Exploration beyond seismic: The role of electromagnetics and gravity gradiometry in deep water subsalt plays of the Red Sea

2014 ◽  
Vol 2 (3) ◽  
pp. SH33-SH53 ◽  
Author(s):  
Daniele Colombo ◽  
Gary McNeice ◽  
Nickolas Raterman ◽  
Mike Zinger ◽  
Diego Rovetta ◽  
...  
Keyword(s):  
Red Sea ◽  
Model Building ◽  
Three Dimensional ◽  
Velocity Model ◽  
Seismic Exploration ◽  
Gravity Gradiometry ◽  
3D Inversion ◽  
Lateral Velocity ◽  
Velocity Models ◽  
Basement Structures

The Red Sea is characterized by thick salt sequences representing a seal for potential hydrocarbon accumulations within Tertiary formations deposited over deep basement structures. The Red Sea “salt” is characterized by halite concentrations embedded in layered evaporite sequences composed of evaporite and clastic lithologies. Salt complicates seismic exploration efforts in the Red Sea by generating vertical and lateral velocity variations that are difficult to estimate by seismic methods alone. In these conditions, the exploration challenges of independently imaging the subsalt section and provide enhanced velocity model building capabilities were addressed by a multigeophysics strategy involving marine electromagnetics (magnetotellurics and controlled source electromagnetics [CSEM]) and gravity gradiometry surveys colocated with wide azimuth seismic. Three-dimensional inversion of MT and CSEM is performed first with minimal a priori constraints and then by including variable amounts of interpretation in the starting models. The internal variations in the evaporitic overburden, the subsalt, and the basement structures are independently imaged by combined electromagnetic methods and confirmed by new drilling results. CSEM, in particular, provides unprecedented detail of the internal structures within the salt overburden while magnetotellurics provides excellent reconstruction of the base of salt and basement. Gravity gradiometry shows primary sensitivity to the basement and the corresponding 3D inversion provides density distributions structurally consistent with the resistivity volumes. The common-structure, multiparameter models obtained from 3D inversion deliver additional aid to seismic interpreters to further derisk exploration in the Red Sea and provide additional detail to depth imaging velocity models. The reciprocal consistency of the obtained results show promises for extending the work to more analytical integration with seismic such as provided by joint geophysical inversion.

3-D joint inversion of seismic waveform and airborne gravity gradiometry data

2020 ◽  
Vol 223 (2) ◽  
pp. 746-764
Author(s):  
Wenbin Jiang
Keyword(s):  
Density Distribution ◽  
Model Building ◽  
Joint Inversion ◽  
Velocity Model ◽  
Airborne Gravity ◽  
Inversion Method ◽  
Gravity Gradiometry ◽  
Velocity Models ◽  
Seismic Waveform ◽  
Velocity Model Building

SUMMARY Seismic full waveform inversion (FWI) is a robust velocity model building technique for hydrocarbon exploration. However, the density reconstruction within the framework of multiparameter FWI leads to more degrees of freedom in the parametrization, and the sensitivity of the inversion change significantly from velocity to density, thereby increasing the ill-posedness of the inverse problem. Gravity gradiometry data inversion is an effective method for resolving density distribution. Combining gravity gradiometry data in FWI could alleviate the non-linearity of the inversion by contributing additional density information for the velocity model building. I develop a 3-D joint seismic waveform and gravity gradiometry inversion method for estimating the velocity and density distribution in the subsurface. The method alternatingly minimizes the waveform and gravity gradiometry misfit. The cross-gradient constraint is applied to enhance the structural similarity between the density and velocity models. The effectiveness of the joint inversion algorithm is demonstrated by a 3-D checkerboard model and 3-D SEAM model. Synthetic examples demonstrate that the joint inversion can improve the image quality in geologically complex areas. A case study from the South China Sea shows that the joint inversion improves the velocity and density solutions compared to a standalone seismic FWI. The joint inversion results are consistent with the pre-stack depth migration section and the shape of the salt body is well resolved.

