Joint inversion of seismic traveltimes and gravity data on unstructured grids with application to mineral exploration

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. K1-K15 ◽  
Author(s):  
Peter G. Lelièvre ◽  
Colin G. Farquharson ◽  
Charles A. Hurich
Keyword(s):  
Seismic Data ◽  
Unstructured Grids ◽  
Joint Inversion ◽  
Mineral Exploration ◽  
Gravity Data ◽  
Physical Property ◽  
Sulfide Deposit ◽  
Earth Model ◽  
Data Set ◽  
First Arrival

Seismic methods continue to receive interest for use in mineral exploration due to the much higher resolution potential of seismic data compared to the techniques traditionally used, namely, gravity, magnetics, resistivity, and electromagnetics. However, the complicated geology often encountered in hard-rock exploration can make data processing and interpretation difficult. Inverting seismic data jointly with a complementary data set can help overcome these difficulties and facilitate the construction of a common earth model. We considered the joint inversion of seismic first-arrival traveltimes and gravity data to recover causative slowness and density distributions. Our joint inversion algorithm differs from previous work by (1) incorporating a large suite of measures for coupling the two physical property models, (2) slowly increasing the effect of the coupling to help avoid potential convergence issues, and (3) automatically adjusting two Tikhonov tradeoff parameters to achieve a desired fit to both data sets. The coupling measures used are both compositional and structural in nature and allow the inclusion of explicitly known or implicitly assumed empirical relationships, physical property distribution information, and cross-gradient structural coupling. For any particular exploration scenario, the combination of coupling measures used should be guided by the geologic knowledge available. We performed our inversions on unstructured grids comprised of triangular cells in 2D, or tetrahedral cells in 3D, but the joint inversion methods are equally applicable to rectilinear grids. We tested our joint inversion methodology on scenarios based on the Voisey’s Bay massive sulfide deposit in Labrador, Canada. These scenarios present a challenge to the inversion of first-arrival traveltimes and we show how joint inversion with gravity data can improve recovery of the subsurface features.

Joint inversion of potential-fields data over the DO-27 kimberlite pipe using a Gaussian mixture model prior

2020 ◽  
Vol 8 (4) ◽  
pp. SS47-SS62
Author(s):  
Thibaut Astic ◽  
Dominique Fournier ◽  
Douglas W. Oldenburg
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Joint Inversion ◽  
Gravity Data ◽  
Physical Property ◽  
Gaussian Mixture ◽  
Kimberlite Pipe ◽  
Magnetic Data ◽  
High Porosity ◽  
Rock Types

We have carried out petrophysically and geologically guided inversions (PGIs) to jointly invert airborne and ground-based gravity data and airborne magnetic data to recover a quasi-geology model of the DO-27 kimberlite pipe in the Tli Kwi Cho (also referred to as TKC) cluster. DO-27 is composed of three main kimberlite rock types in contact with each other and embedded in a granitic host rock covered by a thin layer of glacial till. The pyroclastic kimberlite (PK), which is diamondiferous, and the volcanoclastic kimberlite (VK) have anomalously low density, due to their high porosity, and weak magnetic susceptibility. They are indistinguishable from each other based upon their potential-field responses. The hypabyssal kimberlite (HK), which is not diamondiferous, has been identified as highly magnetic and remanent. Quantitative petrophysical signatures for each rock unit are obtained from sample measurements, such as the increasing density of the PK/VK unit with depth and the remanent magnetization of the HK unit, and are represented as a Gaussian mixture model (GMM). This GMM guides the PGI toward generating a 3D quasi-geology model with physical properties that satisfies the geophysical data sets and the petrophysical signatures. Density and magnetization models recovered individually yield volumes that have physical property combinations that do not conform to any known petrophysical characteristics of the rocks in the area. A multiphysics PGI addresses this problem by using the GMM as a coupling term, but it puts a volume of the PK/VK unit at a location that is incompatible with geologic information from drillholes. To conform to that geologic knowledge, a fourth unit is introduced, PK-minor, which is petrophysically and geographically distinct from the main PK/VK unit. This inversion produces a quasi-geology model that presents good structural locations of the diamondiferous PK unit and can be used to provide a resource estimate or decide the locations of future drillholes.

