Chapter 4
A multidimensional framework for basin evaluation

Subsidence of continental crust and the formation of sedimentary basins is most commonly caused by plate boundary processes, such as continental extension and rifting, or flexural loading in compressional regimes. However, many large intracontinental basins subside slowly over long time intervals, showing a “saucer-shaped” geometry in cross sections, even though they are located far away from active plate boundaries. These basins do not show signs of extensive brittle deformation and they subside long after initial thermal disturbances. Conventional basin models fail to explain the additional accumulation of sediments in those basins and hence this phenomenon is commonly referred to as “anomalous subsidence”. I use a combination of openly available global data sets to create a framework for basin evaluation and try to parametrise the subsidence history of a global set of more than 220 intracontinental basins. One of the key parameters is an assessment of sediment thickness versus crustal thinning observed underneath a given basin to derive alternative total tectonic subsidence estimates, leading to a quantified evaluation of “anomalous tectonic subsidence”. Uncertainties in this approach are assessed by comparing four alternative sediment thickness and two crustal thickness data sets for Australia. I demonstrate that large-scale crustal structure data from global and regional datasets allows the computation of a robust anomalous total tectonic subsidence estimate, which can also be expressed as a “differential extension factor”, based on the relationships between sediment and crustal thickness. I use the global TC1 thermal lithospheric thickness data (Artemieva, 2006) to calculate the residual lithospheric extension (RLE) to further evaluate the degree of lithospheric vs. crustal extension. The computed subsidence/extension factor anomalies can then be used in comparison with predictions of different basin-forming models, used in basin modeling studies and provide a base to further investigate the causes of intracontinental basin subsidence.

 4.1 Introduction
  4.1.1 Basin forming mechanisms
 4.2 Global basin compilation
 4.3 Global lithosphere and crustal data sets
  4.3.1 ETOPO2
  4.3.2 CRUST2
  4.3.3 Laske’s sediment thickness
  4.3.4 Mobil isopach data
  4.3.5 TC1 thermal lithospheric thickness
  4.3.6 Australia - Regional crustal structure grids
 4.4 Methodology and workflow
  4.4.1 Software infrastructure
  4.4.2 Workflow
 4.5 Computations
  4.5.1 Extension factor β and total tectonic subsidence
  4.5.2 Grids calculated from crustal thickness models
  4.5.3 Grids derived from sediment thickness
  4.5.4 Mantle lid thickness and predicted elevation
  4.5.5 Crustal thickness and topography
 4.6 Global results
  4.6.1 Differential beta
  4.6.2 Anomalous tectonic subsidence
  4.6.3 Residual lithospheric extension
 4.7 Discussion

4.1 Introduction

Large intracontinental basins have often been subsiding and accumulating sediments over geologically long periods (hundreds of m.y.) without showing signs of active extension (i.e. brittle faulting) and long after the initial stretching phase or thermal perturbation. These basins are often classified as “thermal sag basins” and their typical cross-sectional geometry is “saucer-shaped” without showing a typical central rift (Norton and Johnson, 2001; Klein, 1995; Ziegler, 1992a; Hartley and Allan, 1994). They are floored with continental basement and mostly overlie broad fossil rift structures (Klein, 1995; Şengör, 1995). It is common that these basins contain more sediments than can be accounted for using conventional basin modeling approaches (Norton and Johnson, 2001). Up to eleven different mechanisms are listed in the work of Klein (1995) attempting to explain the cause and longevity of the subsidence in these basins. Whereas these models sufficiently explain the subsidence of one single basin or a regional group of basins of similar tectonic age, at present no evolutionary model sufficiently explains the observed subsidence patterns in intracontinental basins on a global scale.

In this chapter, crustal and lithospheric information on intracontinental basins is compiled using freely available global and regional data sets and open-source software tools. It is attempted to first provide an account of the general characteristics of these basins in terms of crustal and lithospheric structures and to test, whether it is possible to derive a set of parameters which readily allows to distinguish basins with an anomalous subsidence component. Secondly, the crustal structure of these basins is analysed and the most characteristic parameters are isolated. A third aim of this “global approach” to basin analysis is to generate a set of parameters and semantics which can be utilised for a new generation of fully geodynamic models integrating deep earth and lithospheric processes on regional and global scale within a plate kinematic framework.

