Deep Learning Vertical Resolution Enhancement Considering Features of Seismic Data

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation, environment and etc. applications.

Attenuation in the shallow weathering zone is relatively strong, causing severe energy loss during wave propagation. It is difficult to estimate accurate [Formula: see text] values in the shallow weathering zone, and the influence of shallow weathering zone is seldom considered into attenuation estimation and compensation in the deep part. We achieved [Formula: see text] value estimation where there exist microlog data in the shallow weathering zone using the generalized S transform (GST); then, we establish an empirical formula using the velocity and [Formula: see text] value estimated with microlog data; finally, the [Formula: see text] value in the 3D shallow weathering zone can be obtained using the established formula and the velocity information. During the first procedure, the GST is used to provide reasonable time-frequency resolution, and linear regression is used in the obtained logarithmic spectral ratio to get the estimated [Formula: see text] value. An empirical formula is established using the estimated [Formula: see text] value and the velocity where there exists microlog data in the second procedure. In the third step, [Formula: see text] estimation in the whole shallow weathering zone can be obtained using the established formula and the velocity information, which can overcome the inaccuracy of spatial interpolation with the estimated [Formula: see text] factors where there exist different twin-well microlog data. Attenuation compensation to seismic data obtained from the deep part is carried out to prove the effectiveness of the estimated [Formula: see text] in the shallow weathering zone. After compensation, the resolution of seismic data is effectively increased, which demonstrates the validity of the estimated [Formula: see text] values in the shallow weathering zone. Synthetic data and field data examples demonstrate the validity of our method.

Seismic imaging is a significant technology to provide the image of the subsurface in several fields such as hydrocarbon exploration/production and civil engineering. A fundamental problem in seismic imaging is that both the depth reflectivity and velocity distribution of the subsurface have to be predicted by only seismic events observed at the surface, and it still remains a challenging research topic. Joint migration inversion (JMI) is one of the seismic waveform imaging algorithms that were recently proposed. JMI is capable of simultaneously estimating velocity and reflectivity models of the subsurface by exploiting reflected waves including internal multiples. The seismic modeling algorithm in the JMI process is a method termed full wavefield modeling (FWMod), which is a one-way propagator-based reflection modeling algorithm, including higher-order scattering and transmission effects. In this thesis, two directions to improve the accuracy of seismic imaging based on JMI are discussed. On one hand, an extension of FWMod is proposed to correctly deal with not only reflected waves but refracted/diving waves via one-way propagators in the horizontal direction, and this method is extended to a new JMI algorithm. On the other hand, we assume that only reflected waves including internal multiples are utilized in the imaging based on JMI and present two novel methods for the inversion and pre-processing: 1) iterative reflectivity-constrained velocity estimation, 2) surface amplitude correction via learning from synthetic models for land seismic data. The reflectivity-constrained velocity estimation is employed to improve the accuracy of the estimated velocity by exploiting the estimated reflectivity in the JMI process. The surface amplitude correction process is introduced to mitigate the influence of the amplitude variations caused by source/receiver response sensitivities and the difference of the features between observed land seismic data and the simulated data by the used imaging scheme. The numerical and field data examples for both land and marine cases demonstrate that the proposed approach is capable of effectively estimating reflectivity and velocity model, even though the low frequency components of the observed data are absent.

Absorption in subsurface media severely degrades seismic data quality. Seismic attenuation compensation as an important processing method can effectively improve the resolution and fidelity of seismic data. Based on sparse reflectivity model and attenuated convolution function, inversion-based compensation approaches show better stability and accuracy over traditional direct compensation schemes. However, conventional inversion-based compensation methods are conducted on single trace, which ignore the subsurface spatial continuity and make the compensated result contaminated with high-frequency noise. In this paper, we develop a structurally constrained multichannel L1-2 minimization for seismic attenuation compensation. We first estimate structure tensors from migrated seismic images. The structure tensors can be decomposed by eigenvalues and eigenvectors, which can reflect the structural orientations. Then, we introduce the estimated orientations as a regularization term to the L1-2 inversion-based compensation objective function. In this way, we can improve the stability of the compensation result and enhance the spatial continuity of the compensated seismic reflectors. The structure-guided L1-2 regularized compensation objective function can be efficiently solved via difference of convex algorithm and alternating direction method of multipliers. Synthetic and field data examples demonstrate that the proposed method possesses superior performance over conventional L1-2 regularized inversion-based compensation.

