformation fields using Sentinel-1 offset tracking and Sentinel-2 optical image correlation, and invert them for an initial coseismic slip model, based on which we simulate InSAR coseismic phase measurements. We then remove the simulated phase from the actual Sentinel-1 phase and conduct unwrapping. The simulated phase is added back to the unwrapped phase to produce the final phase measurements. Comparing to the commonly used unwrapping method, our proposed approach can significantly improve coherence and reduce phase gradients, enabling accurate InSAR measurements. Combining InSAR, offset tracking and optical image correlation, we implement a joint inversion to obtain an optimal coseismic slip model. Our model shows that slip on the Çardak Fault is concentrated on a ∼100 km segment; to both ends, slip suddenly diminished. On the contrary, rupture on the East Anatolian Fault Zone propagated much longer as its geometry is fairly smooth. The iterative coseismic modelling method is proven efficient and can be easily applied to other continental earthquakes. [ABSTRACT FROM AUTHOR]

Muography is an imaging technique that relies on the attenuation of the muon flux traversing geological or anthropogenic structures. Several simulation frameworks help to perform muography studies by combining specialized codes: for muon generation through muon transport to muon detector performance. This methodology is precise but requires significant computational resources and time. We present an end-to-end python-based MUographY Simulation Code, which implements a muography simulation framework capable of rapidly estimating muograms of any geological structure worldwide. This framework considers the generated muon flux as the observation point; the energy loss of muons passing through the geological target; the integrated muon flux detected by the telescope and estimates the 3-D density distribution of the target using algebraic reconstruction techniques. The simulations ignore the relatively small muon flux variance caused by geomagnetic effects, solar modulation and atmospheric conditions. We validate the code performance by comparing our simulation results with data from other frameworks. [ABSTRACT FROM AUTHOR]

Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analysed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel 3-D reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefitting from the seed algorithm, a low-density-contrast ore body and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to assist geophysicists in determining the first-break event in a 3-D seismic data set using the well-known technique called dynamic time warping algorithm (DTW), which is usually used to find the optimal alignment between two time-series. We used the optimal path from the cost matrix to identify the first break in the seismogram using a few picks (seeds) made by an interpreter as a reference to perform this task. Furthermore, the data were pre-conditioned by the topographic and linear moveout to improve the method's accuracy. To demonstrate the technique's robustness, first, we applied the methodology in a synthetic seismic data. After demonstrating the efficiency of the algorithm, we applied the aforementioned methodology in the Polo-Miranga 3-D seismic cube located in the Recôncavo sedimentary basin, Bahia-Brazil, and in the seismic data acquired from the Blackfoot field in Alberta, Canada. The high-quality results showed consistency in determining the first break in all ranges of offsets, demonstrating an alternative way to accelerate this seismic processing step. Furthermore, we compared the results obtained by the proposed methodology with an algorithm based on comparing the short-time averages with long-time averages. Finally, we performed the static correction calculation to ensure that the time distortion resulting from the terrain and the low-velocity layer was mitigated in shoot gathers and in the stacked section. [ABSTRACT FROM AUTHOR]

Dense array observation and seismic interferometry have revolutionized the imaging schemes of the earth structure. It is becoming possible to directly obtain the lateral variation of the earth's structure by applying array-based methods such as the cross-correlation beamforming (CBF) of the ambient noise to the subsets of the dense array, without tomography. CBF has been proven to extract the azimuth-averaged multimode surface wave dispersion curves. However, the resolution of the dispersion image generated by conventional CBF is low at high frequencies in the frequency–velocity (f - v) domain. Moreover, the irregular array geometry and uneven source distribution would bias the result of CBF, especially for the estimation of azimuth-dependent velocity. In this paper, two beamforming (BF) es are suggested to improve the resolution of multimode dispersion images in the f - v domain. First, the geometrical spreading of the wavefield is corrected to enhance the amplitude at high frequency (or large distance) and thereby improve the resolution of the dispersion image at high frequency. We call this scheme weighted correlation beamforming (WCBF). The azimuth-averaged velocity can be estimated with sufficient resolution using WCBF by stacking the BF output at each azimuth. We show that WCBF is the 2-D Fourier transform of the spatial wavefield from the viewpoint of the wavefield transform. Secondly, a modified beamforming scheme (MCBF) is suggested to reduce the effect of uneven source and/or irregular array geometry. The delay and summation in MCBF are performed only for plane waves incident from the stationary phase region. The azimuth-dependent velocity can therefore be estimated by MCBF with less dependence on the array geometry, as well as on the uneven source distribution. In terms of the estimation of azimuth-averaged phase velocity, we show the F-J method, another array-based method for extracting multimode surface waves from ambient noise using the Fourier–Bessel transform, is the azimuth-averaged version of WCBF. The reliability of WCBF and MCBF is verified based on the synthetic and field data using the array with different geometry. The dispersion image of multimode Rayleigh wave phase velocity at local and regional scales can be generated by WCBF or MCBF with high resolution. In particular, multimode dispersion curves at the local scale can be measured by MCBF with sufficient accuracy using quite short recordings from hours to days. This offers the possibility of a rapid assessment of the media properties. [ABSTRACT FROM AUTHOR]