I have attempted to design workflows which enable direct links from crustal or lithospheric structure data to future geodynamic modeling packages, either to define a modeling “target” or starting point, based on a case of averaged lithospheric parameters from a large set of observations. With subsequent improvements regarding the data base used for this study, models and computations will become more accurate and reliable. The results of this study will be made available online through the EarthByte portal (http://www.earthbyte.org) for the scientific community.

4.1.1 Basin forming mechanisms

Although geodynamic modeling has advanced significantly from the simple, yet elegant, uniform stretching model of McKenzie (1978), the causes of anomalous subsidence in intracontinental basins still remain enigmatic (e.g. Artyushkov, 1992; Hartley and Allan, 1994; Klein, 1995; Norton and Johnson, 2001; Allen and Allen, 2005). Much progress has been made over the last couple of decades to understand the geodynamics of “successful” rifting and continental break-up and related basin formation, partly because of a much better imaging of the related structures for example through large scale marine seismic surveys. In contrast the fossil, old continental rifts and broad intracontinental basins did not receive an equivalent level of attention. With the advent of global and regional-scale crustal data sets becoming available for public use at no cost (Mooney et al., 1998; Laske, 2004; Artemieva, 2006) and advances in seismic tomography, it is now possible to investigate the formation and evolution of large intracontinental basins at a global scale.

Mechanisms driving the formation of sedimentary basins are directly related to the changing nature of the Earth’s surface due to constant re-shaping by plate tectonic processes. Three main mechanisms cause large-scale subsidence of the Earth’s surface (Allen and Allen, 2005):

1. isostatic processes, such as crustal thinning,
2. flexural loading,
3. dynamic effects, such as mantle convection-induced dynamic topography

As it is beyond the scope of this thesis to review extension and rift models in detail, only a brief overview about formation models for intracontinental rift basins will be given. Generally, the formation of intracontinental sedimentary basins falls into an evolutionary sequence of basins formed by lithospheric extension, either through active or passive rifting (Allen and Allen, 2005; Şengör, 1995; Ziegler, 1992b; Bally, 1980; Bally and Snelson, 1980; Şengör and Burke, 1978). Nearly all of the basins which form part of this study, overlie fossil rift structures of varying age (Şengör, 1995). This heterogeneous group in terms of basin formation age and basin substrate can be differentiated into three sub-categories with various degree of overlap (for a detailed overview see Şengör, 1995):

1. broad passive margins, continental platforms, embankments and narrow, elongate depressions (Arabia, “Aulacogens” of Eastern Europe),
2. broad clusters (taphrogens, according to Şengör, 1995) of fossil rifts (e.g. West Siberian Basin, Patagonian Basins, North Sea, Chinese basins, Australian basins), and
3. circular-shaped depocentres likely overlying more localised fossil rift arms (Michigan, Illinois Basins, most of the intracratonic African Basins, like Taoudeni, Congo).

The commonality of all basins in this study is, that they form low-lying depocentres which show a history of slow subsidence of long geological times. They generally lack extensional structures or show little signs of brittle deformation which can not account for the observed subsidence (Artyushkov, 1992). Ingersoll and Busby (1995) introduced the concept of “preservation potential” in which basins are assessed according to the lifespan of the sedimentary accumulation and the amount of time the basins will not be tectonically destroyed. In the case of the basins analysed in here, they represent the highest preservation potential and a lifespan of sediment accumulation of hundreds of million years (Fig 4.1).

In his “remarks on the development of sedimentary basins”, McKenzie (1978) provides a simple but very effective model to explain the subsidence of rift basins and the subsequent thermal decay following uniform, instantaneous stretching. The total amount of subsidence in an extensional basin is composed of an initial, instantaneous fault controlled subsidence, which is dependant on the initial thickness and the amount of stretching, and a subsequent thermal subsidence (McKenzie, 1978; Allen and Allen, 2005). The rate of thermal subsidence decreases exponentially through time to 1∕e of the original value after ≈ 50 Ma, hence the dependency of the heat flow on the stretching factor β is negligible. For intracratonic basins, which keep on slowly subsiding over up hundreds of millions of years after the initial period of rifting, thermal subsidence is thus not a satisfying explanation for anomalous sediment thickness, unless major thermal perturbations occurred during the post-rift evolution of a basin.

Information about crustal structure, sediment and lithosphere thickness are crucial parameters to reconstruct the extension history of the basin. As the stretching of the lithosphere causes permanent thinning of the crust, the non-thermal subsidence can be extracted from the presently observed crustal thickness. Thinning of mantle lithosphere is transient, and subsidence is associated with the conductive cooling and thickening of the lithosphere after the initial rifting event (Turcotte and Schubert, 2001). A more detailed review of the different parameters associated with lithospheric extension is given in the following Section 4.5.1.

