Prestack Bayesian statistical inversion constrained by reflection features

Geophysics ◽  
2020 ◽  
pp. 1-98
Author(s):  
Bo Yu ◽  
Hui Zhou ◽  
Lingqian Wang ◽  
Wenling Liu
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Posterior Probability ◽  
Probability Distributions ◽  
Seismic Profile ◽  
Inversion Method ◽  
Reflection Coefficients ◽  
Geological Structures ◽  
Practical Applications ◽  
Geological Information

Bayesian statistical inversion can integrate diverse datasets to infer the posterior probability distributions of subsurface elastic properties. However, certain existing methods may suffer from two issues in practical applications, namely spatial discontinuities and the uncertainty caused by the low-quality seismic traces. These limitations are evident in prestack statistical inversion since some traces in prestack angle gathers may be missing or low-quality. We propose a prestack Bayesian statistical inversion method constrained by reflection features to alleviate these issues. Based on a Bayesian linearized inversion framework, the proposed inversion approach is implemented by integrating the prestack seismic data with reflection features. The reflection features are captured from the poststack seismic profile, and they represent the relationships of the reflection coefficients between different traces. By utilizing the proposed approach, we are able to achieve superior inversion results and to evaluate inversion uncertainty simultaneously even with the low-quality prestack seismic data. The results of the synthetic and field data tests confirm the theoretical and practical effects of the reflection features on improving inversion continuity and accuracy and reducing inversion uncertainty. Moreover, this work gives a novel way to integrate the information of geological structures in statistical inversion methods. Other geological information, which can be linearized accurately or approximately, can be utilized in this manner.

scholarly journals Geologi situs paleolitik das kikim, kabupaten lahat, provinsi sumatra selatan.

KALPATARU ◽  
2018 ◽  
Vol 26 (2) ◽  
pp. 73
Author(s):  
Muhammad Fadhlan Syuaib Intan
Keyword(s):  
Literature Review ◽  
Research Method ◽  
Field Data ◽  
Research Area ◽  
Geological Structure ◽  
Strike Slip ◽  
Survey Analysis ◽  
Geological Structures ◽  
Geological Information ◽  
Surface Geology

Lahat is one of the districts within the province of South Sumatra, the site of research, saving many cultural remains, one of them from the paleolithic period, which for so long received no attention from environmental researchers. This is the basis of the main problems that cover geology in general. Therefore, the purpose of this study is to conduct surface geology mapping in general as an effort to present geological information, while the aim is to know the geomorphological aspects, stratigraphy, geological structures associated with existence in paleolithic sites of research area. The research method begins with literature review, survey, analysis, and interpretation of field data. Environmental observations provide information about the landscape consisting of terrestrial morphology units, weak corrugated morphology units, and strong corrugated morphology units. The rivers are in the Old River, the Adult River, and Periodic /Permanent River. The constituent rocks are Gumai Formation, Benakat Air Formation, Muara Enim Formation, Kasai Formation, and alluvial. The geological structure is a strike slip fault that flows northeast-southeast. The study was conducted on the Kikim River, Lingsing River, and Pangi River, which stretches from east to west with direction from south to north. Exploration in the Kikim Basin, Lahat District has managed to find 30 paleolithic sites.Keywords: Geology, Pleistocene, Paleolithic, Open SiteABSTRAKLahat merupakan salah satu kabupaten dalam Provinsi Sumatra Selatan yang menjadi lokasi penelitian, menyimpan banyak tinggalan budaya, salah satunya dari masa paleolitik, yang sekian lama tak mendapat perhatian dari para peneliti lingkungan. Hal inilah yang dijadikan dasar permasalahan utama yang mencakup geologi secara umum. Oleh sebab itu, maksud penelitian ini dalah untuk melakukan pemetaan geologi permukaan secara umum sebagai salah satu upaya untuk menyajikan informasi geologi, sedangkan tujuannya adalah untuk mengetahui aspek-aspek geomorfologi, stratigrafi, struktur geologi yang dikaitkan dengan keberadaan di situs-situs paleolitik wilayah penelitian. Metode penelitian diawali dengan kajian pustaka, survei, analisis, dan interpretasi data lapangan. Pengamatan lingkungan memberikan informasi tentang bentang alamnya yang terdiri dari satuan morfologi dataran, satuan morfologi bergelombang lemah, dan satuan morfologi bergelombang kuat. Sungainya berstadia Sungai Tua, Sungai Dewasa-Tua, dan Sungai Periodik/Permanen. Batuan penyusun adalah Formasi Gumai, Formasi Air Benakat, Formasi Muara Enim, Formasi Kasai, dan aluvial. Struktur geologi berupa patahan geser yang berarah timur laut-tenggara. Penelitian dilaksanakan di Sungai Kikim, Sungai Lingsing, dan Sungai Pangi, yang membentang dari timur ke barat dengan arah aliran dari selatan ke utara. Eksplorasi di DAS Kikim, Kabupaten Lahat telah berhasil menemukan 30 situs paleolitik. Kata kunci: Geologi, Plistosen, Paleolitik, Situs Terbuka