Earthquake source parameters can be estimated using seismological observations, but the identification of the fault responsible is often complicated by location uncertainties and the inherent ambiguity between nodal planes. Satellite Interferometric Synthetic Aperture Radar (InSAR) can be used to observe ground deformation and model fault geometry but is limited by climate conditions (water vapour) and ground coverage (dense vegetation). In the tropics, the atmosphere is dynamic and most regions are densely vegetated, making detecting coseismic deformation challenging. Here, we perform a systematic inspection of coseismic interferograms from Sentinel-1 SAR images, to assess their suitability for detecting coseismic deformation in Costa Rica. Using data from the seismological network, we target seven earthquakes between 2016 and 2020 with depths |$\le \, 20$| km and magnitudes M w 5.3–6.2. For each event, we use the seismic parameters to compute line-of-sight displacements for ascending and descending geometries and for both nodal planes and generate 12- and 24-d coseismic interferograms where available. We obtain interferograms with coseismic displacement signals for three of the seven earthquakes. We invert the geodetic data to retrieve the earthquake source parameters but the lack of offshore geodetic coverage causes trade-offs between parameters and large uncertainties. The Jacó and Golfito earthquakes likely occurred on the subduction interface and the geodetic locations were 6–9 km closer to the coast than previous seismic estimates. The Burica earthquake occurred on a shallow steeply dipping thrust fault in the outer forearc. For the other earthquakes, no coseismic deformation was detected due to atmospheric noise or poor coherence. These results demonstrate the suitability of 12-d Sentinel-1 interferograms for monitoring shallow earthquakes of magnitude > M w 5.7 in Central America. This approach can be used to begin a surface deformation catalogue for the region, which will ultimately help improve the understanding of active deformation processes and improve hazard maps. [ABSTRACT FROM AUTHOR]

Motivation Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio. Results To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset—CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking. Availability and implementation The source code and data for CryoTransformer are openly available at: https://github.com/jianlin-cheng/CryoTransformer. [ABSTRACT FROM AUTHOR]

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as noise and galactic foreground contamination that pose a challenge for accurate detection. In this study, we present the SegU-Net v2 framework, an enhanced version of our convolutional neural network, built to identify neutral and ionized regions in the 21-cm signal contaminated with foreground emission. We trained our neural network on 21-cm image data processed by a foreground removal method based on Principal Component Analysis achieving an average classification accuracy of 71 per cent between redshift z  = 7 and 11. We tested SegU-Net v2 against various foreground removal methods, including Gaussian Process Regression, Polynomial Fitting, and Foreground-Wedge Removal. Results show comparable performance, highlighting SegU-Net v2 's independence on these pre-processing methods. Statistical analysis shows that a perfect classification score with |${\rm AUC}=95~{{\ \rm per\ cent}}$| is possible for 8 < z < 10. While the network prediction lacks the ability to correctly identify ionized regions at higher redshift and differentiate well the few remaining neutral regions at lower redshift due to low contrast between 21-cm signal, noise, and foreground residual in images. Moreover, as the photon sources driving reionization are expected to be located inside ionized regions, we show that SegU-Net v2 can be used to correctly identify and measure the volume of isolated bubbles with |$V_{\rm ion}\gt (10\, {\rm cMpc})^3$| at z > 9, for follow-up studies with infrared/optical telescopes to detect these sources. [ABSTRACT FROM AUTHOR]

The low surface brightness (LSB) regime (μg  ≳  26 mag arcsec−2) comprises a vast, mostly unexplored discovery space, from dwarf galaxies to the diffuse interstellar medium. Accessing this regime requires precisely removing instrumental signatures and light contamination, including, most critically, night sky emission. This is not trivial, as faint astrophysical and instrumental contamination can bias sky models at the precision needed to characterize LSB structures. Using idealized synthetic images, we assess how this bias impacts two common LSB-oriented sky-estimation algorithms: (1) masking and parametric modelling, and (2) stacking and smoothing dithered exposures. Undetected flux limits both methods by imposing a pedestal offset to all derived sky models. Careful, deep masking of fixed sources can mitigate this, but source density always imposes a fundamental limit. Stellar scattered light can contribute ∼28–29 mag arcsec−2 of background flux even in low-density fields; its removal is critical prior to sky estimation. For complex skies, image combining is an effective non-parametric approach, although it strongly depends on observing strategy and adds noise to images on the smoothing kernel scale. Preemptive subtraction of fixed sources may be the only practical approach for robust sky estimation. We thus tested a third algorithm, subtracting a preliminary sky-subtracted coadd from exposures to isolate sky emission. Unfortunately, initial errors in sky estimation propagate through all subsequent sky models, making the method impractical. For large-scale surveys like Legacy Survey of Space and Time, where key science goals constrain observing strategy, masking and modelling remain the optimal sky estimation approach, assuming stellar scattered light is removed first. [ABSTRACT FROM AUTHOR]

Spectral induced polarization (SIP) and electrical impedance tomography (EIT) are advanced approaches for structural characterisation of soils and rocks based on measurements of the frequency-dependent complex electrical resistivity. Accurate SIP and EIT measurements require the reduction of electrode effects, which have often been attributed to the polarization of metal electrodes in the current pathway. In this study, we demonstrate an alternative electrode effect that influences the accuracy of complex impedance measurements. This alternative electrode effect is caused by a heterogeneous distribution of the contact impedance, which can occur due to ageing or corrosion of the electrode. First, theoretical considerations are given to explain how this electrode effect influences the accuracy of complex impedance measurements. Next, we demonstrate using SIP and EIT measurements that this alternative mechanism better explains the observed electrode effects than previous explanations based only on the presence of metal. The SIP measurements were made on a water-filled container with a metal sphere between two non-polarizing electrodes, which showed that the mere presence of metal in the current pathway only leads to small electrode effects that are typically well below 1 mrad even for small electrode spacings. When the same metal sphere was used as a potential electrode, the observed electrode effect was about 2.5 times larger and varied with rotation. These changes can be explained by a heterogeneous contact impedance resulting in a varying electrode voltage. The EIT measurements were performed with a cylindrical sample holder filled with water. They are used to illustrate how the presented theoretical considerations can be used to investigate the magnitude of the electrode effects for different electrode configurations. We conclude that electrode effects are important to consider for laboratory SIP and EIT measurements and field measurement with closely spaced electrodes. [ABSTRACT FROM AUTHOR]