4.2 Global basin compilation

A global basin database supplied by Trond Torsvik, Center for Geodynamics, Norwegian Geological Survey (NGU) comprising more than individual 500 basin polygons acted as starting point for selecting a subset of about 250 intracontinental basins which were used for this study. These basins have been regionally subdivided in 11 larger areas (Tab. 4.1). Based on geological information from published literature, a subset of these basins (see Sec. 4.2) was selected trying to fulfill the following criteria outlined during the early phases of this study:

• The selection should encompass the West Siberian, Arabian and Australian intraplate basins;
• additional basins should located away from active plate boundaries avoiding overprinting due to lithospheric flexure and plate boundary effects and display as little as possible (recent) deformation;
• the basins should have a young continental basement which was stabilised as continental crust in accretionary processes during the Phanerozoic;
• they should contain significant Mesozoic-Cenozoic depositional sequences.

A subset of basin polygons which represented large, intracontinental basins on young accretionary lithosphere was generated. As the main objective of this study was to investigate anomalous subsidence, special emphasis was put on basins where subsidence phases have been tagged as “enigmatic” in the published literature. The number of intracontinental basins was minimised with recent (< 50 Ma) tectonic or thermal activity, like flexural loading, active rifting, or large scale brittle deformation due to large compressional or extensional stresses. As the selection process was purely subjective, it is not claimed here that this subset comprises all basins which fit the above criteria, nor that all criteria of the selection were always applied. This is the case for example for the Patagonian South American basins (PAT in Fig. 4.2) which are located relatively closely to the Chilean Trench, or the Southeast Asian basins (SUN in Fig. 4.2) which are located in one of the tectonically most active areas worldwide but provide nevertheless important insight into the geodynamics of intracontinental basins on relatively young crust.

Starting with a total of about 560 basins globally, the initial set of basins was narrowed down to about 220 basins. This was first done by briefly investigating the individual plate tectonic history of a basin and its past and present position relative to plate boundaries and deformation zones. The amount of basins was then continuously narrowed down through the course of the research in an iterative process by rigorously applying the selection criteria.

4.3 Global lithosphere and crustal data sets

This study is based on 4 freely available global crustal data sets and 2 regional ones for the Australian continent. The data is publicly accessible via download from the world wide web (see references for links to the data portals) or has been used in publications and can be requested from the corresponding authors. All data sets are in non-proprietary format, either plain-text ASCII or netCDF format (Unidata netCDF Group, 2007), which can be used in conjunction with open-source software tools.

It should be stressed, that most of the data sets used in this study represents “data models” or data compilations comprising a variety of different sources rather than processed, coherent data from a single source as it might be the case for example for the ETOPO2 topographic data. This inherently introduces uncertainties, especially in the large scale crustal data sets, which can only be properly assessed when the appropriate meta-data (sources, computational models and/or processing techniques) are provided and allow a reconstruction of how the model was assembled. Data models without such meta-data information potentially can contain large errors and have to be used with appropriate caution. The different data sets or data models will be discussed in more detail in the following sections of this chapter, here only a brief introduction is given:

1. ETOPO2 global 2’ topography from the U.S. Department of Commerce, N.O.A.A., National Geophysical Data Center, 2006). Available for download from http://www.ngdc.noaa.gov.
2. CRUST2, available for download from the Reference Earth Model (REM) web pages at http://mahi.ucsd.edu/Gabi/rem.dir/crust/crust2.html (Bassin et al., 2000; Laske, 2004).
3. Laske’s 1° sediment thickness grid, available from Gabi Laske’s web page at http://mahi.ucsd.edu/Gabi/sediment.html or through the REM website (see above).
4. Global isopach map compiled by Mobil Exploration and Producing of Dallas/TX. From The University of Texas Institute of Geophysics’ PLATES project (http://utig.ig.utexas.edu). Only available upon request.
5. The TC1 global thermal lithospheric thickness grid (Artemieva, 2006). This is available from Irina Artemieva’s web pages at http://www.lithosphere.info.