Practical approaches for subsalt velocity model building

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE183-VE194 ◽  
Author(s):  
Junru Jiao ◽  
David R. Lowrey ◽  
John F. Willis ◽  
Ruben D. Martínez
Keyword(s):  
Software Package ◽  
Model Building ◽  
Velocity Model ◽  
Fine Tuning ◽  
Inherent Difficulty ◽  
Velocity Models ◽  
First Approximation ◽  
Velocity Model Building ◽  
Trade Offs ◽  
Efficient Performance

Imaging sediments below salt bodies is challenging because of the inherent difficulty of estimating accurate velocity models. These models can be estimated in a variety of ways with varying degrees of expense and effectiveness. Two methods are commercially viable trade-offs. In the first method, residual-moveout analysis is performed in a layer-stripping mode. The models produced with this method can be used as a first approximation of the subsalt velocity field. A wave-equation migration scanning technique is more suitable for fine-tuning the velocity model below the salt. Both methods can be run as part of a sophisticated interactive velocity interpretation software package that makes velocity interpretation efficient. Performance of these methods has been tested on synthetic and field data examples.

Full-waveform inversion for full-wavefield imaging: Decades in the making

2021 ◽  
Vol 40 (5) ◽  
pp. 324-334
Author(s):  
Rongxin Huang ◽  
Zhigang Zhang ◽  
Zedong Wu ◽  
Zhiyuan Wei ◽  
Jiawei Mei ◽  
...  
Keyword(s):  
Model Building ◽  
Seismic Imaging ◽  
Structural Information ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Data Sets ◽  
Full Waveform Inversion ◽  
Data Types ◽  
Velocity Models ◽  
Full Waveform

Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward-modeling process to generate full-wavefield data, an inversion scheme to minimize the difference between modeled and recorded data, and, more importantly, an accurate velocity model to correctly propagate and collapse energy of different wave modes. All of these elements have been embedded in the framework of full-waveform inversion (FWI) since it was proposed three decades ago. However, for a long time, the application of FWI did not find its way into the domain of full-wavefield imaging, mostly owing to the lack of data sets with good constraints to ensure the convergence of inversion, the required compute power to handle large data sets and extend the inversion frequency to the bandwidth needed for imaging, and, most significantly, stable FWI algorithms that could work with different data types in different geologic settings. Recently, with the advancement of high-performance computing and progress in FWI algorithms at tackling issues such as cycle skipping and amplitude mismatch, FWI has found success using different data types in a variety of geologic settings, providing some of the most accurate velocity models for generating significantly improved migration images. Here, we take a step further to modify the FWI workflow to output the subsurface image or reflectivity directly, potentially eliminating the need to go through the time-consuming conventional seismic imaging process that involves preprocessing, velocity model building, and migration. Compared with a conventional migration image, the reflectivity image directly output from FWI often provides additional structural information with better illumination and higher signal-to-noise ratio naturally as a result of many iterations of least-squares fitting of the full-wavefield data.

scholarly journals Moving from 1-D to 3-D velocity model: automated waveform-based earthquake moment tensor inversion in the Los Angeles region

2019 ◽  
Vol 220 (1) ◽  
pp. 218-234 ◽  
Author(s):  
Xin Wang ◽  
Zhongwen Zhan
Keyword(s):  
Los Angeles ◽  
Moment Tensor ◽  
Velocity Model ◽  
Primary Control ◽  
Lateral Velocity ◽  
Real Time System ◽  
Moment Tensors ◽  
Velocity Models ◽  
Double Couple ◽  
The Moment

SUMMARY Earthquake focal mechanisms put primary control on the distribution of ground motion, and also bear on the stress state of the crust. Most routine focal mechanism catalogues still use 1-D velocity models in inversions, which may introduce large uncertainties in regions with strong lateral velocity heterogeneities. In this study, we develop an automated waveform-based inversion approach to determine the moment tensors of small-to-medium-sized earthquakes using 3-D velocity models. We apply our approach in the Los Angeles region to produce a new moment tensor catalogue with a completeness of ML ≥ 3.5. The inversions using the Southern California Earthquake Center Community Velocity Model (3D CVM-S4.26) significantly reduces the moment tensor uncertainties, mainly owing to the accuracy of the 3-D velocity model in predicting both the phases and the amplitudes of the observed seismograms. By comparing the full moment tensor solutions obtained using 1-D and 3-D velocity models, we show that the percentages of non-double-couple components decrease dramatically with the usage of 3-D velocity model, suggesting that large fractions of non-double-couple components from 1-D inversions are artifacts caused by unmodelled 3-D velocity structures. The new catalogue also features more accurate focal depths and moment magnitudes. Our highly accurate, efficient and automatic inversion approach can be expanded in other regions, and can be easily implemented in near real-time system.