The upcoming Nancy Grace Roman Space Telescope will carry out a wide-area survey in the near-infrared. A key science objective is the measurement of cosmic structure via weak gravitational lensing. Roman data will be undersampled, which introduces new challenges in the measurement of source galaxy shapes; a potential solution is to use linear algebra-based coaddition techniques such as imcom that combine multiple undersampled images to produce a single oversampled output mosaic with a desired 'target' point spread function (PSF). We present here an initial application of imcom to 0.64 square degrees of simulated Roman data, based on the Roman branch of the Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) simulation. We show that imcom runs successfully on simulated data that includes features such as plate scale distortions, chip gaps, detector defects, and cosmic ray masks. We simultaneously propagate grids of injected sources and simulated noise fields as well as the full simulation. We quantify the residual deviations of the PSF from the target (the 'leakage'), as well as noise properties of the output images; we discuss how the overall tiling pattern as well as Moiré patterns appear in the final leakage and noise maps. We include appendices on interpolation algorithms and the interaction of undersampling with image processing operations that may be of broader applicability. The companion paper ('Paper II') explores the implications for weak lensing analyses. [ABSTRACT FROM AUTHOR]

Motivation BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP's first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. Results Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. Availability and implementation BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d. [ABSTRACT FROM AUTHOR]

Seismic interrogation of the upper mantle from the base of the crust to the top of the mantle transition zone has revealed discontinuities that are variable in space, depth, lateral extent, amplitude and lack a unified explanation for their origin. Improved constraints on the detectability and properties of mantle discontinuities can be obtained with P-to-S receiver function (Ps-RF) where energy scatters from P to S as seismic waves propagate across discontinuities of interest. However, due to the interference of crustal multiples, uppermost mantle discontinuities are more commonly imaged with lower resolution S-to-P receiver function (Sp-RF). In this study, a new method called CRISP-RF (Clean Receiver-function Imaging using SParse Radon Filters) is proposed, which incorporates ideas from compressive sensing and model-based image reconstruction. The central idea involves applying a sparse Radon transform to effectively decompose the Ps-RF into its underlying wavefield contributions, that is direct conversions, multiples, and noise, based on the phase moveout and coherence. A masking filter is then designed and applied to create a multiple-free and denoised Ps-RF. We demonstrate, using synthetic experiment, that our implementation of the Radon transform using a sparsity-promoting regularization outperforms the conventional least-squares methods and can effectively isolate direct Ps conversions. We further apply the CRISP-RF workflow on real data, including single station data on cratons, common-conversion-point stack at continental margins and seismic data from ocean islands. The application of CRISP-RF to global data sets will advance our understanding of the enigmatic origins of the upper mantle discontinuities like the ubiquitous mid-lithospheric discontinuity and the elusive X-discontinuity. [ABSTRACT FROM AUTHOR]

Gravity surveys constitute an important method for investigating the Earth's interior based on density contrasts related to Earth material differentials. Because lithology depends on the environment and the period of formation, there are generally clear boundaries between rocks with different lithologies. Inversions with convex functions for approximating the L 0 norm are used to detect boundaries in reconstructed models. Optimizations can easily be found because of the convex transformations; however, the volume of the reconstructed model depends on the weighting parameter and the density constraint rather than the model sparsity. To determine and adapt the modelling size, a novel non-convex framework for gravity inversion is proposed. The proposed optimization aims to directly reduce the L 0 norm of the density matrix. An improved iterative hard thresholding algorithm is developed to linearly reduce the L 0 penalty during the inner iteration. Accordingly, it is possible to determine the modelling scale during the iteration and achieve an expected scale for the reconstructed model. Both simple and complex model experiments demonstrate that the proposed method efficiently reconstructs models. In addition, granites formed during the Yanshanian and Indosinian periods in the Nanling region, China, are reconstructed according to the modelling size evaluated in agreement with the magnetotelluric profile and density statistics of rock samples. The known ores occur at the contact zones between the sedimentary rocks and the reconstructed Yanshanian granites. The ore-forming bodies, periods, and processes are identified, providing guidance for further deep resource exploration in the study area. [ABSTRACT FROM AUTHOR]

We report on three optically bright, ∼16th mag, point sources within 10 arcsec of each other that vanished within 1 h, based on two consecutive exposures at Palomar Observatory on 1952 July 19 (POSS I Red and Blue). The three point sources have continued to be absent in telescope exposures during 71 yr with detection thresholds of ∼21st mag. We obtained two deep exposures with the 10.4-m Gran Telescopio Canarias on 2023 April 25 and 27 in r and g band, both reaching magnitude 25.5 (3σ). The three point sources are still absent, implying they have dimmed by more than 10 mag within an hour back in 1952. When bright in 1952, the most isolated transient source has a profile nearly the same as comparison stars, implying the sources are subarcsec in angular size and they exhibit no elongation due to movement. This triple transient has observed properties similar to other cases where groups of transients ('multiple transients') have appeared and vanished in a small region within a plate exposure. The explanation for these three transients and the previously reported cases remains unclear. Models involving background objects that are optically luminous for less than 1 h coupled with foreground gravitational lensing seem plausible. If so, a significant population of massive objects with structure serving as the lenses, to produce three images, are required to explain the subhour transients. [ABSTRACT FROM AUTHOR]

We studied the benefits of a nine-component (9C) seismic survey over a landslide-prone area in southwest of Sweden to retrieve ultrahigh-resolution shear wave reflection images of the subsurface as well as crucial information on physical properties of the sediments. A complete, 1 m shot and receiver spacing, multicomponent 2-D seismic profile was acquired using three-component microelectromechanical-system-based landstreamer receivers, and a 5-kg sledgehammer strike in vertical and horizontal orientations as three-component seismic source. Given the rich number of shear wave reflections observed on all the 9C data, the processing work focused on their retrievals. It revealed three distinct reflections, two of which associated with coarse-grained materials and one with an extremely undulating bedrock surface. Given the extremely slow shear wave velocities on the order of 60–100 m s−1, we obtained ultrahigh-resolution shear wave sections avoiding temporal and spatial aliasing. Imaging results suggest vertical-source and horizontal-radial receiver (V–HR), and horizontal-transverse source–receiver orientations (HT–HT) provided the most optimum images of the subsurface. A non-hyperbolic algorithm was applied to the normal-moveout corrections justified by the traveltime differences of the bedrock reflection in different shear wave sections. The improved images by incorporating the anisotropy term suggest that the data set reveals moderate shear wave anisotropy along some portions of the profile. The Vp / Vs ratio obtained by using bedrock reflection in P - and S -wave sections suggests values ranging 10–16, which implies high water content. Areas with lower Vp / Vs coincides with greater anisotropic parameters and this can indicate disturbed clays or presence of sensitive clays. [ABSTRACT FROM AUTHOR]