For Australia, two other regional crustal data sets were used in conjunction with the global grids for evaluating the accuracy of the global data sets on regional and local scale:

1. Geoscience Australia’s depth to basement map for sediment thickness. This is an older data compilation based on a variety of different data. An access point for this data is the Geoscience Australia website at http://www.ga.gov.au.
2. Geoscience Australia’s depth to Moho grid for crustal thickness. This data was compiled by the “Basement and Crustal Studies” Group (BCS) in a relatively recent effort.
3. FrOGTech’s OzSEEBASE grids. This data compilation is based on integrated potential field models with seismic, well and geological data. The data is freely available (http://www.frogtech.com.au) but in proprietary format requiring specialised GIS software.

For the analysis presented in this chapter, the ETOPO2, CRUST2, Geoscience Australia and FrOGTech data have been compared and evaluated. The global isopach data set by MOBIL Exploration and Production was incorporated in the project but has not been used in any computations due to large unconstrained areas in the continental interiors. The terminology to describe crustal structure elements as shown in Fig. 4.3 will be adopted for this study. In the following sections, the different data sets will be explained in more detail.

4.3.1 ETOPO2

The ETOPO2 digital elevation model (DEM; U.S. Department of Commerce, N.O.A.A., National Geophysical Data Center, 2006) is a widely used global elevation data set at 2’ resolution, combining both, topography of the continents and bathymetry of the oceans. The topographic data was used to assess the elevation of individual basins.

4.3.2 CRUST2 

The CRUST2 model is a global crustal structure model in 3D with an original grid cell resolution of 2° × 2° (Bassin et al., 2000; Laske, 2004). It is a major update to the CRUST 5.1 model of Mooney et al. (1998). The CRUST2 model uses primarily Laske’s 1×1° sediment thickness grid as sediment thickness model (see Sec. 4.3.3). For the crustal model, every grid cell is assigned a 1-D profile. Sediment thicknesses within each cell are reported to be within 1.0 km of true sediment thickness and crustal thicknesses are within 5 km of true crustal thickness (Laske, 2004). The sediment thickness model is a lower resolution version of Laske and Masters (1997).

Each individual profile in the CRUST2 model is composed of 7 layer models with the following layers: 1. bottom of ice (t0), 2. bottom of water (t1), 3. bottom of soft sediments (t3), 4. bottom of hard sediments (t4), 5. bottom of upper crust (t5), 6. bottom of middle crust (t6), and 7. bottom of lower crust (t7) = Mohorovičić Discontinuity (Moho). The model was based on statistical average of regions with similar crustal and tectonic history/setting. The different crustal layers have not been used computations in this study. Fig 4.5a shows the global basement thickness (crustal thickness without sediment layers t3 and t4, comp. Figure 4.3)

The original grid resolution is 2°×2°, with a global coverage. The model has been used for a variety of large scale and regional scale studies (e.g. Zhao et al., 2006; Zhou et al., 2006; D’Agostino and McKenzie, 1999) and is widely accepted in the scientific community. In this study, the CRUST2 model is used as reference model.

4.3.3 Laske’s sediment thickness

The 1×1° sediment thickness grid by Gabi Laske (Laske and Masters, 1997) is a compilation of sediment thickness data from various sources. These include the Exxon Tectonic Map of the World (Exxon Production Research Company, 1985), published high resolution sediment thickness maps for the oceanic regions from the US National Geophysical Data center (NGDC) and various other sources. It is probably the most accurate global sediment thickness compilation which is currently publicly available. It was compiled in order to provide more accurate crustal correction factors for global seismic and mantle tomographic models for crustal models. Figure 4.6a shows the global sediment thickness, Figure 4.6b the sediment thickness for the selected basins of this study. For sediment thickness-related computations, this grid has been used instead of the sediment thickness of the CRUST2 data set as it was compiled at a higher resolution.

4.3.4 Mobil isopach data

Originating at Mobil Exploration & Producing Technical Center the global isopach map represents a compilation of published and internal company data on sediment thickness in a number of regions globally. This dataset is part of the Univ. of Texas Institute of Geophysics’ PLATES project. It is based on 30-40 regional maps (half of them published) from which the global map was compiled. (Bill Powell & Ian O. Norton, ExxonMobil; pers. comm.). The isopachs were supplied as line data and gridded using GMT's spline-in-tension algorithm.