The high resolution of multidimensional space-time measurements and enormity of data readout counts in applications such as particle tracking in high-energy physics (HEP) is becoming nowadays a major challenge. In this work, we propose combining dimension reduction techniques with quantum information processing for application in domains that generate large volumes of data such as HEP. More specifically, we propose using quantum wavelet transform (QWT) to reduce the dimensionality of high spatial resolution data. The quantum wavelet transform takes advantage of the principles of quantum mechanics to achieve reductions in computation time while processing exponentially larger amount of information. We develop simpler and optimized emulation architectures than what has been previously reported, to perform quantum wavelet transform on high-resolution data. We also implement the inverse quantum wavelet transform (IQWT) to accurately reconstruct the data without any losses. The algorithms are prototyped on an FPGA-based quantum emulator that supports double-precision floating-point computations. Experimental work has been performed using high-resolution image data on a state-of-the-art multinode high-performance reconfigurable computer. The experimental results show that the proposed concepts represent a feasible approach to reducing dimensionality of high spatial resolution data generated by applications such as particle tracking in high-energy physics.

This study develops a nonstationary blind deconvolution using spectral constraints (NBDSC), a novel method to address the ill-posed nature of wavelet inversion in nonstationary seismic data. NBDSC does not require prior knowledge about the [Formula: see text]-factor of the medium and source wavelet, which are typically unknown. Our power spectrum-guided waveform inversion method corrects a wavelet to match any desired spectral shape. This method can be introduced as a wavelet-smoothing constraint, yielding blind deconvolution with spectral constraints (BDSC). First, based on a segmented stationary convolution model (SSCM), the original nonstationary convolution problem is decomposed into multiple stationary subproblems. Next, an absorption-constrained wavelet power spectrum inversion method is used on the SSCM decomposition results to extract the time-varying wavelet power spectrum. Finally, the estimated time-varying wavelet power spectrum is used as a spectral constraint to perform BDSC on each subseismic trace. We use synthetic and field data examples to demonstrate how the NBDSC enhances the resolution of nonstationary seismic data. Compared with conventional blind deconvolution methods, NBDSC incorporates prior knowledge of the wavelet power spectrum, thereby reducing inversion ambiguity and improving noise robustness.

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño–Southern Oscillation (ENSO) is considered a significant signal of the global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed-resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, the influence of the month from which the low-resolution sea surface temperature data are obtained. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

Initialisation using high spatial, temporal and spectral resolution satellite observations

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. The results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

It is widely recognized that the first arrival seismic tomography is one of the robust ways to estimate the near surface velocity model. However, both the first arrival tomography itself and its application to static corrections are still less perfect. With regard to the first arrival seismic tomography, one of the challenges is the sparseness of the tomography matrix. This sparseness will cause the tomography matrix poorly conditioned. Although the model regularization can produce a well‐posed system for an ill‐posed system from the mathematic point of view, in principle, we had better to provide more information or reasonable physical constraints to the first arrival seismic tomography, if we want to get a more reliable result. In this paper, we introduce the fat‐ray into the first arrival seismic tomography in order to reduce the sparseness or the dimension of the null space of the tomography matrix. Thus, although the seismic tomography may be still partly underdetermined and partly over‐determined, it is possible to use the sparsely acquired seismic data to get reasonable near surface velocity model, or use part of the normally acquired seismic data or big cell size to perform seismic tomography to lower the computational cost. Synthetic data and field data examples shows that this method is feasible.

Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings

Various instruments are used to create images of the earth and other objects the universe a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built a phased approach, with additional measurement capabilities added later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned the original design of the instrument. In still other cases, high-resolution spectral measurements may be too costly to perform on a large sample, and therefore, lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. This work addresses this problem using virtual sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to fill in unmeasured spectral channels spectrally poor (low spectral resolution) data. The models we use Are multilayer perceptrons, support vector machines (SVMs) with radial basis function kernels, and SVMs with mixture density Mercer kernels. We demonstrate this method by using models trained on the high spectral resolution Terra Moderate Resolution Imaging Spectrometer (MODIS) instrument to estimate what the equivalent of the MODIS 1.6-/spl mu/m channel would be for the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR/2) instrument. The scientific motivation for the simulation of the 1.6-/spl mu/m channel is to improve the ability of the AVHRR/2 sensor to detect clouds over snow and ice.