Seismic interferometry (SI) is a technique that allows one to estimate the wavefields accounting for the wave propagation between seismometers, any of which can act as a virtual source (VS). Interferometry, particularly noise interferometry, has been applied to several geophysical disciplines such as passive monitoring and distributed acoustic sensing. In practice, one requires long recordings of seismic noise for noise interferometry. Additionally, one can have missing seismic interferometric traces because some receivers in seismic arrays may be absent or inoperative due to issues of receiver installation and malfunction. Thus, filling the gap of seismic interferometric profile requires wavefield reconstruction and regularization techniques. Compressive sensing (CS) is one such method that can reconstruct seismic interferometric wavefields and help mitigate the limitations by exploiting the sparsity of seismic waves. In our work, we use CS to reconstruct missing seismic interferometric wavefields. One can interpolate interferometric wavefields using correlograms provided by one VS. We call this method of reconstructing an individual VS gather single-source wavefield reconstruction. We propose an alternative technique called multi-source wavefield reconstruction , which applies CS to reconstruct multiple interferometric wavefields using a volume of VS gathers provided from all available VSs. Using numerical examples, we show that one can apply CS to recover interferometric wavefields resulting from interferometry of a linear seismic array. To exploit the sparsity of interferometric wavefields, we apply the Fourier and Curvelet transforms to the two reconstruction schemes. Using the signal-to-noise ratio (SNR) to compare reconstruction of interferometric wavefields, the Fourier multi-source method improves the recovery of interferometric wavefields by approximately 50 dB compared to the Fourier and Curvelet single-source wavefield reconstructions. [ABSTRACT FROM AUTHOR]

We present a new method, called 'forced-spectrum fitting', for physically based spectral modelling of radio sources during deconvolution. This improves upon current common deconvolution fitting methods, which often produce inaccurate spectra. Our method uses any pre-existing spectral index map to assign spectral indices to each model component cleaned during the multifrequency deconvolution of wsclean , where the pre-determined spectrum is fitted. The component magnitude is evaluated by performing a modified weighted linear least-squares fit. We test this method on a simulated LOFAR high-band antenna (HBA) observation of the 3C 196 QSO and a real LOFAR HBA observation of the 4C+55.16 FRI galaxy. We compare the results from the forced-spectrum fitting with traditional joined-channel deconvolution using polynomial fitting. Because no prior spectral information was available for 4C+55.16, we demonstrate a method for extracting spectral indices in the observed frequency band using 'clustering'. The models generated by the forced-spectrum fitting are used to improve the calibration of the data sets. The final residuals are comparable to existing multifrequency deconvolution methods, but the output model agrees with the provided spectral index map, embedding correct spectral information. While forced-spectrum fitting does not solve the determination of the spectral information itself, it enables the construction of accurate multifrequency models that can be used for wide-band calibration and subtraction. [ABSTRACT FROM AUTHOR]

The treatment of recurrent ovarian cancer has been based on systemic therapy. The role of secondary cytoreductive surgery has been addressed recently in several trials. Imaging plays a key role in helping the surgical team to decide which patients will have resectable disease and benefit from surgery. The role of staging laparoscopy and several imaging and clinical scores has been extensively debated in the field. In other surgical fields there have been reports of using 3D imaging software and 3D printed models to help surgeons better plan the surgical approach. To the best of our knowledge, we report the first case of a patient with recurrent ovarian cancer undergoing 3D modeling before secondary cytoreductive surgery. The 3D modeling was of most value to evaluate the extension of the disease in our patient who underwent a successful secondary cytoreductive surgery and is currently free of the disease. [ABSTRACT FROM AUTHOR]

