A systematic, science-driven approach for predicting subsurface properties

2020 ◽  
Vol 8 (1) ◽  
pp. T167-T181
Author(s):  
Kelly Kathleen Rose ◽  
Jennifer R. Bauer ◽  
MacKenzie Mark-Moser
Keyword(s):  
Language Processing ◽  
A Priori ◽  
Basin Scale ◽  
Porosity And Permeability ◽  
Priori Information ◽  
Efficient Exploration ◽  
Well Data ◽  
Ideal Framework ◽  
Discontinuous Data

As human exploration of the subsurface increases, there is a need for better data- and knowledge-driven methods to improve prediction of subsurface properties. Present subsurface predictions often rely upon disparate and limited a priori information. Even regions with concentrated subsurface exploration still face uncertainties that can obstruct safe and efficient exploration of the subsurface. Uncertainty may be reduced, even for areas with little or no subsurface measurements, using methodical, science-driven geologic knowledge and data. We have developed a hybrid spatiotemporal statistical-geologic approach, subsurface trend analysis (STA), that provides improved understanding of subsurface systems. The STA method assumes that the present-day subsurface is not random, but is a product of its history, which is a sum of its systematic processes. With even limited data and geologic knowledge, the STA method can be used to methodically improve prediction of subsurface properties. To demonstrate and validate the improved prediction potential of the STA method, it was applied in an analysis of the northern Gulf of Mexico. This evaluation was prepared using only existing, publicly available well data and geologic literature. Using the STA method, this information was used to predict subsurface trends for in situ pressure, in situ temperature, porosity, and permeability. The results of this STA-based analysis were validated against new reservoir data. STA-driven results were also contrasted with previous studies. Both indicated that STA predictions were an improvement over other methods. Overall, STA results can provide critical information to evaluate and reduce risks, identify and improve areas of scarce or discontinuous data, and provide inputs for multiscale modeling efforts, from reservoir scale to basin scale. Thereby, the STA method offers an ideal framework for guiding future science-based machine learning and natural language processing to optimize subsurface analyses and predictions.

scholarly journals The bias in GRACE estimates of continental water storage variations

2006 ◽  
Vol 3 (6) ◽  
pp. 3557-3594 ◽  
Author(s):  
R. Klees ◽  
E. A. Zapreeva ◽  
H. C. Winsemius ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Southern Africa ◽  
A Priori ◽  
Water Storage ◽  
Target Area ◽  
Hydrological Models ◽  
A Priori Information ◽  
Basin Scale ◽  
Priori Information ◽  
Gravity Field Models

Abstract. The estimation of terrestrial water storage variations at river basin scale is among the best documented applications of the GRACE (Gravity and Climate Experiment) satellite gravity mission. In particular, it is expected that GRACE closes the water balance at river basin scale and allows the verification, improvement and modeling of the related hydrological processes by combining GRACE amplitude estimates with hydrological models' output and in-situ data. When computing monthly mean storage variations from GRACE gravity field models, spatial filtering is mandatory to reduce GRACE errors, but at the same time yields biased amplitude estimates. The objective of this paper is three-fold. Firstly, we want to compute and analyze amplitude and time behaviour of the bias in GRACE estimates of monthly mean water storage variations for several target areas in Southern Africa. In particular, we want to know the relation between bias and the choice of the filter correlation length, the size of the target area, and the amplitude of mass variations inside and outside the target area. Secondly, we want to know to what extent the bias can be corrected for using a priori information about mass variations. Thirdly, we want to quantify errors in the estimated bias due to uncertainties in the a priori information about mass variations that are used to compute the bias. The target areas are located in Southern Africa around the Zambezi river basin. The latest release of monthly GRACE gravity field models have been used for the period from January 2003 until March 2006. An accurate and properly calibrated regional hydrological model has been developed for this area and its surroundings and provides the necessary a priori information about mass variations inside and outside the target areas. The main conclusion of the study is that spatial smoothing significantly biases GRACE estimates of the amplitude of annual and monthly mean water storage variations. For most of the practical applications, the bias will be positive, which implies that GRACE underestimates the amplitudes. The bias is mainly determined by the filter correlation length; in the case of 1000 km smoothing, which is shown to be an appropriate choice for the target areas, the annual bias attains values up to 50% of the annual storage; the monthly bias is even larger with a maximum value of 75% of the monthly storage. A priori information about mass variations can provide reasonably accurate estimates of the bias, which significantly improves the quality of GRACE water storage amplitudes. For the target areas in Southern Africa, we show that after bias correction, GRACE annual amplitudes differ between 0 and 30 mm from the output of a regional hydrological model, which is between 0% and 25% of the storage. Annual phase shifts are small, not exceeding 0.25 months, i.e. 7.5 deg. Our analysis suggests that bias correction of GRACE water storage amplitudes is indispensable if GRACE is used to calibrate hydrological models.