Case Study: Improving the quality of the seismic reflection image for a Fujian mineral exploration data set with offset-domain common-image gathers

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Guofeng Liu ◽  
Xiaohong Meng ◽  
Johanes Gedo Sea
Keyword(s):  
Wave Equation ◽  
Image Quality ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Mineral Exploration ◽  
Velocity Model ◽  
Image Point ◽  
Single Shot ◽  
Data Set ◽  
Depth Migration

Seismic reflection is a proven and effective method commonly used during the exploration of deep mineral deposits in Fujian, China. In seismic data processing, rugged depth migration based on wave-equation migration can play a key role in handling surface fluctuations and complex underground structures. Because wave-equation migration in the shot domain cannot output offset-domain common-image gathers in a straightforward way, the use of traditional tools for updating the velocity model and improving image quality can be quite challenging. To overcome this problem, we employed the attribute migration method. This worked by sorting the migrated stack results for every single-shot gather into the offset gathers. The value of the offset that corresponded to each image point was obtained from the ratio of the original migration results to the offset-modulated shot-data migration results. A Gaussian function was proposed to map every image point to a certain range of offsets. This helped improve the signal-to-noise ratio, which was especially important in handing low quality seismic data obtained during mineral exploration. Residual velocity analysis was applied to these gathers to update the velocity model and improve image quality. The offset-domain common-image gathers were also used directly for real mineral exploration seismic data with rugged depth migration. After several iterations of migration and updating the velocity, the proposed procedure achieved an image quality better than the one obtained with the initial velocity model. The results can help with the interpretation of thrust faults and deep deposit exploration.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

Completed in Spring 2020: AAGRG´s new recompilation of the Alpine gravity field

2020 ◽  
Author(s):  
Hans-Jürgen Götze ◽  
Keyword(s):  
Reference Frames ◽  
Joint Inversion ◽  
Gravity Data ◽  
Bouguer Anomaly ◽  
Interdisciplinary Studies ◽  
Digital Data ◽  
Long Distance ◽  
Data Set ◽  
Homogeneous Surface ◽  
The Alps

<p>The AlpArray gravity research group (AAGRG) focuses on compiling a homogeneous surface-based gravity dataset across the Alpine area, on creating digital data sets for Bouguer-, Free Air- and isostatic anomalies. In 2016/17 all ten countries around the Alps have agreed to contribute with point/gridded gravity data and/or gravity data processing techniques to recompilation of the Alpine gravity in an area from 2° East to 23° East and 50° North to 42° North. For this recompilation, the group was able to rely on existing national data. For the Ivrea zone in the western Alps, newly surveyed data were also integrated into the database.</p><p>The AAGRG decided to present the data set of the recalculated gravity fields on a 2 km x 2 km and 4 km x 4 km grid for the public. The final products will also include the calculated values for mass corrections of the measured gravity at each grid point. This allows users to use later customized densities for their own calculations of mass corrections between the physical surface and the ellipsoidal reference. The densities used are 2 670 kg/m<sup>3</sup> for landmasses, 1 030 kg/m<sup>3</sup> for water masses above and  -1 640 kg/m<sup>3</sup> below the ellipsoid. The correction radius was set to the Hayford zone O2 (167 km). In the future, the calculation of long-distance effects of topography/bathymetry and its compensating masses (roots) are planned. The new Bouguer anomaly will be station completed (CBA) and compiled according to the most modern criteria and reference frames (both location and gravity). The concept of ellipsoidal heights implicitly includes the geophysical indirect effect. Atmospheric corrections are also considered. Special emphasis was put on the numerous lakes in the study area. They partly have a considerable effect on the gravity of stations that lie at their edges (for example, the rather deep reservoirs in the Alps). In the Ligurian and the Adriatic seas, ship data of the Bureau Gravimétrique International were used. Although not unproblematic, these data got the preference over satellite data.</p><p> It is the aim of the work of the AAGRG to release a gravity database that can be used for high-resolution modeling, interdisciplinary studies from local to regional to continental scales, as well as for joint inversion with other datasets.</p>

The structural architecture of the Whataroa Valley at the Alpine Fault (New Zealand) from first-arrival tomography and reflection imaging using an extended 3D VSP survey