Digital rock physics (DRP) is important for characterizing the pore characteristics and petrophysical parameters of rocks from a microscopic perspective. Among the digital rock reconstruction methods, the most widely used is the computer tomography (CT) scan method. However, the pore structure of rocks has multiscale features, and CT scan images with a single resolution cannot completely describe the pore structure characteristics of rocks. In this paper, we analysed reconstructed multisource and multiscale 3-D digital rocks based on the cycle-consistent generative adversarial network (CycleGAN) method. This method fully integrates the advantages of the large field of view of low-resolution images and the high-precision features of high-resolution images. To investigate the performance of the method, two sequences of CT scan images of a sandstone (sample A) and a carbonate rock (sample B) collected from oilfields were studied. Moreover, due to the high resolution of scanning electron microscope (SEM) images, we also constructed 3-D digital rocks with different resolutions of the Berea sandstone based on the combination of CT scan images and SEM images. Finally, the statistical properties and absolute permeabilities were calculated to evaluate the accuracies of reconstructed multisource and multiscale 3-D digital rocks. The results show that the reconstructed multiscale digital rocks based on the CycleGAN method have good accuracy in terms of statistical properties and petrophysical properties. Considering the computational cost and computational accuracy, for high- and low-resolution CT scan images and CT scan images with SEM images as training image data sources, we suggest that the resolution of the reconstructed multiscale digital cores is 4–8 times and 4–16 times higher than that of the low-resolution CT scan images, respectively. The findings of our research will be helpful in gaining insight into the petrophysical properties of heterogeneous rocks. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) is the current standard method to determine final and detailed model parameters to be used in the seismic imaging process. However, FWI is an ill-posed problem that easily achieves a local minimum, leading the model solution in the wrong direction. Recently, some works proposed integrating FWI with Convolutional Neural Networks (CNN). In this case, the CNN weights are updated following the FWI gradient, defining the process as a Physics-Informed Neural Network (PINN). FWI integrated with CNN has an important advantage. The CNN stabilizes the inversion, acting like a regularizer, avoiding local minima-related problems and sparing an initial velocity model in some cases. However, such a process, especially when not requiring an initial model, is computationally expensive due to the high number of iterations required until the convergence. In this work, we propose an approach which relies on combining supervised learning and physics-informed by using a previously trained CNN to start the DL-FWI inversion. Loading the pre-trained weights configures transfer learning. The pre-trained CNN is obtained using a supervised approach based on training with a reduced and simple data set to capture the main velocity trend at the initial FWI iterations. The proposed training process is different from the initial works on the area which obtained the velocity model from the shots in supervised learning tasks and that required a large amount of labelled data to ensure reasonable model predictions. We investigated in our approach two CNN architectures, obtaining more robust results and a reduced number of parameters when using a modified U-Net. The method was probed over three benchmark models, showing consistently that the pre-training phase reduces the process's uncertainties and accelerates the model convergence using minimal prior information. Besides, the final scores of the iterative process are better than the examples without transfer learning. Thus, transfer learning solved one main limitation of the previous PINN approaches: the unfeasible number of iterations when not using an initial model. Moreover, we tested the method using data with low-frequency band limitations, since the lack of low frequencies is a common issue within real seismic data. The inversion converges to reasonable results probing the method's robustness with restricted frequency content. [ABSTRACT FROM AUTHOR]

Accurate near-surface velocity models are necessary for land seismic imaging. First-arrival traveltime tomography (FTT) routinely used for estimating near-surface velocity models may fail in geological complex areas. Supervised deep learning (SDL) is capable of building accurate velocity models, based on tens of thousands of velocity model-shot gathers training pairs. It takes lots of time and memory space, which may be unaffordable for practical applications. We propose integrating the FTT and SDL to build near-surface velocity models. During the neural network training, the FTT-inverted models rather than the original seismic data are used as the network inputs and corresponding true models are the outputs. The FTT-inverted and true models are the same physical quantities and with the same dimensions. Their relationship is less non-linear than that between shot gathers and true models. Thus, the neural network of the proposed method can be trained well using only a small number of training samples, dramatically reducing the time and memory costs. Numerical tests demonstrate the feasibility and effectiveness of the proposed method. We applied the proposed method to a land data set obtained in mountainous areas in the west of China and obtained satisfactory near-surface velocity models and stacking images. [ABSTRACT FROM AUTHOR]

Seismic velocity analysis is the basis for seismic imaging and understanding complex subsurface geological structures. Although the performance of automatic velocity analysis methods based on Common Middle Point (CMP) data or Velocity Spectra (VS) is encouraging, particularly deep learning methods. However, most methods ignore the complementarity between CMP data and VS data, and only one of them is selected for velocity modelling. We propose a multimodal neural network (MMN) that combines the advantages of CMP data details representation and simplification of VS. MMN includes multilayer convolution structures and auto-encoder structures, which are used to extract time–space amplitude information from CMP gathers and energy groups features from VS data, respectively. This paper compared MMN with the CMP single-modal network (CSN) and the velocity spectra single-modal network (VSSN). Based on synthetic data, we investigated their differences in terms of continuity, accuracy, noise resistance and generalization. The MMN prediction results makes a trade-off between the overall continuity and local details. Visualization analysis of the intermediate feature maps explains the MMN velocity prediction mechanism, that is, the multi-angle representation and complementary fusion of velocity information. Finally, the performance of the proposed method is demonstrated using the braided river deposited field data example. [ABSTRACT FROM AUTHOR]

PAUCam is an innovative optical narrow-band imager mounted at the William Herschel Telescope built for the Physics of the Accelerating Universe Survey (PAUS). Its set of 40 filters results in images that are complex to calibrate, with specific instrumental signatures that cannot be processed with traditional data reduction techniques. In this paper, we present two pipelines developed by the PAUS data management team with the objective of producing science-ready catalogues from the uncalibrated raw images. The Nightly pipeline takes care of entire image processing, with bespoke algorithms for photometric calibration and scatter-light correction. The Multi-Epoch and Multi-Band Analysis pipeline performs forced photometry over a reference catalogue to optimize the photometric redshift (photo-z) performance. We verify against spectroscopic observations that the current approach delivers an inter-band photometric calibration of 0.8 per cent across the 40 narrow-band set. The large volume of data produced every night and the rapid survey strategy feedback constraints require operating both pipelines in the Port d'Informació Cientifica data centre with intense parallelization. While alternative algorithms for further improvements in photo-z performance are under investigation, the image calibration and photometry presented in this work already enable state-of-the-art photo-z down to i AB = 23.0. [ABSTRACT FROM AUTHOR]

Motivation Cell membrane segmentation in electron microscopy (EM) images is a crucial step in EM image processing. However, while popular approaches have achieved performance comparable to that of humans on low-resolution EM datasets, they have shown limited success when applied to high-resolution EM datasets. The human visual system, on the other hand, displays consistently excellent performance on both low and high resolutions. To better understand this limitation, we conducted eye movement and perceptual consistency experiments. Our data showed that human observers are more sensitive to the structure of the membrane while tolerating misalignment, contrary to commonly used evaluation criteria. Additionally, our results indicated that the human visual system processes images in both global–local and coarse-to-fine manners. Results Based on these observations, we propose a computational framework for membrane segmentation that incorporates these characteristics of human perception. This framework includes a novel evaluation metric, the perceptual Hausdorff distance (PHD), and an end-to-end network called the PHD-guided segmentation network (PS-Net) that is trained using adaptively tuned PHD loss functions and a multiscale architecture. Our subjective experiments showed that the PHD metric is more consistent with human perception than other criteria, and our proposed PS-Net outperformed state-of-the-art methods on both low- and high-resolution EM image datasets as well as other natural image datasets. Availability and implementation The code and dataset can be found at https://github.com/EmmaSRH/PS-Net. [ABSTRACT FROM AUTHOR]

