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Traditional survey methods for finding surface resistivity are time-consuming and labor intensive. Very few studies have focused on finding the resistivity/conductivity using remote sensing data and deep learning techniques. In this line of work, we assessed the correlation between surface resistivity and Synthetic Aperture Radar (SAR) by applying various deep learning methods and tested our hypothesis in the Coso Geothermal Area, USA. For detecting the resistivity, L-band full polarimetric SAR data acquired by UAVSAR were used, and MT (Magnetotellurics) inverted resistivity data of the area were used as the ground truth. We conducted experiments to compare various deep learning architectures and suggest the use of Dual Input UNet (DI-UNet) architecture. DI-UNet uses a deep learning architecture to predict the resistivity using full polarimetric SAR data by promising a quick survey addition to the traditional method. Our proposed approach accomplished improved outcomes for the mapping of MT resistivity from SAR data.

Due to the tremendous cost of seismic data acquisition, methods have been developed to reduce the amount of data acquired by designing optimal missing trace reconstruction algorithms. These technologies are designed to record as little data as possible in the field, while providing accurate wavefield reconstruction in the areas of the survey that are not recorded. This is achieved by designing randomized subsampling masks that allow for accurate wavefield reconstruction via matrix completion methods. Motivated by these recent results, we propose a simulation-free seismic survey design that aims at improving the quality of a given randomized subsampling using a simulated annealing algorithm that iteratively increases the spectral gap of the subsampling mask, a property recently linked to the quality of the reconstruction. We demonstrate that our proposed method improves the data reconstruction quality for a fixed subsampling rate on a realistic synthetic dataset.

Seismic imaging in challenging sedimentary basins and reservoirs requires acquiring, processing, and imaging very large volumes of data (tens of terabytes). To reduce the cost of acquisition and the time from acquiring the data to producing a subsurface image, novel acquisition systems based on compressive sensing, low-rank matrix recovery, and randomized sampling have been developed and implemented. These approaches allow practitioners to achieve dense wavefield reconstruction from a substantially reduced number of field samples. However, designing acquisition surveys suited for this new sampling paradigm remains a critical and challenging role in oil, gas, and geothermal exploration. Typical random designs studied in the low-rank matrix recovery and compressive sensing literature are difficult to achieve by standard industry hardware. For practical purposes, a compromise between stochastic and realizable samples is needed. In this paper, we propose a deterministic and computationally cheap tool to alleviate randomized acquisition design, prior to survey deployment and large-scale optimization. We consider universal and deterministic matrix completion results in the context of seismology, where a bipartite graph representation of the source-receiver layout allows for the respective spectral gap to act as a quality metric for wavefield reconstruction. We provide realistic scenarios to demonstrate the utility of the spectral gap as a flexible tool that can be incorporated into existing survey design workflows for successful seismic data acquisition via low-rank and sparse signal recovery.

Deep learning has led to many recent advances in object detection and instance segmentation, among other computer vision tasks. These advancements have led to wide application of deep learning based methods and related methodologies in object detection tasks for satellite imagery. In this paper, we introduce MIS Check-Dam, a new dataset of check-dams from satellite imagery for building an automated system for the detection and mapping of check-dams, focusing on the importance of irrigation structures used for agriculture. We review some of the most recent object detection and instance segmentation methods and assess their performance on our new dataset. We evaluate several single stage, two-stage and attention based methods under various network configurations and backbone architectures. The dataset and the pre-trained models are available at https://www.cse.iitb.ac.in/gramdrishti/.

In the previous numerical study, we find the blob formation and ejection in the presence of magnetic reconnection in the environment of the hot flow of the accretion disk. Based on those encouraging results, in the present work, we calculate the energy and the spectrum of the emission in the different bands around Sgr A*. We assume the electrons in the magnetic reconnection region are non-thermal and emits synchrotron radiation. The electrons in the other region are thermal, which follows the thermal distribution, and the thermal electron emission mechanism is thermal synchrotron radiation. During the whole process of the magnetic evolution and reconnection, we find two peaks in the temporal light curve in the recently observed Radio frequencies (230 GHz and 43 GHz) and NIR wavelengths. Although, the light curve of the NIR band is most prominent in a single peak. The first peak appears because of the blob in the plasma flow, which is formed due to the magnetic reconnection. The second peak appears due to the production of the non-thermal electrons with the evolution of the magnetic flux. Both peaks reach luminosity more than 1026 erg/s for a single plasmoid/blob. For the NIR band, the highest luminosity can reach more than 1028 erg/s. These luminosities can be high for the large simulation area and the stronger magnetic field with the multiple blobs. We infer that the observed flares are a group of magnetic reconnection phenomena, not a single one.

We study the regularity of small symbolic powers and integral closure of small powers of edge ideals. We also prove that the regularity of integral closure of powers of edge ideals of graphs with at most two odd cycles is the same as the regularity of their powers., Comment: 16 pages; Title has been changed, and results on the comparison of the regularity of small powers have been added. Comments and suggestions are welcome

It is generally believed that high energy radiation (power-law components) can be mostly produced by a hot corona gas in the accreting black holes. There is a very popular hybrid disk radial coupling model that the inner part of cool Keplerian disk (or Shakura-Sunyaev disk) can produce advection-dominated accretion flow or corona-like structure, which can also generate outflows/jets. Here we argue that this simple coupling model cannot explain the whole hardness-intensity diagram of the spectral states and their transitions, and associated jets of a $X-$ray binary. Based on recent theoretical works on advective disk structures around a black hole, as well as many observational behaviors of a source, we conclude that there should be a third component of hot accretion flow with the radial coupling model, which can successfully explain all the spectral states and transitions. Interestingly, this model also provides a new scenario for the jet generation, launching, and evolution during the states with help of created barrier of the inner flow. We have also find out the jet kinetic power expression with our new jet generation scenario., Comment: 8 pages, 3 figures, submitted to ApJ, comments are welcome

Unsupervised image-to-image translation is used to transform images from a source domain to generate images in a target domain without using source-target image pairs. Promising results have been obtained for this problem in an adversarial setting using two independent GANs and attention mechanisms. We propose a new method that uses a single shared discriminator between the two GANs, which improves the overall efficacy. We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain. Our results indicate that even without adding attention mechanisms, our method performs at par with attention-based methods and generates images of comparable quality.

We studied the general advective accretion solutions around the Kerr black hole (BH) with investigating two types of inflow gases at the outer accretion boundary (AB). We classified these two types of gases as a \cm and a \hm inflow gas at the outer AB on the basis of their temperatures and solutions. We found that the \hm gas is more efficient for angular momentum transportation around the outer AB than the \cm gas. The \hm gas can give global multiple \cite[popular as shock solution][]{c89} or single sonic point solutions and the \cm can give smooth global solution \cite[popular as ADAF][]{ny94} or two sonic point solutions. These solutions also represented on a plane of energy and angular momentum ($\be-L_0$) parameter space. Theoretically for the first time, we explored the relation between the nature of accretion solutions with the nature of initial accreting gas at the AB with detail computational and possible physical analysis. We also found that the surface density of the flow is highly affected with changing of the temperature at the AB, which can alter the radiative emissivities of the flow. The flow variables of various advective solutions also compared. On the basis of those results, we plotted some inner disk structures around the BHs. Doing so, we conjectured about the persistent/transient nature of spectral states, soft-excess and time scales of variabilities around the black hole $X-$ ray binaries (BXBs) and active galactic nuclei (AGNs)., Comment: 26 pages, 10 figures, accepted in ApJ