scholarly journals Probabilistic moveout analysis by time warping

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. U1-U20
Author(s):  
Yanadet Sripanich ◽  
Sergey Fomel ◽  
Jeannot Trampert ◽  
William Burnett ◽  
Thomas Hess
Keyword(s):  
Parameter Estimation ◽  
Posterior Probability ◽  
Model Building ◽  
Probability Distributions ◽  
Real Data ◽  
Inversion Method ◽  
Time Warping ◽  
Anisotropic Models ◽  
Trade Offs ◽  
Set Up

Parameter estimation from reflection moveout analysis represents one of the most fundamental problems in subsurface model building. We have developed an efficient moveout inversion method based on the process of automatic flattening of common-midpoint (CMP) gathers using local slopes. We find that as a by-product of this flattening process, we can also estimate reflection traveltimes corresponding to the flattened CMP gathers. This traveltime information allows us to construct a highly overdetermined system and subsequently invert for moveout parameters including normal-moveout velocities and quartic coefficients related to anisotropy. We use the 3D generalized moveout approximation (GMA), which can accurately capture the effects of complex anisotropy on reflection traveltimes as the basis for our moveout inversion. Due to the cheap forward traveltime computations by GMA, we use a Monte Carlo inversion scheme for improved handling of the nonlinearity between the reflection traveltimes and moveout parameters. This choice also allows us to set up a probabilistic inversion workflow within a Bayesian framework, in which we can obtain the posterior probability distributions that contain valuable statistical information on estimated parameters such as uncertainty and correlations. We use synthetic and real data examples including the data from the SEAM Phase II unconventional reservoir model to demonstrate the performance of our method and discuss insights into the problem of moveout inversion gained from analyzing the posterior probability distributions. Our results suggest that the solutions to the problem of traveltime-only moveout inversion from 2D CMP gathers are relatively well constrained by the data. However, parameter estimation from 3D CMP gathers associated with more moveout parameters and complex anisotropic models are generally nonunique, and there are trade-offs among inverted parameters, especially the quartic coefficients.

Q-compensated acoustic impedance inversion of attenuated seismic data: Numerical and field-data experiments

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R553-R567 ◽  
Author(s):  
Xintao Chai ◽  
Genyang Tang ◽  
Fangfang Wang ◽  
Hanming Gu ◽  
Xinqiang Wang
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Field Data ◽  
Reservoir Characterization ◽  
Rock Properties ◽  
Inversion Method ◽  
Data Sets ◽  
Anelastic Attenuation ◽  
Text Filtering ◽  
Frequency Components

Acoustic impedance (AI) inversion is of great interest because it extracts information regarding rock properties from seismic data and has successful applications in reservoir characterization. During wave propagation, anelastic attenuation and dispersion always occur because the subsurface is not perfectly elastic, thereby diminishing the seismic resolution. AI inversion based on the convolutional model requires that the input data be free of attenuation effects; otherwise, low-resolution results are inevitable. The intrinsic instability that occurs while compensating for the anelastic effects via inverse [Formula: see text] filtering is notorious. The gain-limit inverse [Formula: see text] filtering method cannot compensate for strongly attenuated high-frequency components. A nonstationary sparse reflectivity inversion (NSRI) method can estimate the reflectivity series from attenuated seismic data without the instability issue. Although AI is obtainable from an inverted reflectivity series through recursion, small inaccuracies in the reflectivity series can result in large perturbations in the AI result because of the cumulative effects. To address these issues, we have developed a [Formula: see text]-compensated AI inversion method that directly retrieves high-resolution AI from attenuated seismic data without prior inverse [Formula: see text] filtering based on the theory of NSRI and AI inversion. This approach circumvents the intrinsic instability of inverse [Formula: see text] filtering by integrating the [Formula: see text] filtering operator into the convolutional model and solving the inverse problem iteratively. This approach also avoids the ill-conditioned nature of the recursion scheme for transforming an inverted reflectivity series to AI. Experiments on a benchmark Marmousi2 model validate the feasibility and capabilities of our method. Applications to two field data sets verify that the inversion results generated by our approach are mostly consistent with the well logs.