SUMMARY: With the increasing advances in the oil and gas industry, seismic imaging near or under salt structures has become an important point in deep-water exploration. Detailed velocity models of these areas are particularly interesting not only to characterize hydrocarbon reservoirs but also to identify potential sites for hydrogen and carbon dioxide storage in offshore salt caverns. Thus, we study the full-waveform inversion for the salt reconstruction in acoustic media with constant density considering the time-harmonic wave propagation in a finite element formulation using the topology optimization (TO) method. This problem is challenging due to the strong velocity contrast between salt bodies and the sedimentary background, in addition to the lack of low-frequency data and the inherent ill-posedness of the inverse problem. In this context, we incorporate techniques from the TO field, usually used in design applications, to overcome or reduce these known problems. We initially defined the squared slowness as a combination of two fields, one related to the salt shape and the other to the background. An interpolation rule based on the solid isotropic material with penalization method, combined with filtering and projection schemes, is used to find the shape of the salt bodies with increased sharpness interfaces. A Helmholtz-type filter is applied to modify the gradient aimed to regularize the problem and provide a more stable way for the salt shape to evolve during the inversion process. In particular, we demonstrate that the proposed approach may be relevant for reconstructing media with salt bodies when a suitable starting model is unavailable, and sharp interfaces are required. In addition, we present inversion results from synthetic data generated by a variable density model to demonstrate the approach capability when subjected to a reconstruction application. [ABSTRACT FROM AUTHOR]

SUMMARY: Large continental earthquakes are infrequent, but they are important for understanding active tectonics. Many pre-1990, magnitude greater than 7 earthquakes have not been well documented due to a lack of data. The declassification of KeyHole (KH) imagery provides opportunities for investigating those historical events. In this study, we present new geodetic observations of surface deformation from the 1976 November 24 Ms 7.3 Chaldiran earthquake in eastern Turkey. We use various historical KH imagery and modern Pleiades stereo imagery to determine the coseismic deformation, providing new constraints on the 1976 rupture. Based on the image measurements, we resolve the coseismic slip at depth. The inverted slip from a simple elastic dislocation model, ∼4.0 m at shallow depth of the Hidirmentes segment, is larger than the surface displacement, around 3 m from previous field surveys, suggesting that the coseismic deformation is a combination of slip on the primary fault with 30 ± 16 per cent off-fault deformation. We also measure cumulative offsets at two sites with well-preserved features, and find a constant ratio of horizontal to vertical offsets throughout the past few earthquake cycles. Both the observed constant slip ratio in this study and offset clusters from previous field measurements support that the Chaldiran fault exhibits a characteristic slip behaviour in the late Quaternary. By combining all the geodetic and geological observations in the broader region, we also present a brief analysis of the slip rate of the Chaldiran fault. [ABSTRACT FROM AUTHOR]

In 1982, a water-soluble aminoplastic resin embedding method for electron microscopy (EM) was developed to study lipids. It involves a silylation technique for processing cured resins by converting them from hydrophilic to hydrophobic status, facilitating ultra-thin sectioning. The technique offers better preservation of lipovitellin material in frog (Rana amurensis) yolk platelets, and it shows membrane ultrastructure better than traditional epoxy embedding methods. An early (1982) pseudo-colored TEM image processing technique applied to the micrographs obtained by EM of silylated aminoplastic thin sections revealed the lipovitellin molecules, in relation to the biosynthesis of new membranes in the first cleavage of the egg, with remarkable clarity and explains the lipovitellin molecules in relation to the biosynthesis of new membranes. Because the original article was written in Chinese and published in China before its open policy, many scientists may not be aware of the publication. Thus, in this short review, the essential components of the procedure are presented in English to help guide investigators who might be interested in the use of water-soluble embedding methods for lipid research. [ABSTRACT FROM AUTHOR]

Reflector-normal angles and reflector-curvature parameters are the principal geometric attributes used in seismic interpretation for characterizing the orientations and shapes, respectively, of geological reflecting surfaces. Commonly, the input data set for their computation consists of fine 3-D grids of scalar fields representing either the seismic-driven reflectivities (e.g. amplitudes of 3-D seismic migrated volumes) or model-driven reflectivities, computed, for example, from the derived elastic impedance parameters. Conventionally, the computation of curvature parameters at each gridpoint is based on analysing the local change in the inline/crossline dips, considering the potential existence of a local quadratic reflecting surface in the vicinity of that point. This assumption breaks down for subsurface points in the vicinity of either complex reflecting surfaces (e.g. brittle/rough/tilted synclines/anticlines, ridges/troughs and saddles) and/or sharp, discontinuous geological features (e.g. fault edges/tips, pinch-outs, fracture systems, channels and small geobodies), where the values of the computed curvature become extremely high. However, while these high values can indicate the existence of non-reflecting objects, they do not deliver their specific geometric characteristics. In this study, we present a novel method that better characterizes the shapes of these complex geological features by extending the assumption of local surfaces (2-D surfaces in 3-D space) into local hypersurfaces (3-D hypersurfaces in 4-D space), with their corresponding (three rather than two) principal (and effective) curvature parameters. We demonstrate the advantages of our method by comparing the conventional dip-based surface curvature parameters with the hypersurface curvature parameters, using a synthetic model/image with different types and shapes of geological features and a seismic image of real data containing a complex fault and hidden buried channels. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has achieved remarkable progress in geophysics. The most commonly used supervised learning (SL) framework requires massive labelled representative data to train artificial neural networks (ANNs) for good generalization. However, the labels are limited or unavailable for field seismic data applications. In addition, SL generally cannot take advantage of well-known physical laws and thus fails to generate physically consistent results. The weaknesses of standard SL are non-negligible. Therefore, we provide an open-source package for geophysics-steered self-supervised learning (SSL; taking application to seismic deconvolution as an example). With the wavelet given, we incorporate the convolution model into the loss function to measure the error between the synthetic trace generated by the ANN deconvolution result and the observed data, steering the ANN's learning process toward yielding accurate and physically consistent results. We utilize an enhanced U-Net as the ANN. We determine a hard threshold operator to impose a sparse constraint on the ANN deconvolution result, which is challenging for current DL platforms because no layer is available. 2-D/3-D ANNs can naturally introduce spatial regularization to the ANN deconvolution results. Tests on synthetic data and 3-D field data with available well logs verify the effectiveness of the proposed approach. The approach outperforms the traditional trace-by-trace method in terms of accuracy and spatial continuity. Experiments on synthetic data validate that sparsity promotion matters for sparse recovery problems. Field data results of the proposed approach precisely identify the layer interfaces and mostly match well with the log. All codes and data are publicly available at https://doi.org/10.5281/zenodo.7233751 (Xintao Chai). [ABSTRACT FROM AUTHOR]