The Mobil isopachs were used a control data set to verify Laske’s sediment thickness data (comp. Sec.4.3.3). Figure 4.8 is a simple difference grid, generated by subtracting the Mobil isopach grid from Laske’s sediment thickness model for the selected basins. Mean values for the differential grid are -0.16 km, with a standard deviation of 1.915, indicating a slight underestimation of sediment thickness in the Laske model relative to the Mobil compilation. Minimum and maximum values are -14 km and 11.8 km, respectively. The broad scattering is most likely related to a regionally different coverage for both grids. Whereas the Mobil grid is most likely more accurate in traditional hydrocarbon provinces, Laske’s grid seems to be more accurate on a global scale and in remote regions where the Mobil data coverage has been sparse. As noted above, the report accompanying the data lists Europe, South America, Australia and Indonesia as most accurate regions. In general, the relatively small mean value indicates a good agreement of both data sets on a global scale.

4.3.5 TC1 thermal lithospheric thickness

The global thermal model TC1 for the continental upper mantle by Artemieva (2006) is a based on a previously published model of geotherms for stable continental regions (Artemieva and Mooney, 2001) and a new compilation of crustal ages, supplemented by xenolith P-T arrays and electrical conductivity profiles for cratonic regions (Artemieva, 2006). Global heat flow measurements (Pollack et al., 1993) and mantle geotherms are statistically analysed as a function of the thermo-tectonic age of continental regions. Based on the analysis, the geotherms for regions without reliable heat flow data coverage are computed.

The model uses the depth to the 1300 °C isotherm as definition for the base of the lithosphere and provides estimates for the temperature at 50 km depth, the depth to the 550 °C isotherm and the temperature gradient for the subcrustal lithosphere. The uncertainties for the lithospheric thickness are 25 %, temperatures in the upper mantle above 100 km have an uncertainty of 100 °C, and up to 150 °C below 150 km (Irena Artemieva, pers. comm.). Figure 4.9a shows the global lithospheric thickness distribution for the continental areas and for the area covered by basins used in this study (Fig. 4.9b).

4.3.6 Australia - Regional crustal structure grids

For Australia, two additional crustal structure data sets were available and have been compared to assess the accuracy of the global dataset on regional scale. Apart from the global CRUST2 data, regional Moho depth based on seismic refraction data and sediment thickness grids were available through Geoscience Australia (GA) and from FrOGTech (formerly SRK Consulting).

FrOGTech have developed the SEEBASE™ technique to provide a “Structurally Enhanced view of the Economic BASEment” modeling potential field data and integrating published well, seismic and other geophysical and geological data into the model. Due to an agreement between Shell Development Australia and the Commonwealth Government, this data compilation was made available to the public in 2005. The FrOGTech compilation heavily utilises potential field modeling techniques and the parameters and meta-data used for the modeling are not distributed with data model. Hence the model can not be used with confidence to provide quantitative comparison with the Geoscience Australia Moho depth and CRUST2 models and is thus not included in the study. The OZSEEBASE™ grids are, however, compared to the data from Geoscience Australia.

Australia - Regional sediment thickness grids

The sediment thickness data was derived from GA in-house-compilation of depth to basement data from 44 published and unpublished contour maps created between 1977 and 1994. Offshore, it represents a seismic reflection horizon interpreted as basement, or the deepest horizon interpreted as sedimentary rift-phase fill. This data-model was compiled using seismic reflection, refraction, well and geological information at 0.1° resolution, providing a quantitative estimate of the depth to basement, covering continental Australia and its margins (Alexey Goncharov, pers. comm.). The sediment thickness estimate was computed by subtracting the ETOPO2 DEM from the depth to basement grid. In places with deep sedimentary basins next to outcropping basement, the GA gridding algorithm overshot the actual topography, resulting in negative depth to basement values, implying more topography than the actual observed relief. Here, the sediment thickness was automatically set to 0 m. According to the GA sediment thickness model, maximum values of up to 16 km are reached in the Petrel/Bonaparte basins on the Northwest Shelf and the southern parts of the Canning Basin.

FrOGTech uses a variety of different data and modeling techniques to generate a depth to the economic basement model. They constrain 2-D and 3-D models of the depth to the top magnetic basement with depth-converted seismic, well data and structural information to create high resolution sediment thickness grids.