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

scholarly journals Constraining fault interpretation through tomographic velocity gradients: application to northern Cascadia

Solid Earth ◽  
2012 ◽  
Vol 3 (1) ◽  
pp. 53-61 ◽  
Author(s):  
K. Ramachandran
Keyword(s):  
Three Dimensional ◽  
Velocity Model ◽  
Absolute Velocity ◽  
Tomographic Inversion ◽  
Spatial Gradients ◽  
Velocity Models ◽  
Final Velocity ◽  
Velocity Gradients ◽  
Spatial Velocity ◽  
Perturbation Velocity

Abstract. Spatial gradients of tomographic velocities are seldom used in interpretation of subsurface fault structures. This study shows that spatial velocity gradients can be used effectively in identifying subsurface discontinuities in the horizontal and vertical directions. Three-dimensional velocity models constructed through tomographic inversion of active source and/or earthquake traveltime data are generally built from an initial 1-D velocity model that varies only with depth. Regularized tomographic inversion algorithms impose constraints on the roughness of the model that help to stabilize the inversion process. Final velocity models obtained from regularized tomographic inversions have smooth three-dimensional structures that are required by the data. Final velocity models are usually analyzed and interpreted either as a perturbation velocity model or as an absolute velocity model. Compared to perturbation velocity model, absolute velocity models have an advantage of providing constraints on lithology. Both velocity models lack the ability to provide sharp constraints on subsurface faults. An interpretational approach utilizing spatial velocity gradients applied to northern Cascadia shows that subsurface faults that are not clearly interpretable from velocity model plots can be identified by sharp contrasts in velocity gradient plots. This interpretation resulted in inferring the locations of the Tacoma, Seattle, Southern Whidbey Island, and Darrington Devil's Mountain faults much more clearly. The Coast Range Boundary fault, previously hypothesized on the basis of sedimentological and tectonic observations, is inferred clearly from the gradient plots. Many of the fault locations imaged from gradient data correlate with earthquake hypocenters, indicating their seismogenic nature.

PS Energy Imaging Condition for Microseismic Data - Part 1: Theory and Applications in 3D Isotropic Media

Geophysics ◽  
2020 ◽  
pp. 1-79
Author(s):  
Can Oren ◽  
Jeffrey Shragge
Keyword(s):  
Wave Velocity ◽  
Model Building ◽  
Velocity Model ◽  
Microseismic Monitoring ◽  
Elastic Model ◽  
S Wave ◽  
Imaging Condition ◽  
Velocity Models ◽  
Monitoring Programs ◽  
Imaging Conditions

Accurately estimating event locations is of significant importance in microseismic investigations because this information greatly contributes to the overall success of hydraulic fracturing monitoring programs. Full-wavefield time-reverse imaging (TRI) using one or more wave-equation imaging conditions offers an effective methodology for locating surface-recorded microseismic events. To be most beneficial in microseismic monitoring programs, though, the TRI procedure requires using accurate subsurface models that account for elastic media effects. We develop a novel microseismic (extended) PS energy imaging condition that explicitly incorporates the stiffness tensor and exhibits heightened sensitivity to isotropic elastic model perturbations compared to existing imaging conditions. Numerical experiments demonstrate the sensitivity of microseismic TRI results to perturbations in P- and S-wave velocity models. Zero-lag and extended microseismic source images computed at selected subsurface locations yields useful information about 3D P- and S-wave velocity model accuracy. Thus, we assert that these image volumes potentially can serve as the input into microseismic elastic velocity model building algorithms.

Diffraction imaging and depth-velocity inversion with 3D P-Cable seismic data

2021 ◽  
Author(s):  
Alexander Bauer ◽  
Benjamin Schwarz ◽  
Dirk Gajewski
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Computational Cost ◽  
Velocity Model ◽  
Data Cube ◽  
Velocity Inversion ◽  
Velocity Models ◽  
North East ◽  
3D Velocity Model ◽  
Zero Offset