We present an application of auto-encoders to the problem of noise reduction in single-shot astronomical images and explore its suitability for upcoming large-scale surveys. Auto-encoders are a machine learning model that summarizes an input to identify its key features, and then from this knowledge predicts a representation of a different input. The broad aim of our auto-encoder model is to retain morphological information (e.g. non-parametric morphological information) from the survey data while simultaneously reducing the noise contained in the image. We implement an auto-encoder with convolutional and max pooling layers. We test our implementation on images from the Panoramic Survey Telescope and Rapid Response System that contain varying levels of noise and report how successful our auto-encoder is by considering mean squared error, structural similarity index, the second-order moment of the brightest 20 per cent of the galaxy's flux M 20, and the Gini coefficient, while noting how the results vary between original images, stacked images, and noise-reduced images. We show that we are able to reduce noise, over many different targets of observations, while retaining the galaxy's morphology, with metric evaluation on a target-by-target analysis. We establish that this process manages to achieve a positive result in a matter of minutes, and by only using one single-shot image compared to multiple survey images found in other noise reduction techniques. [ABSTRACT FROM AUTHOR]

Because the magnetotelluric (MT) method uses natural sources, the electric and magnetic fields recorded in the field acquisition are not directly used but usually converted into other MT response functions for interpretation such as inversion. Considering that inversion results are dependent on types of input data, it can be helpful to analyse different characteristics of MT response functions for inversion. In this study, we examine sensitivity patterns of MT response functions used commonly in MT inversion, which are the impedance tensor, apparent resistivity, phase, tipper, effective impedance and phase tensor; and investigate how their sensitivity patterns affect inversion results. We first describe overall tendencies of 3-D sensitivity patterns of the MT response functions, and then classify the MT response functions into six groups based on 2-D sensitivity patterns computed at the surface, which are briefly called 'surface-sensitivity patterns' in this study. The 'diagonal components of the impedance' and 'off-diagonal components of the phase tensor', which have four petals-shaped surface-sensitivity patterns along the diagonal directions, belong to Group 1, and contribute to imaging 3-D subsurface structures from receivers installed evenly at the surface. Group 2 contains the ' xy -components of the impedance, apparent resistivity and phase' and ' yy -component of the phase tensor' whose surface-sensitivity patterns are linear in the y -axis. The ' yx -components of the impedance, apparent resistivity and phase' and ' xx -component of the phase tensor' that have strong linear surface-sensitivity patterns along the x -axis are classified into Group 3. The MT response functions of Groups 2 and 3 are useful for inversion of structures close to 2-D, whose strike extends along the y - and x -axes, respectively. Groups 4 and 5 include the ' x - and y -components of tipper' that possess linearly aligned two petals-shaped surface-sensitivity patterns in the x - and y -axes, respectively. The tipper can be helpful in imaging both 2-D and 3-D structures. The 'effective impedance' belongs to Group 6, whose surface-sensitivity patterns appear as a small circle. The surface-sensitivity patterns allow the effective impedance to have an advantage in interpretation of 1-D structures. By using several MT response functions for specific cases of 1-D, 2-D and 3-D interpretation of MT data, we investigate whether characteristics of the sensitivity patterns are reflected in modelling (simulating field data) and inversion results, and then suggest optimal MT response functions for those cases. In doing so, we show how to utilize the characteristics of the sensitivity patterns in inversion, and recommend the input MT response functions for inversion according to MT exploration situations. Our study provides basic information on similarities and differences of major MT response functions for inversion and insights on which MT response functions are suitable to increase the feasibility of MT inversion for different field situations based on the sensitivity patterns. [ABSTRACT FROM AUTHOR]

We present the first release of the Calar Alto CAFOS direct imaging data, a project led by the Spanish Virtual Observatory with the goal of enhancing the use of the Calar Alto archive by the astrophysics community. Data Release 1 contains 23 903 reduced and astrometrically calibrated images taken from 2008 March to 2019 July with a median of the mean uncertainties in the astrometric calibration of 0.04 arcsec. The catalogue associated to 6132 images in the Sloan griz filters provides accurate astrometry and PSF calibrated photometry for 139 337 point-like detections corresponding to 21 985 different sources extracted from a selection of 2338 good-quality images. The mean internal astrometric and photometric accuracies are 0.05 arcsec and 0.04 mag, respectively. In this work we describe the approach followed to process and calibrate the images, and the construction of the associated catalogue, together with the validation quality tests carried out. Finally, we present three cases to prove the science capabilities of the catalogue: discovery and identification of asteroids, identification of potential transients, and identification of cool and ultracool dwarfs. [ABSTRACT FROM AUTHOR]