As the Laske sediment thickness grid has been the reference sediment thickness due to its global coverage and good accuracy on that scale, the GA and FrOGTech  sediment thickness maps were subtracted from the Laske sediment thickness estimate to generate differential thickness maps (Fig. 4.11). From the differential thickness maps shown in Figure 4.10 it is evident that the original 1°× 1° grid cell resolution printed through to the resampled 2’×2’ grids. The original grid cell size of the sediment thickness is unable to resolve sediment thickness changes of shorter wavelengths. This becomes problematic mostly along the Australian margins, for example along the central section of the Southern Margin or along the northern margin in the Petrel Basin region, where the Laske model grossly underestimates the sediment thickness compared to regional scale grids. As this study is only concerned with large sedimentary basins in the interior of the continent these problems become less pronounced as the Laske model averaging accounts well for most of the basin areas apart from basins or sub-basins with a diameter below the 1° wavelength of the Laske model.

Australia - Regional basement thickness data

Alexey Goncharov of the Basement and Crustal Studies (BCS) project of Geoscience Australia supplied a set of recently processed seismic refraction data along several transects and isolated measurements. These data points were gridded using a nearneighbor gridding algorithm (GMT’s nearneighbor) and minimum curvature splines in tension gridding algorithm (GMT’s surface; Smith and Wessel, 1990) to produce a regional Moho depth grid at 2’ grid cell size. FrOGTech have also created a Moho depth estimate “ from scattered refraction data points supplied by Geoscience Australia (2005)” (Metadata description from crustal thickness file in OzSEEBASE) although neither the original data points nor details about the gridding algorithms are provided with the data.

Similar to the regional Australian sediment thickness data, differential crustal thickness grids (Fig. 4.13) have been created by taking the CRUST2 crustal thickness model as reference and subtracting the regional data. Here large differences and little correlation between the regional data sets and the global model are observed. The histogram of the difference grid between the Geoscience Australia data and the CRUST2 model shows a bimodal distribution with local peaks at about -3 km and 0 km indicates that the CRUST2 model systematically underestimates the plain crustal thickness (histogram Fig. 4.13a). Again, this is mostly observed where large gradients in crustal thickness occur, along the continental margins. The correlation between the FrOGTech crustal thickness model and the global data is very weak, the histogram (Fig. 4.13b) just showing an even distribution but spread out over the full range of difference thickness (20 km).

4.4 Methodology and workflow

In the following section I will briefly lay out the computing infrastructure and work flow which was built to analyse the global set of basins. The technical infrastructure was planned with a later web service in mind and designed to be extensible, accounting for an increase in data quality/availability either from global or regional data sets and the addition of new computational steps. Furthermore, the output and storage system can be easily tailored to allow porting to potential geodynamic modeling packages to e. g. derive a particular crustal architecture as target or as input for a numerical extensional model.

4.4.1 Software infrastructure

The global analysis of more than 200 sedimentary basins was accomplished using a combination of open-source geospatial, programming and visualisation tools. The solution is portable, and, as open-source software, free to use and available for all major operating system platforms. The file formats used to store data are all non-proprietary and standardised and platform independent. The project was set up and extensively tested on a Unix-based Apple Mac OS X platform.

The basin polygons and basin related meta-data (e.g. basin names, region codes, mean sediment thickness etc.) are stored in a geospatially enabled PostgreSQL database using PostGIS. PostgreSQL (PostgreSQL Global Development Group, 2007) is an open-source relational database system which runs on all major operating system platforms and complies with the SQL standards. The PostGIS project (Refractions Research, 2007) adds support for geospatial objects to the PostgreSQL database server and provides an extensive library of geospatial functions which can be used to query and alter geographic objects stored in the database. The PostGIS system follows the OpenGIS consortium’s “Simple Features Specification for SQL” standard (The Open Geospatial Consortium, Inc. (OGC), 2007). The PostGIS extension is also open-source software.

The Python programming language (Python Software Foundation, 2007) is used as “glue” to process information through command-line based applications and scripts which interact with the geospatial database using the psycopg2 extension (The T2 Project, 2007) module for Python. This module provides a set of functions for the Python application/script to interact with the database, query for specific data and write new records to the database. A set of Python command line programs which access the Generic Mapping Tools (GMT; Wessel and Smith, 1998) through the Unix shell perform computations on the individual data grids and are used to visualise the different types of data and grids. In order to store project related meta data like regional boundaries for map plots, or the location of data grids on the file system an XML schema has been developed (World Wide Web Consortium (W3C), 2007). This provides an easy, human- and machine readable way to store information which is used by the different Python applications. Furthermore, this modular architecture is extensible and allows very easy integration of additional data sets, in case the users has access to – for example – high resolution sediment thickness in a given basin.