Estimation of reflection coefficients from zero‐offset field data

Geophysics ◽  
1993 ◽  
Vol 58 (11) ◽  
pp. 1634-1645 ◽  
Author(s):  
Børge Arntsen ◽  
Bjørn Ursin
Keyword(s):  
Point Source ◽  
Least Squares ◽  
Seismic Data ◽  
Field Data ◽  
Model Fitting ◽  
Seismic Survey ◽  
Reflection Coefficients ◽  
Data Set ◽  
Seismic Experiment ◽  
Zero Offset

The classical one‐dimensional (1-D) inverse problem consists of estimating reflection coefficients from surface seismic data using the 1-D wave equation. Several authors have found stable solutions to this problem using least‐squares model‐fitting methods. We show that the application of these plane‐wave solutions to seismic data generated with a point source can lead to errors in estimating reflection coefficients. This difficulty is avoided by using a least‐squares model fitting scheme describing vertically traveling waves originating from a point source. It is shown that this method is roughly equivalent to deterministic deconvolution with built‐in multiple removal and compensation for spherical spreading. A true zero‐offset field data set from a specially designed seismic experiment is then used as input to estimate reflection coefficients. Stacking velocities from a conventional seismic survey were used to estimate spherical spreading. The resulting reflection coefficients are shown to correlate well with an available well log.

Non-stationary local signal-and-noise orthogonalization

Geophysics ◽  
2021 ◽  
pp. 1-96
Author(s):  
Yangkang Chen ◽  
Sergey Fomel
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Optimization Problem ◽  
Seismic Profile ◽  
New Method ◽  
Successful Performance ◽  
Stationary Model ◽  
Seismic Processing ◽  
Orthogonalization Method ◽  
Low Pass

The local signal-and-noise orthogonalization method has been widely used in the seismic processing and imaging community. In the local signal-and-noise orthogonalization method, a fixed triangle smoother is used for regularizing the local orthogonalization weight, which is based on the assumption that the energy is homogeneously distributed across the whole seismic profile. The fixed triangle smoother limits the performance of the local orthogonalization method in processing complicated seismic datasets. Here, we propose a new local orthogonalization method that uses a variable triangle smoother. The non-stationary smoothing radius is obtained by solving an optimization problem, where the low-pass filtered seismic data are matched by the smoothed data in terms of the local frequency attribute. The new local orthogonalization method with non-stationary model smoothness constraint is called the non-stationary local orthogonalization method. We use several synthetic and field data examples to demonstrate the successful performance of the new method.

A Solution of Imaging and Wave Impedance Inversion of Seismic Data

2013 ◽  
Vol 373-375 ◽  
pp. 569-573
Author(s):  
Rui Yang ◽  
Guang Xun Chen ◽  
Pan Ke Qin ◽  
Neng You Wu ◽  
Jia Shun Yu
Keyword(s):  
Seismic Data ◽  
Wave Impedance ◽  
Inversion Method ◽  
Reflection Coefficients ◽  
Inversion Result ◽  
Impedance Model ◽  
Impedance Inversion ◽  
High Vertical Resolution ◽  
And Inversion

In order to improve the resolution and accuracy of the inversion, this paper proposed a new inversion method. By introducing constraint sparse spike inversion, the new method can fully take the advantages of high vertical resolution of logging data and the preferable transverse continuity of the seismic data to improve the resolution of the profiles and the quality of imaging and inversion in specific areas. Experimental results showed that this solution can deduce more precise and reasonable inversion result than other inversion solution. Constraint sparse spike inversion can generate reflection coefficients with broad frequency band and solve the marking problems preferably, thereby makes the impedance model obtained from the inversion even close to the actual situation underground.

Autocorrelogram migration: Theory

Geophysics ◽  
2003 ◽  
Vol 68 (5) ◽  
pp. 1685-1694 ◽  
Author(s):  
Gerard T. Schuster ◽  
Fred Followill ◽  
Lewis J. Katz ◽  
Jianhua Yu ◽  
Zhaojun Liu
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Seismic Profile ◽  
Coherent Noise ◽  
Direct Wave ◽  
Pilot Signal ◽  
Source Signal ◽  
2D And 3D ◽  
Direct Primary ◽  
Imaging Conditions