In a companion paper, a faceted wideband imaging technique for radio interferometry, dubbed Faceted HyperSARA, has been introduced and validated on synthetic data. Building on the recent HyperSARA approach, Faceted HyperSARA leverages the splitting functionality inherent to the underlying primal-dual forward-backward algorithm to decompose the image reconstruction over multiple spatio-spectral facets. The approach allows complex regularization to be injected into the imaging process while providing additional parallelization flexibility compared to HyperSARA. This paper introduces new algorithm functionalities to address real data sets, implemented as part of a fully fledged matlab imaging library made available on GitHub. A large-scale proof of concept is proposed to validate Faceted HyperSARA in a new data and parameter scale regime, compared to the state of the art. The reconstruction of a 15 GB wideband image of Cyg A from 7.4 GB of Very Large Array data is considered, utilizing 1440 CPU cores on a high-performance computing system for about 9 h. The conducted experiments illustrate the reconstruction performance of the proposed approach on real data, exploiting new functionalities to leverage known direction-dependent effects, for an accurate model of the measurement operator, and an effective noise level accounting for imperfect calibration. They also demonstrate that, when combined with a further dimensionality reduction functionality, Faceted HyperSARA enables the recovery of a 3.6 GB image of Cyg A from the same data using only 91 CPU cores for 39 h. In this setting, the proposed approach is shown to provide a superior reconstruction quality compared to the state-of-the-art wideband clean -based algorithm of the wsclean software. [ABSTRACT FROM AUTHOR]

On 2013 September 24, an earthquake of magnitude M w 7.7 occurred on the Hoshab Fault in southern Pakistan, south of the 650-km-long Chaman Fault, within the eastern Makran accretionary wedge. This earthquake was caused predominantly by strike-slip faulting. To quantify the post-seismic deformation following this large earthquake, we used ascending and descending Sentinel-1 data over the period 2014 November–2017 December. The deformation time-series over these 3 years shows that the post-seismic deformation was not linear over the time. To determine the mechanisms that may have driven post-seismic deformation, such as after-slip or a combination of after-slip and viscous relaxation, we explored some models, including after-slip only, and a combination of after-slip and viscous relaxation, to identify the best fit between the observed and simulated deformation time-series. Our results indicate that after-slip was the main mechanism controlling the post-seismic deformation. The introduction of a basal flat fault in the modelling improves the fit between the model results and the time-series obtained from the satellite images This basal fault is located at a depth of 18 km, has a northward dip of 7° and corresponds to the basal décollement level of the Makran accretionary prism. [ABSTRACT FROM AUTHOR]

Subsurface reflectivity imaging is one of the most important geophysical characterization methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity imaging is challenging because of, for example, random or coherent noise in the data and sparse source–receiver observation geometry. We develop a deep-learning-guided iterative imaging method to improve subsurface structure imaging. Specifically, we train a supervised neural network to infer a noise-free, high-resolution image from a noisy, low-resolution image and use this estimated image as guidance to regularize least-squares imaging. We develop a systematic method to generate high-quality synthetic training data (data-label pairs) to train the guidance neural network. The trained neural network can provide high-fidelity predictions even for field-data images that are not in the training data. We validate our new imaging method using one synthetic and two field ground-penetrating radar data examples, and find that our method can produce clean, high-resolution subsurface reflectivity images where existing single-pass and least-squares imaging methods fail due to noise and insufficient data coverage. [ABSTRACT FROM AUTHOR]

The performance of supervised deep learning-based denoising methods relies on massive amounts of high-quality training data set with labels. However, data labelling is a time-consuming and tedious process, and the lack of labelled data set has become a major bottleneck affecting the development of supervised deep learning-based denoising methods. In recent years, denoising methods that only use unlabelled noisy data set for training have received more and more attention. Although these methods get rid of the dependence on labels, they usually have some specific requirements on the training data set. For example, the paired training data are required to be multiple noisy observations for each scene or obey a specific noise distribution, etc., which are often very challenging to meet in practical applications. In this study, we propose an improved self-supervised denoising framework based on Noise2Noise that only uses noisy seismic data set for training, and we name it sample2sample. The proposed denoising framework does not require multiple repeated acquisitions of seismic data to obtain multiple independent noisy observations for each scene used for training, and has no specific requirement for the noise distribution prior. Specifically, we introduce a random sampler to generate paired subsamples from some individual noisy seismic data for training. The corresponding elements in the two paired subsamples are adjacent in the original seismic data and approximately meet the training premise of Noise2Noise, that is the paired training data have the same signal. In addition, considering that there are some subtle differences in the signals of the paired subsamples generated by sampling, we also introduce a regularization loss to compensate for this. We conducted a qualitative and quantitative analysis of the denoising performance of the proposed method through further experiments, including synthetic data experiments and field data experiments. [ABSTRACT FROM AUTHOR]

The accurate and fast estimation of velocity models is crucial in seismic imaging. Conventional methods, such as tomography, stereotomography, migration velocity analysis and full-waveform inversion (FWI), obtain appropriate velocity models; however, they require intense and specialized human supervision and consume much time and computational resources. In recent years, some works investigated deep learning (DL) algorithms to obtain the velocity model directly from shots or migrated angle panels, obtaining encouraging predictions of synthetic models. This paper proposes a new flow to recover structurally complex velocity models with DL. Inspired by the conventional geophysical velocity model building methods, instead of predicting the entire model in one step, we predict the velocity model iteratively. We implement the iterative nature of the process when, at each iteration, we train the DL algorithm to determine the velocity model with a certain level of precision/resolution for the next iteration; we name this process as 'Deep-Tomography'. Starting from an initial model, that is an ultrasmooth version of the true model, Deep-Tomography is able to predict an appropriate final model, even in complete unseen during the training data, like the Marmousi model. When used as the initial model for FWI, the models estimated by Deep-Tomography can also improve substantially the final results obtained with FWI. [ABSTRACT FROM AUTHOR]