Independent a priori information for reduced intercomparison errors between TROPOMI and TCCON retrievals of methane and carbon monoxide

2020 ◽  
Author(s):  
Johannes Lutzmann ◽  
Ralf Sussmann ◽  
Huilin Chen ◽  
Frank Hase ◽  
Rigel Kivi ◽  
...  
Keyword(s):  
A Priori ◽  
Polar Vortex ◽  
Total Carbon ◽  
Space Agency ◽  
Ongoing Work ◽  
The Status ◽  
Priori Information ◽  
The Moment ◽  
Stratospheric Intrusions

<p>Ground-based column measurements of trace gases by FTIR spectrometers within the Total Carbon Column Observing Network (TCCON) provide accurate ground reference for the validation of the nadir-viewing hyperspectral Tropospheric Monitoring Instrument (TROPOMI) on-board the ESA satellite Sentinel 5 Precursor (S-5P). In such intercomparisons of two independent remote soundings, errors can occur as the a priori profiles used in the respective retrievals are i) differing from each other, and ii) both different from the true atmospheric state at the moment of observation. In certain conditions of atmospheric dynamics, e.g. polar vortex subsidence or stratospheric intrusions, which strongly alter the shape of vertical concentration profiles, these intercomparison errors can become considerable (Ostler et al., 2014).</p><p>In our work funded by the German Space Agency DLR and performed as part of the ESA AO project TCCON4S5P, we search for potential sources of realistic common a priori profiles for S-5P and TCCON CH<sub>4</sub> and CO measurements which reduce these large errors. We examine the performance of a number of chemical transport models and data assimilation systems in reproducing dynamical effects and in minimizing intercomparison errors. In-situ profiles measured by AirCores are used as validation where they are available. We present the status and results of our ongoing work.</p><p>Reference:</p><p>Ostler, A., Sussmann, R., Rettinger, M., Deutscher, N. M., Dohe, S., Hase, F., Jones, N., Palm, M., and Sinnhuber, B.-M.: Multistation intercomparison of column-averaged methane from NDACC and TCCON: impact of dynamical variability, Atmos. Meas. Tech., 7, 4081–4101, doi:10.5194/amt-7-4081-2014, 2014. Ostler, A., Sussmann, R., Rettinger, M., Deutscher, N. M., Dohe, S., Hase, F., Jones, N., Palm, M., and Sinnhuber, B.-M.: Multistation intercomparison of column-averaged methane from NDACC and TCCON: impact of dynamical variability, Atmos. Meas. Tech., 7, 4081–4101, doi:10.5194/amt-7-4081-2014, 2014.</p>

scholarly journals The bias in GRACE estimates of continental water storage variations

2007 ◽  
Vol 11 (4) ◽  
pp. 1227-1241 ◽  
Author(s):  
R. Klees ◽  
E. A. Zapreeva ◽  
H. C. Winsemius ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Southern Africa ◽  
Bias Correction ◽  
Hydrological Model ◽  
A Priori ◽  
Water Storage ◽  
Target Area ◽  
A Priori Information ◽  
Basin Scale ◽  
Priori Information

Abstract. The estimation of terrestrial water storage variations at river basin scale is among the best documented applications of the GRACE (Gravity and Climate Experiment) satellite gravity mission. In particular, it is expected that GRACE closes the water balance at river basin scale and allows the verification, improvement and modeling of the related hydrological processes by combining GRACE amplitude estimates with hydrological models' output and in-situ data. When computing monthly mean storage variations from GRACE gravity field models, spatial filtering is mandatory to reduce GRACE errors, but at the same time yields biased amplitude estimates. The objective of this paper is three-fold. Firstly, we want to compute and analyze amplitude and time behaviour of the bias in GRACE estimates of monthly mean water storage variations for several target areas in Southern Africa. In particular, we want to know the relation between bias and the choice of the filter correlation length, the size of the target area, and the amplitude of mass variations inside and outside the target area. Secondly, we want to know to what extent the bias can be corrected for using a priori information about mass variations. Thirdly, we want to quantify errors in the estimated bias due to uncertainties in the a priori information about mass variations that are used to compute the bias. The target areas are located in Southern Africa around the Zambezi river basin. The latest release of monthly GRACE gravity field models have been used for the period from January 2003 until March 2006. An accurate and properly calibrated regional hydrological model has been developed for this area and its surroundings and provides the necessary a priori information about mass variations inside and outside the target areas. The main conclusion of the study is that spatial smoothing significantly biases GRACE estimates of the amplitude of annual and monthly mean water storage variations and that bias correction using existing hydrological models significantly improves the quality of GRACE estimates. For most of the practical applications, the bias will be positive, which implies that GRACE underestimates the amplitudes. The bias is mainly determined by the filter correlation length; in the case of 1000 km smoothing, which is shown to be an appropriate choice for the target areas, the annual bias attains values up to 50% of the annual storage; the monthly bias is even larger with a maximum value of 75% of the monthly storage. A priori information about mass variations can provide reasonably accurate estimates of the bias, which significantly improves the quality of GRACE water storage amplitudes. For the target areas in Southern Africa, we show that after bias correction, GRACE annual amplitudes differ between 0 and 30 mm from the output of a regional hydrological model, which is between 0% and 25% of the storage. Annual phase shifts are small, not exceeding 0.25 months, i.e. 7.5 deg. It is shown that after bias correction, the fit between GRACE and a hydrological model is overoptimistic, if the same hydrological model is used to estimate the bias and to compare with GRACE. If another hydrological model is used to compute the bias, the fit is less, although the improvement is still very significant compared with uncorrected GRACE estimates of water storage variations. Therefore, the proposed approach for bias correction works for the target areas subject to this study. It may also be an option for other target areas provided that some reasonable a priori information about water storage variations are available.

