Sensitivity analysis of optimization parameters on cooperative inversion of seismic reflection and gravity data

2020 ◽  
Author(s):  
Maiara Gonçalves ◽  
Emilson Leite
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Seismic Data ◽  
Optimization Methods ◽  
Inversion Method ◽  
Standard Case ◽  
Reflection Seismic ◽  
Gravimetric Data ◽  
Standard Values ◽  
Cooperative Inversion

<p>Reflections of seismic waves are strongly distorted by the presence of complex geological structures (e.g. salt bodies) and their vertical resolution is usually of the order of a few tens of meters, imposing limitations in the construction of subsurface models. One way to improve the reliability of such models is to integrate reflection seismic data with other types of geophysical data, such as gravimetric data, since the latter provide an additional link to map geological structures that exhibit density contrasts with respect to their surroundings. In a previous study, we developed a cooperative inversion method of 2D post-stack and migrated reflection seismic data, and gravimetric data. Using that inversion method, we minimize two problems: (1) the problem of the distortion of reflection seismic data due to the presence of complex geological bodies and (2) the problem of the greater ambiguity and the commonly lower resolution of the models obtained only from gravimetric anomalies. The method incorporates a technique to decrease the number of variables and is solved by optimization of the gravity inverse problem, thus reducing computing time. The objective function of cooperative inversion was minimized using three different methods of optimization: (1) simplex, (2) simulated annealing, and (3) genetic algorithm. However, these optimization methods have internal parameters which affect the convergence rate and objective function values. These parameters are usually chosen accordingly to previous references. Although the usage of these standard values is widely accepted, the best values to assure effectiveness and stability of convergence are case-dependent. In the present study, we propose a sensitivity analysis on the internal parameters of the optimization methods for the previously presented cooperative inversion. First, we developed the standard case, which is an inversion performed using all parameters at their standard values. Then, the sensitivity analysis is performed by running multiple inversions, each one with a set of parameters. Each set is obtained by modifying the value of a single parameter either for a lower or for a higher value, keeping all other values at their standard values. The results obtained by each setting are compared to the results of the standard case. The compared results are both the number of evaluations and the final value of the objective function. We then classify parameters accordingly to their relative influence on the optimization processes. The sensitivity analysis provides insight into the best practices to deal with object-based cooperative inversion schemes. The technique was tested using a synthetic model calculated from the Benchmark BP 2004, representing an offshore sedimentary basin containing salt bodies and small hydrocarbons reservoirs.</p>

Simultaneous inversion of prestack seismic data for rock properties using simulated annealing

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 1877-1885 ◽  
Author(s):  
Xin‐Quan Ma
Keyword(s):  
Simulated Annealing ◽  
Seismic Data ◽  
Low Frequency ◽  
Optimization Procedure ◽  
P Wave ◽  
Rock Properties ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Simultaneous Inversion ◽  
Reflection Seismic

A new prestack inversion algorithm has been developed to simultaneously estimate acoustic and shear impedances from P‐wave reflection seismic data. The algorithm uses a global optimization procedure in the form of simulated annealing. The goal of optimization is to find a global minimum of the objective function, which includes the misfit between synthetic and observed prestack seismic data. During the iterative inversion process, the acoustic and shear impedance models are randomly perturbed, and the synthetic seismic data are calculated and compared with the observed seismic data. To increase stability, constraints have been built into the inversion algorithm, using the low‐frequency impedance and background Vs/Vp models. The inversion method has been successfully applied to synthetic and field data examples to produce acoustic and shear impedances comparable to log data of similar bandwidth. The estimated acoustic and shear impedances can be combined to derive other elastic parameters, which may be used for identifying of lithology and fluid content of reservoirs.