4.4.2 Workflow

Based on geological information from published literature, a subset of basins (see Sec. 4.2) was selected from a global basin polygon database. Available input data were – if necessary – gridded on global grids, re-sampled to a 2’ grid cell size to allow basin-scale analysis and plotting of the data. The individual basin polygons selected for the study were extracted from the database and gridded using GMT’s grdmask routine. These “base grids” were than used as masks to derive basin-scale “observational” subsets from every global- or regional scale dataset (e.g. CRUST2 sediment thickness). Statistical information, generated from GMT’s grdinfo was written back into the database. At this stage, no further computations were carried out. Another routine generates a lithospheric-scale cross section for every basin using the longest possible axis within a given basin polygon to provide the users with a vertical geometry of the basin (Fig. 4.14).

After the observational processing was carried out, the extension factor, isostatic correction and tectonic subsidence grids were computed in a second analysis step. Again, statistical information derived from the computed grids was fed back into the database for later analysis.

A last step serves the data using a set of HTML documents in the form of a static but “interactive atlas”, providing maps and statistical overviews for each basin grouped by region. PostgreSQL/PostGIS provide an interface for the open-source desktop-GIS QGIS to display data stored in the spatial database in a dynamic, georeferenced, and interactive format. QGIS and PostgreSQL/PostGIS capabilities let the user for example interrogate the data, run queries or add actions such as the display of cross sections on a mouse click.

The setup of the workflow is modular, meaning that it is easy to implement either more computations or add the capability of integrating higher resolution regional scale data. The usage of XML as storage format for processing-related information provides the users with an easy-to-read and structured input file which can be verified against an XML schema to avoid wrong structuring of the document. The XML project file contains settings for user-specified regions (e.g. Australia, Southeast Asia), activates and de-activates the plotting and usage of different data and serves as a container for all project-related meta-data information.

4.5 Computations

A set of computations is carried out on the global crustal structure data to derive tectonic subsidence, extension factors and differential grids. For this project, both, sediment and sub-basement crustal thickness were used to derive independent estimates of these parameters.

I have not accounted for the basin age or any stratigraphic layers during these calculations, as this data is not available on a global scale. Tectonic subsidence and extension factors are estimated for the whole evolution of the basin from its initial rifting, as defined by the basement topography from the global / regional crustal model.

4.5.1 Extension factor β and total tectonic subsidence

The atlas is based on two main computations, namely the computation of the total extension (extension factor β) in a given basin polygon and the computation of the total tectonic subsidence (TTS). Both were calculated individually for the sediment thickness grids for all basins as well as on the observed sub-basement crustal thickness (SBCT) to obtain grids which illustrate the differences between extension and subsidence estimates stored in crustal and sediment thickness. Additionally, a residual lithospheric extension factor β_l was calculated using the thermal lithospheric thickness model TC1 by Artemieva (2006).

Extension factor β

Subsidence of the continental surface and the creation of sedimentary basins is often caused by continental thinning due to extensional forces acting on the continental crust. As the crust and lithosphere are thinned symmetrically and by the same amount, the principle of isostasy requires the surface to subside (McKenzie, 1978; Turcotte and Schubert, 2001; Allen and Allen, 2005). The McKenzie (1978) pure shear model assumes that the crustal volume stays constant during the process as do the crustal densities. It follows that the ratio of unextended to extended crustal thickness yields the extension factor β. Hence the extension factor is defined by:

(4.1)

where β is the stretch factor, t_c is the observed crustal thickness and t₀ is the initial crustal thickness. Le Pichon and Sibuet (1981) have shown that extension factors >3.5 in continental crust will cause the crust to rupture and lead to the formation of oceanic crust and the start of seafloor spreading.

Total tectonic subsidence (TTS)

Sawyer (1985) defined the term “total tectonic subsidence” (TTS) as the difference between the pre-rifting continental crust elevation and the present, sediment unloaded, basement depth in a sedimentary basin. He used TTS as tool to investigate the subsidence along profiles across the North American Atlantic margin in order to determine the location of the continent-ocean transition. Although the TTS contains less information than subsidence history analysis, it can be applied to multiple points in a basin and is thus ideal for basin-wide, or even continent-wide analysis of data points.