Enhancing the Resolving Ability of ERT for Imaging Saltwater Intrusion through Improvements in Inversion Methods: A Laboratory and Numerical Study

Geophysics ◽  
2021 ◽  
pp. 1-65
Author(s):  
Meredith Goebel ◽  
Rosemary Knight ◽  
Seogi Kang
Keyword(s):  
Saltwater Intrusion ◽  
Reference Model ◽  
Numerical Study ◽  
A Priori ◽  
Geophysical Methods ◽  
Resistivity Tomography ◽  
Porosity And Permeability ◽  
Priori Information ◽  
High Level

Mapping and monitoring of saltwater intrusion are critical to the sustainable management of groundwater in coastal aquifers around the world. Increasingly, geophysical methods, such as electrical resistivity tomography (ERT), have been used to address these needs. We identified methods for the inversion of ERT data that would most accurately map the location and geometry of an intrusion wedge. This was accomplished using both laboratory and synthetic experiments, with the classic representation of an intrusion wedge perpendicular to the coast. The laboratory experiments allowed us to collect ERT data on a saltwater intrusion wedge in an environment where we had supporting data that provided (1) the distribution of salinity within the tank with which to verify our inversion results, (2) the resistivity, porosity and permeability of the porous medium, and (3) the transform between resistivity and salinity. The synthetic experiments allowed to explore issues of specific interest related to the presence of lithologic heterogeneity at a field site: the role of lithologic heterogeneity in introducing complexity both the resistivity-salinity relationship and the geometry of the saltwater intrusion wedge. We found that using a reference model with a good approximation of the wedge to inform the inversion greatly improved the ability of the resulting resistivity profile to map the wedge. Where there was no, or limited lithologic heterogeneity, a parametric approach, which constrained the range of possible solutions by solving for a sharp interface between the saltwater and freshwater regions, was very effective at capturing the wedge location and geometry. Where there was lithologic heterogeneity, a hybrid between the parametric and informed inversion approaches was most effective, resolving the wedge with a high level of accuracy with little a priori information.

Innovative Approach to Information Search by Example of a Patent Analysis of an Important Substitution Plan

2020 ◽  
pp. 143-157
Author(s):  
Maria A. Milkova
Keyword(s):  
Information Search ◽  
Topic Modeling ◽  
Cognitive Biases ◽  
A Priori ◽  
Import Substitution ◽  
Innovative Approach ◽  
Iterative Search ◽  
Comprehensive Picture ◽  
Priori Information ◽  
Selection Of

Nowadays the process of information accumulation is so rapid that the concept of the usual iterative search requires revision. Being in the world of oversaturated information in order to comprehensively cover and analyze the problem under study, it is necessary to make high demands on the search methods. An innovative approach to search should flexibly take into account the large amount of already accumulated knowledge and a priori requirements for results. The results, in turn, should immediately provide a roadmap of the direction being studied with the possibility of as much detail as possible. The approach to search based on topic modeling, the so-called topic search, allows you to take into account all these requirements and thereby streamline the nature of working with information, increase the efficiency of knowledge production, avoid cognitive biases in the perception of information, which is important both on micro and macro level. In order to demonstrate an example of applying topic search, the article considers the task of analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1,500 products and technologies for the proposed import substitution. The use of patent search based on topic modeling allows to search immediately by the blocks of a priori information – terms of industrial plans for import substitution and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to visually obtain more detailed information about which groups of products and technologies have been patented.

scholarly journals Computational Method for Wavefront Sensing Based on Transport-Of-Intensity Equation

Photonics ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 177
Author(s):  
Iliya Gritsenko ◽  
Michael Kovalev ◽  
George Krasin ◽  
Matvey Konoplyov ◽  
Nikita Stsepuro
Keyword(s):  
A Priori ◽  
Spherical Wave ◽  
Computational Method ◽  
Optical Systems ◽  
Radius Of Curvature ◽  
Imaging Method ◽  
Wavefront Sensing ◽  
Phase Imaging ◽  
Transport Of Intensity Equation ◽  
Priori Information

Recently the transport-of-intensity equation as a phase imaging method turned out as an effective microscopy method that does not require the use of high-resolution optical systems and a priori information about the object. In this paper we propose a mathematical model that adapts the transport-of-intensity equation for the purpose of wavefront sensing of the given light wave. The analysis of the influence of the longitudinal displacement z and the step between intensity distributions measurements on the error in determining the wavefront radius of curvature of a spherical wave is carried out. The proposed method is compared with the traditional Shack–Hartmann method and the method based on computer-generated Fourier holograms. Numerical simulation showed that the proposed method allows measurement of the wavefront radius of curvature with radius of 40 mm and with accuracy of ~200 μm.

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