We present the equations for migrating inverse‐vertical‐seismic‐profile‐while‐drilling and common‐midpoint autocorrelograms. These equations partly generalize the 1D autocorrelation imaging methods of Katz and Claerbout to 2D and 3D media, and also provide a formal mathematical procedure for imaging the reflectivity distribution from autocorrelograms. The imaging conditions are designed to migrate specific events in the autocorrelograms, either the direct‐primary correlations or the direct‐ghost correlations. Here, direct stands for direct wave, primary stands for primary reflections, and ghost denotes free‐surface ghost reflections. The main advantage in migrating autocorrelograms is that the source wavelet does not need to be known, which is the case for seismic data generated by a rotating drill bit or for vibroseis data with a corrupted pilot signal. Another advantage is that the source and receiver static problems are mitigated by autocorrelation migration. Two limitations are that autocorrelation of traces amplifies coherent noise such as surface waves, and produces undesirable coherent noise denoted as “virtual multiples.” Similar to “physical multiples,” such noise can, in principle, be partially suppressed by filtering and stacking of migration images obtained from many different shot gathers. Results with both synthetic and field data validate this conjecture, and show that autocorrelogram migration can be a viable alternative to standard migration when the source signal is not adequately known or there are severe static problems.

Two-dimensional Bayesian inversion of magnetotelluric data using trans-dimensional Gaussian processes

2021 ◽  
Author(s):  
Daniel Blatter ◽  
Anandaroop Ray ◽  
Kerry Key
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Field Data ◽  
Probability Distributions ◽  
Bulk Composition ◽  
Computational Cost ◽  
Model Space ◽  
Bayesian Inversion ◽  
Adaptive Finite Element ◽  
Magnetotelluric Data

Summary Bayesian inversion of electromagnetic data produces crucial uncertainty information on inferred subsurface resistivity. Due to their high computational cost, however, Bayesian inverse methods have largely been restricted to computationally expedient 1D resistivity models. In this study, we successfully demonstrate, for the first time, a fully 2D, trans-dimensional Bayesian inversion of magnetotelluric data. We render this problem tractable from a computational standpoint by using a stochastic interpolation algorithm known as a Gaussian process to achieve a parsimonious parametrization of the model vis-a-vis the dense parameter grids used in numerical forward modeling codes. The Gaussian process links a trans-dimensional, parallel tempered Markov chain Monte Carlo sampler, which explores the parsimonious model space, to MARE2DEM, an adaptive finite element forward solver. MARE2DEM computes the model response using a dense parameter mesh with resistivity assigned via the Gaussian process model. We demonstrate the new trans-dimensional Gaussian process sampler by inverting both synthetic and field magnetotelluric data for 2D models of electrical resistivity, with the field data example converging within 10 days on 148 cores, a non-negligible but tractable computational cost. For a field data inversion, our algorithm achieves a parameter reduction of over 32x compared to the fixed parameter grid used for the MARE2DEM regularized inversion. Resistivity probability distributions computed from the ensemble of models produced by the inversion yield credible intervals and interquartile plots that quantitatively show the non-linear 2D uncertainty in model structure. This uncertainty could then be propagated to other physical properties that impact resistivity including bulk composition, porosity and pore-fluid content.

scholarly journals The importance of entrainment and bulking on debris flow runout modeling: examples from the Swiss Alps

2015 ◽  
Vol 15 (11) ◽  
pp. 2569-2583 ◽  
Author(s):  
F. Frank ◽  
B. W. McArdell ◽  
C. Huggel ◽  
A. Vieli
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Field Data ◽  
Average Rate ◽  
Hazard Analysis ◽  
Maximum Shear Stress ◽  
Empirical Relationship ◽  
Swiss Alps ◽  
Field Site ◽  
Practical Applications

Abstract. This study describes an investigation of channel-bed entrainment of sediment by debris flows. An entrainment model, developed using field data from debris flows at the Illgraben catchment, Switzerland, was incorporated into the existing RAMMS debris-flow model, which solves the 2-D shallow-water equations for granular flows. In the entrainment model, an empirical relationship between maximum shear stress and measured erosion is used to determine the maximum potential erosion depth. Additionally, the average rate of erosion, measured at the same field site, is used to constrain the erosion rate. The model predicts plausible erosion values in comparison with field data from highly erosive debris flow events at the Spreitgraben torrent channel, Switzerland in 2010, without any adjustment to the coefficients in the entrainment model. We find that by including bulking due to entrainment (e.g., by channel erosion) in runout models a more realistic flow pattern is produced than in simulations where entrainment is not included. In detail, simulations without entrainment show more lateral outflow from the channel where it has not been observed in the field. Therefore the entrainment model may be especially useful for practical applications such as hazard analysis and mapping, as well as scientific case studies of erosive debris flows.

Sign in / Sign up

Export Citation Format

Share Document