<p>Most established methods for the estimation of subsurface velocity models rely on the measurements of reflected or diving waves and therefore require data with sufficiently large source-receiver offsets. For seismic data that lacks these offsets, such as vintage data, low-fold academic data or near zero-offset P-Cable data, these methods fail. Building on recent studies, we apply a workflow that exploits the diffracted wavefield for depth-velocity-model building. This workflow consists of three principal steps: (1) revealing the diffracted wavefield by modeling and adaptively subtracting reflections from the raw data, (2) characterizing the diffractions with physically meaningful wavefront attributes, (3) estimating depth-velocity models with wavefront tomography. We propose a hybrid 2D/3D approach, in which we apply the well-established and automated 2D workflow to numerous inlines of a high-resolution 3D P-Cable dataset acquired near Ritter Island, a small volcanic island located north-east of New Guinea known for a catastrophic flank collapse in 1888. We use the obtained set of parallel 2D velocity models to interpolate a 3D velocity model for the whole data cube, thus overcoming possible issues such as varying data quality in inline and crossline direction and the high computational cost of 3D data analysis. Even though the 2D workflow may suffer from out-of-plane effects, we obtain a smooth 3D velocity model that is consistent with the data.</p>

Seismic Velocity Model Building Using Neural Networks: Training Data Design and Learning Generalization

Geophysics ◽  
2021 ◽  
pp. 1-73
Author(s):  
Hani Alzahrani ◽  
Jeffrey Shragge
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Training Data ◽  
Training Process ◽  
Training Models ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Layer Velocity

Data-driven artificial neural networks (ANNs) offer a number of advantages over conventional deterministic methods in a wide range of geophysical problems. For seismic velocity model building, judiciously trained ANNs offer the possibility of estimating high-resolution subsurface velocity models. However, a significant challenge of ANNs is training generalization, which is the ability of an ANN to apply the learning from the training process to test data not previously encountered. In the context of velocity model building, this means learning the relationship between velocity models and the corresponding seismic data from a set of training data, and then using acquired seismic data to accurately estimate unknown velocity models. We ask the following question: what type of velocity model structures need be included in the training process so that the trained ANN can invert seismic data from a different (hypothetical) geological setting? To address this question, we create four sets of training models: geologically inspired and purely geometrical, both with and without background velocity gradients. We find that using geologically inspired training data produce models with well-delineated layer interfaces and fewer intra-layer velocity variations. The absence of a certain geological structure in training models, though, hinders the ANN's ability to recover it in the testing data. We use purely geometric training models consisting of square blocks of varying size to demonstrate the ability of ANNs to recover reasonable approximations of flat, dipping, and curved interfaces. However, the predicted models suffer from intra-layer velocity variations and non-physical artifacts. Overall, the results successfully demonstrate the use of ANNs in recovering accurate velocity model estimates, and highlight the possibility of using such an approach for the generalized seismic velocity inversion problem.

Earthquake locations by 3-D finite-difference travel times

1990 ◽  
Vol 80 (2) ◽  
pp. 395-410 ◽  
Author(s):  
Glenn D. Nelson ◽  
John E. Vidale
Keyword(s):  
Travel Time ◽  
Velocity Structure ◽  
Three Dimensional ◽  
Computation Time ◽  
Velocity Model ◽  
Model Complexity ◽  
Grid Spacing ◽  
Origin Time ◽  
Velocity Models ◽  
Fault Trace

Abstract We present a new method for locating earthquakes in a region with arbitrarily complex three-dimensional velocity structure, called QUAKE3D. Our method searches a gridded volume and finds the global minimum travel-time residual location within the volume. Any minimization criterion may be employed. The L1 criterion, which minimizes the sum of the absolute values of travel-time residuals, is especially useful when the station coverage is sparse and is more robust than the L2 criterion (which minimizes the RMS sum) employed by most earthquake location programs. On a UNIX workstation with 8 Mbytes memory, travel-time grids of size 150 by 150 by 50 are reasonably employed, with the actual geographic coverage dependent on the grid spacing. Location precision is finer than the grid spacing. Earthquake recordings at six stations in Bear Valley are located as an example, using various layered and laterally varying velocity models. Locations with QUAKE3D are nearly identical to HYPOINVERSE locations when the same flat-layered velocity model is used. For the examples presented, the computation time per event is approximately 4 times slower than HYPOINVERSE, but the computation time for QUAKE3D is dependent only on the grid size and number of stations, and independent of the velocity model complexity. Using QUAKE3D with a laterally varying velocity model results in locations that are physically more plausible and statistically more precise. Compared to flat-layered solutions, the earthquakes are more closely aligned with the surface fault trace, are more uniform in depth distribution, and the event and station travel-time residuals are much smaller. Hypocentral error bars computed by QUAKE3D are more realistic in that the trade-off of depth versus origin time is implicit in our error estimation, but ignored by HYPOINVERSE.

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