High resolution fixed-point seismic inversion

2021 ◽  
pp. 1-54
Author(s):  
Song Pei ◽  
Xingyao Yin ◽  
Zhaoyun Zong ◽  
Kun Li
Keyword(s):  
Fixed Point ◽  
Objective Function ◽  
Seismic Data ◽  
Target Function ◽  
Recursive Formula ◽  
Frequency Component ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Good Convergence ◽  
Band Limited

Resolution improvement always presents the crucial task in geological inversion. Band-limited characteristics of seismic data and noise make seismic inversion complicated. Specifically, geological inversion suffers from the deficiency of both low- and high-frequency components. We propose the fixed-point seismic inversion method to alleviate these issues. The problem of solving objective function is transformed into the problem of finding the fixed-point of objective function. Concretely, a recursive formula between seismic signal and reflection coefficient is established, which is characterized by good convergence and verified by model examples. The error between the model value and the inverted value is reduced to around zero after few iterations. The model examples show that in either case, that is, the seismic traces are noise-free or with a little noise, the model value can almost be duplicated. Even if the seismic trace is accompanied by the moderate noise, the optimal inverted results can still be obtained with the proposed method. The initial model constraint is further introduced into the objective function to increase the low-frequency component of the inverted results by adding prior information into the target function. The singular value decomposition (SVD) method is applied to the inversion framework, thus making a high improvement of anti-noise ability. At last, the synthetic models and seismic data are investigated following the proposed method. The inverted results obtained from the fixed-point seismic inversion are compared with those obtained from the conventional seismic inversion, and it is found that the former has a higher resolution than the latter.

Cooperative deep learning inversion of controlled-source electromagnetic data for salt delineation

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. E121-E137 ◽  
Author(s):  
Seokmin Oh ◽  
Kyubo Noh ◽  
Soon Jee Seol ◽  
Joongmoo Byun
Keyword(s):  
Deep Learning ◽  
Test Data ◽  
Seismic Data ◽  
Surrounding Medium ◽  
Geophysical Data ◽  
Single Type ◽  
Inversion Method ◽  
Data Types ◽  
Controlled Source ◽  
Cooperative Inversion

Various geophysical data types have advantages for exploring the subsurface, and more reliable exploration can be realized through integration of such data. However, the imaging of physical properties based on deep learning (DL) techniques, which has received considerable attention because of its enormous potential, has generally been performed using only a single type of data. We have developed a cooperative inversion method based on supervised DL for salt delineation. Controlled-source electromagnetic (CSEM) data, which can effectively distinguish a salt body with high electrical resistivity from the surrounding medium, are used as data for cooperative inversion, with high-resolution information derived from seismic data used as the constraint. This approach can entrain seismic information into a fully convolutional network designed to invert CSEM data to reconstruct the resistivity distribution. The inversion network is trained using large synthetic data sets, including the seismic information derived from seismic data as well as CSEM data and resistivity models. A cooperative strategy for reasonable entrainment of seismic information into the inversion network is established based on analysis of the network and kernels of the convolutional layers. The performance of the proposed method is demonstrated through experiments on test data generated for resistivity models for complex salt structures. The trained cooperative inversion network shows improved salt delineation results compared to the independent inversion network, irrespective of noise levels added to the test data, due to restriction of the resistivity model to fit seismic information. Moreover, training with noise-added data decreased the effects of noise on prediction results, similar to the case of adversarial training. We develop the possibility of combining geophysical data with a constraint using DL-based techniques.

Joint inversion of seismic, vertical seismic profiling, and crosswell data — A case study from China

2017 ◽  
Vol 5 (1) ◽  
pp. T107-T119 ◽  
Author(s):  
Hemin Yuan ◽  
De-hua Han ◽  
Hui Li ◽  
Danping Cao
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Seismic Inversion ◽  
Inversion Method ◽  
A Posteriori ◽  
Frequency Bandwidth ◽  
Surface Reflection ◽  
Seismic Profiling ◽  
Reflection Seismic ◽  
Vertical Seismic Profiling

Three-dimensional poststack and prestack seismic inversion results such as P- and S-impedance are commonly used for reservoir characterization. However, the frequency bandwidth of surface-based reflection seismic surveys usually ranges from 10 to 70 Hz, and these surveys have limited vertical resolution. The frequency bandwidth of vertical seismic profiling (VSP) and crosswell data is much wider than that of surface reflection seismic data, and it can give a detailed illumination of the subsurface around the borehole. We test a joint inversion method that integrated surface reflection seismic, VSP, and crosswell data. To better constrain the inversion results, we further integrate a posteriori information on the reflectivity obtained from petrophysics data into the inversion procedure. The a posteriori distribution we use is a modified-Cauchy distribution obtained from the statistical analysis of petrophysics data. To demonstrate the effectiveness of our algorithm, we applied our inversion strategy to a 2D synthetic model and a real seismic data set, and an uncertainty assessment was also performed. The joint inversion method can detect the thin layers that surface seismic inversion fail to, demonstrating the higher resolution of the method.