2020 ◽  
Author(s):  
Vera Lay ◽  
Stefan Buske ◽  
Sascha Barbara Bodenburg ◽  
Franz Kleine ◽  
John Townend ◽  
...  
Keyword(s):  
New Zealand ◽  
Seismic Data ◽  
Average Velocity ◽  
Velocity Model ◽  
P Wave ◽  
3D Seismic ◽  
Data Set ◽  
Alpine Fault ◽  
P Wave Velocity ◽  
First Arrival

<p>The Alpine Fault along the West Coast of the South Island (New Zealand) is a major plate boundary that is expected to rupture in the next 50 years, likely as a magnitude 8 earthquake. The Deep Fault Drilling Project (DFDP) aims to deliver insight into the geological structure of this fault zone and its evolution by drilling and sampling the Alpine Fault at depth.  </p><p>Here we present results from a 3D seismic survey around the DFDP-2 drill site in the Whataroa Valley where the drillhole penetrated almost down to the fault surface. Within the glacial valley, we collected 3D seismic data to constrain valley structures that were obscured in previous 2D seismic data. The new data consist of a 3D extended vertical seismic profiling (VSP) survey using three-component receivers and a fibre optic cable in the DFDP-2B borehole as well as a variety of receivers at the surface.</p><p>The data set enables us to derive a reliable 3D P-wave velocity model by first-arrival travel time tomography. We identify a 100-460 m thick sediment layer (average velocity 2200±400 m/s) above the basement (average velocity 4200±500 m/s). Particularly on the western valley side, a region of high velocities steeply rises to the surface and mimics the topography. We interpret this to be the infilled flank of the glacial valley that has been eroded into the basement. In general, the 3D structures implied by the velocity model on the upthrown (Pacific Plate) side of the Alpine Fault correlate well with the surface topography and borehole findings.</p><p>A reliable velocity model is not only valuable by itself but it is also required as input for prestack depth migration (PSDM). We performed PSDM with a part of the 3D data set to derive a structural image of the subsurface within the Whataroa Valley. The top of the basement identified in the P-wave velocity model coincides well with reflectors in the migrated images so that we can analyse the geometry of the basement in detail.</p>

scholarly journals Advanced Methods of Joint Inversion of Multiphysics Data for Mineral Exploration

Geosciences ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 262
Author(s):  
Michael S. Zhdanov ◽  
Michael Jorgensen ◽  
Leif Cox
Keyword(s):  
Joint Inversion ◽  
Mineral Exploration ◽  
A Priori ◽  
Geophysical Methods ◽  
Physical Property ◽  
Model Parameters ◽  
Time Inversion ◽  
Structural Constraints ◽  
Considerable Uncertainty ◽  
Additional Information

Different geophysical methods provide information about various physical properties of rock formations and mineralization. In many cases, this information is mutually complementary. At the same time, inversion of the data for a particular survey is subject to considerable uncertainty and ambiguity as to causative body geometry and intrinsic physical property contrast. One productive approach to reducing uncertainty is to jointly invert several types of data. Non-uniqueness can also be reduced by incorporating additional information derived from available geological and/or geophysical data in the survey area to reduce the searching space for the solution. This additional information can be incorporated in the form of a joint inversion of multiphysics data. This paper presents an overview of the main ideas and principles of novel methods of joint inversion, developed over the last decade, which do not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. These approaches are designated as follows: (1) Gramian constraints; (2) Gramian-based structural constraints; (3) localized Gramian constraints; and (4) joint focusing constraints. We provide a short description of the mathematical foundations of each of these approaches and discuss the practical aspects of their applications in mineral exploration.

Multiple level-set joint inversion of traveltime and gravity data with application to ore delineation: A synthetic study

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. R13-R30 ◽  
Author(s):  
Polina Zheglova ◽  
Peter G. Lelièvre ◽  
Colin G. Farquharson
Keyword(s):  
Physical Properties ◽  
Level Set Method ◽  
Level Set ◽  
Joint Inversion ◽  
Gravity Data ◽  
Level Set Methods ◽  
Physical Property ◽  
Magnetic Data ◽  
Inversion Method ◽  
Multiple Level