Event detection is an essential component of microseismic data analysis. This process is typically carried out using a short- and long-term-average-ratio (STA/LTA) method, which is simple and computationally efficient but often yields inconsistent results for noisy data sets. We have aimed to optimize the performance of the STA/LTA method by testing different input forms of 3C waveform data and different characteristic functions (CFs), including a proposed [Formula: see text]-mean CF. These tests are evaluated using receiver operating characteristic (ROC) analysis and are compared based on synthetic and field data examples. Our analysis indicates that the STA/LTA method using a [Formula: see text]-mean CF improves the detection sensitivity and yields more robust event detection on noisy data sets than some previous approaches. In addition, microseismic events are detected efficiently on field data examples using the same detection threshold obtained from the ROC analysis on synthetic data examples. We recommend the use of the Youden index based on ROC analysis using a training subset, extracted from the continuous data, to further improve the detection threshold for field microseismic data.

Abstract. Nowadays, Terrestrial Laser Scanning (TLS) technology is gaining popularity in monitoring and predicting the movement of landslide due to the capability of high-speed data capture without requiring a direct contact with the monitored surface. It offers very high density of point cloud data in high resolution and also can be an effective tool in detecting the surface movement of the landslide area. The aim of this research is to determine the optimal level of scanning resolution for landslide monitoring using TLS. The Topcon Geodetic Laser Scanner (GLS) 2000 was used in this research to obtain the three dimensional (3D) point cloud data of the landslide area. Four types of resolution were used during scanning operation which were consist of very high, high, medium and low resolutions. After done with the data collection, the point clouds datasets were undergone the process of registration and filtering using ScanMaster software. After that, the registered point clouds datasets were analyzed using CloudCompare software. Based on the results obtained, the accuracy of TLS point cloud data between picking point manually and computed automatically by ScanMaster software shows the maximum Root Mean Square (RMS) value of coordinate differences were 0.013 m in very high resolution, 0.017 m in high resolution, 0.031 m in medium resolution and 0.052 m in low resolution respectively. Meanwhile, the accuracy of TLS point cloud data between picking point manually and total station data using intersection method shows the maximum RMS values of coordinate differences were 0.013 m in very high resolution, 0.018 m in high resolution, 0.033 m in medium resolution and 0.054 m in low resolution respectively. Hence, it can be concluded that the high or very high resolution is needed for landslide monitoring using Topcon GLS-2000 which can provide more accurate data in slope result, while the low and medium resolutions is not suitable for landslide monitoring due to the accuracy of TLS point cloud data that will decreased when the resolution value is increased.

Production through wellbores in petroleum industry involves multi-phase flow of oil, gas and water. It is necessary to capture the effects of varying composition of phases to represent wellbore hydraulics accurately. However, a majority of current wellbore simulators prefer a ‘black-oil’ formulation due to its higher speed as compared to a compositional approach. In this work, a neuro-simulation protocol is developed for fast and accurate simulation of wellbore hydraulics in vertical non-isothermal systems. The proposed toolbox is capable of predicting the pressure profile along a production tubing containing flow of pure hydrocarbons (C1 through C45) and water. The tool can operate within a range of flow rates, well depths, pipe diameters, inlet fluid compositions, wellhead pressures and geothermal gradients. The data for training the expert system is generated from a numerical simulator that computes wellbore hydraulics. The numerical simulator is a compositional model, which uses a two-phase equilibrium flash calculation for determining the distribution of hydrocarbon components in oil and gas phases. A two-phase drift flux model accounts for the variation in gas and liquid velocities under various flow regimes. The compositional mass balance and momentum equations are simultaneously solved using a fully implicit scheme, while the temperature profile of the well is taken into consideration in computations. The results provided by both the numerical simulator and the tool described in this paper are compared against field data as well as numerical simulation results reported in the literature. It is observed that the average relative deviation of the results generated in this study from the field data example is 2.2 %. The average relative deviation between the numerical simulator developed and field data example is 2.4 %, showing that the expert system is capable of capturing the related physics and thermodynamics of the wellbore hydraulics phenomena. The average relative deviation between the results generated by the proposed tool and numerical simulation examples from literature data is observed to be between 3.3 and 7.1 %. The average relative deviations between results from the numerical simulator developed in the current work and simulation results reported in literature are found to be between 2.3 and 8.1 %. The variation between predictions provided by various models for wellbore hydraulics in the literature lie within the ranges mentioned above. It is observed that the tool discussed in this paper is a fast, accurate and robust model for predicting wellbore hydraulics for compositional systems. The proposed tool also overcomes the stability issues associated with compositional numerical wellbore simulation models. Such a versatile tool can also be used for generating tubing intake curves for an unrestricted set of scenarios and for quick selection of optimal gas lift design parameters.