The Application of Spectrum Inversion Based on Genetic Algorithm to Recognition of Thin Layer

2015 ◽  
Vol 738-739 ◽  
pp. 405-408
Author(s):  
Kai Yan Wang ◽  
Dan Liu ◽  
Shi Long Wang
Keyword(s):  
Genetic Algorithm ◽  
Reflection Coefficient ◽  
Thin Layer ◽  
Seismic Data ◽  
Optimization Methods ◽  
Seismic Exploration ◽  
Inversion Method ◽  
Data Resolution ◽  
Starting Model ◽  
Spectrum Inversion

Spectrum inversion method based on genetic algorithm (GA) is a kind of optimization methods for improving seismic data resolution, compared with the spectrum inversion based on conjugate gradient method,GA does not require a good starting model but rather a search space.In this paper, this algorithm is first proposed, and applied to thin layer identification,base on the resolution of odd and even component of reflection coefficient in thin layer,derive the spectrum inversion objective function, briefly describe the principle of genetic algorithm, finally in the wedge model and actual data for trial. According to the result, spectrum inversion method based on genetic algorithm can improve the ability of thin layer identification to a certain extent, improve the resolution of the seismic exploration.

Automatic traveltime inversion via sparse decomposition of seismic data

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R659-R668 ◽  
Author(s):  
Bo Feng ◽  
Huazhong Wang ◽  
Ru-Shan Wu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Velocity Model ◽  
Inversion Method ◽  
Traveltime Inversion ◽  
Sparse Decomposition ◽  
Reflection Seismic ◽  
New Strategy ◽  
Gradient Based ◽  
Intersection Criterion

We have developed an automatic traveltime inversion (ATI) method to estimate the macrovelocity model from reflection seismic data. First, we extract the kinematic information (i.e., source/receiver ray parameters, traveltime, and source/receiver coordinates) of locally coherent events using a sparse-decomposition method. And then we evaluate a new strategy to calculate the reflection traveltime residual based on a ray-intersection criterion, eliminating the influence of seismic amplitude to the estimation of the traveltime residual. The velocity model can be updated iteratively by minimizing the traveltime residual functional with a gradient-based method. To obtain a smooth gradient free of artifacts, we first estimate the high-wavenumber components of the functional gradient with a total variation (TV) regularization method and then subtract it from the full gradient. Because the reflection traveltime residual calculation and velocity update are fully automated procedures, the proposed traveltime inversion method is referred to as ATI. We determine with 2D synthetic and field examples that ATI does not need a good starting model. Furthermore, it requires neither low-frequency seismic data nor long-offset acquisition. Nevertheless, the proposed traveltime residual calculation strategy is only valid for the 2D case, which limits its 3D applicability. We explore a possible solution for 3D extension.

A More Effective Formulation of the Path Generation Mechanism Synthesis Problem

1994 ◽  
Author(s):  
Irfan Ullah ◽  
Sridhar Kota
Keyword(s):  
Objective Function ◽  
Error Function ◽  
Mathematical Optimization ◽  
Optimization Methods ◽  
Complex Nature ◽  
Mechanism Synthesis ◽  
Structural Error ◽  
Crank Angle ◽  
The Common ◽  
Selection Of

Abstract Use of mathematical optimization methods for synthesis of path-generating mechanisms has had only limited success due to the very complex nature of the commonly used Structural Error objective function. The complexity arises, in part, because the objective function represents not only the error in the shape of the coupler curve, but also the error in location, orientation and size of the curve. Furthermore, the common introduction of timing (or crank angle), done generally to facilitate selection of corresponding points on the curve for calculating structural error, has little practical value and unnecessarily limits possible solutions. This paper proposes a new objective function, based on Fourier Descriptors, which allows search for coupler curve of the desired shape without reference to location, orientation, or size. The proposed objective function compares overall shape properties of curves rather than making point-by-point comparison and therefore does not requires prescription of timing. Experimental evidence is provided to show that it is much easier to search the space of the proposed objective function compared to the structural error function.

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