We have developed a multiple level-set method for simultaneous inversion of gravity and seismic traveltime data. The method recovers the boundaries between regions with distinct physical properties assumed constant and known, creating structurally consistent models of two subsurface properties: P-wave velocity and density. In single level-set methods, only two rock units can be considered: background and inclusion. Such methods have been applied to examples representing various geophysical scenarios, including in the context of joint inversion. In multiple level-set methods, several units can be considered, which make them far more applicable to real earth scenarios. Recently, a multiple level-set method has been proposed for inversion of magnetic data. We extend the multiple level-set formulation to joint inversion of gravity and traveltime data, improving upon previous work, and we investigate applicability of such an inversion method in ore delineation. In mineral exploration environments, traditional seismic imaging and inversion methods are challenging because of the small target size and the specific physical property contrasts involved. First-arrival seismic traveltime and gravity data complement each other, and we found that joint multiple level-set inversion is more beneficial than separate inversions, especially with limited data and slow targets. Our method is more robust than the joint inversion method based on clustering of physical properties in recovery of piecewise homogeneous models not well-constrained by the data. To justify the known property assumption, we found the degree of robustness of the multiple level-set joint inversion to uncertainties arising from incomplete knowledge of small-scale subsurface physical property variations and target composition.

scholarly journals Joint Inversion of 2D Gravity Gradiometry and Magnetotelluric Data in Mineral Exploration

Minerals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 541 ◽  
Author(s):  
Rongzhe Zhang ◽  
Tonglin Li
Keyword(s):  
2D Gravity ◽  
Joint Inversion ◽  
Mineral Exploration ◽  
Gravity Data ◽  
Computation Time ◽  
Iteration Algorithm ◽  
Imaging Method ◽  
Gravity Gradiometry ◽  
Magnetotelluric Data ◽  
Data Space

We have developed a mineral exploration method for the joint inversion of 2D gravity gradiometry and magnetotelluric (MT) data based on data-space and normalized cross-gradient constraints. To accurately explore the underground structure of complex mineral deposits and solve the problems such as the non-uniqueness and inconsistency of the single parameter inversion model, it is now common practice to perform collocated MT and gravity surveys that complement each other in the search. Although conventional joint inversion of MT and gravity using model-space can be diagnostic, we posit that better results can be derived from the joint inversion of the MT and gravity gradiometry data using data-space. Gravity gradiometry data contains more abundant component information than traditional gravity data and can be used to classify the spatial structure and location of underground structures and field sources more accurately and finely, and the data-space method consumes less memory and has a shorter computation time for our particular inversion iteration algorithm. We verify our proposed method with synthetic models. The experimental results prove that our proposed method leads to models with remarkable structural resemblance and improved estimates of electrical resistivity and density and requires shorter computation time and less memory. We also apply the method to field data to test its potential use for subsurface lithofacies discrimination or structural classification. Our results suggest that the imaging method leads to improved characterization of geological targets, which is more conducive to geological interpretation and the exploration of mineral resources.

First-arrival picking with a U-net convolutional network

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. U45-U57 ◽  
Author(s):  
Lianlian Hu ◽  
Xiaodong Zheng ◽  
Yanting Duan ◽  
Xinfei Yan ◽  
Ying Hu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Synthetic Data ◽  
Training Data ◽  
Convolutional Network ◽  
Data Set ◽  
Waveform Data ◽  
Near Surface ◽  
Arrival Times ◽  
Seismic Waveform ◽  
First Arrival

In exploration geophysics, the first arrivals on data acquired under complicated near-surface conditions are often characterized by significant static corrections, weak energy, low signal-to-noise ratio, and dramatic phase change, and they are difficult to pick accurately with traditional automatic procedures. We have approached this problem by using a U-shaped fully convolutional network (U-net) to first-arrival picking, which is formulated as a binary segmentation problem. U-net has the ability to recognize inherent patterns of the first arrivals by combining attributes of arrivals in space and time on data of varying quality. An effective workflow based on U-net is presented for fast and accurate picking. A set of seismic waveform data and their corresponding first-arrival times are used to train the network in a supervised learning approach, then the trained model is used to detect the first arrivals for other seismic data. Our method is applied on one synthetic data set and three field data sets of low quality to identify the first arrivals. Results indicate that U-net only needs a few annotated samples for learning and is able to efficiently detect first-arrival times with high precision on complicated seismic data from a large survey. With the increasing training data of various first arrivals, a trained U-net has the potential to directly identify the first arrivals on new seismic data.

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