The total amount of subsidence in a basin is defined as the sum of the thickness of sediments above a basement horizon and the water depth. As most intracontinental basins are elevated slightly above sea level and mostly display evidence of shallow water deposition, the total subsidence equates to the sediment thickness. It is assumed that the basement surface was at sea level when the basin started forming. To obtain the tectonic subsidence from the sediment thickness, it is necessary to remove the sediments and unload the basement (backstripping). The unloading correction U requires to make assumptions about the sediment thickness and average density as well as the crustal response to spatially and temporarily varying sediment load. Generally the unloading equation takes the form of:

(4.2)

where U is the unloading correction, t_s the observed sediment thickness, _s the average sediment density of the whole sequence, ρ_m the mantle density and ρ_w the density of seawater. Typically U is about 2/3 of the sediment thickness.

When calculating the tectonic subsidence from sediment thickness, an average sediment density has to be computed. This will be discussed in more detail in Section 4.5.3. With regard to the crustal response to a laterally varying sediment load, it is assumed here that the crust is in local Airy isostatic equilibrium and has no flexural rigidity. As most of the basins have a diameter well over 300 km, the Airy isostatic compensation model is a good approximation to investigate large-scale basin subsidence anomalies.

Sawyer (1985) has shown that flexural loading and a local isostatic model will be similar in regions where there sediment has nearly uniform lateral thickness as the the flexural isostatic correction is a function of sediment thickness at all nearby points. As most of the intracontinental basins used in this study are broad, saucer shaped depressions, most basins show laterally relatively uniform sediment thicknesses.

4.5.2 Grids calculated from crustal thickness models

The global and regional crustal models allow to derive extension factor and tectonic subsidence estimates based on the sub-basement crustal thickness in every given basin.

Crustal extension factor – β_crust

The computation of grids for the crustal stretching factor was carried out by calculating a “plain” crustal thickness or sub-basement crustal thickness (i.e. without any sediments, see Fig. 4.3) subtracting the CRUST2 t4 grid (base hard sediments) from the CRUST2 t7 grid (Base lower crust = Moho). For accurate regional estimation of crustal extension factors, it was assumed that the median thickness of the basin rim (the outline of a given basin) represents the most objective input for an initial crustal thickness estimate relative to the observed crustal thickness in the basin interior. The median thickness along the individual basin rims were extracted from the SBCT grid and used as initial crustal thickness relative the the SBCT grid of the basin. The crustal extension factor for the basins in the study was calculated for the individual basins according to this equation:

(4.3)

where t_rim is the median crustal thickness along the basin rim and t_c is the SBCT in the interior of the basin. Figure 4.16 shows the parameter space of values for the crustal stretching factor.

The global map for the sub-basement crustal extension factors shows the highest values for basins which cover areas spanning from the interior of a continent onto the margins, like the Australian Canning Basin, the West Siberian Basin, the Patagonian Basins, the North American Arctic passive margin or the North African basins (for locations see Fig. 4.2) with values well over β_crust > 1.3. Higher crustal extension factors in true intracontinental basin settings are observed in the Australian Eromanga basin, the Chinese basins, the North Sea, the Precaspian and central West Siberian Basin. Also, the Basin and Range province clearly exhibits high crustal extension factors of 1.1 < β_crust < 1.34, as do basins in the vicinity of the East African Rift, which is comparable to a passive margin setting from the crustal extension point of view. These observations are well in agreement with published values (Artyushkov, 1992; Wernicke, 1992). In the West Siberian Basin it becomes obvious the initial 2°×2°grid cell size is the limiting factor of obtaining a more detailed global SBCT map.

Total tectonic subsidence (Crust) – TTSc 

The total tectonic subsidence is the difference between the elevation of the pre-rifting continental crust and the present, sediment-unloaded, basement depth in a sedimentary basin, see Section 4.5.1 (Sawyer, 1985). The TTS based solely on sub-basement crustal thickness TTSc can be derived using the equation of Le Pichon and Sibuet (1981) in Sawyer (1985); Allen and Allen (2005) and the SBCT extension factors β_c where:

(4.4)

where TTS_c is the total tectonic subsidence of the crust, and β_c is the crustal extension factor (Eqn. 4.3).

As the TTSc is based on β_c, the spatial patterns and distribution of minima and maxima are similar to the β_c shown in Fig. 4.18. The highest TTSc values ( > 3 km) are thus observed in the basins stretching onto continental margins but also in the Precaspian Basin, central West Siberian Basin and the eastern Chinese Basins. The Norwegian part of the North Sea and the western Baltic/Northern German/Denmark region also shows TTSc values of up to 2.4 km. The Australian Eromanga Basin and Murray-Darling Basins, the Timan-Pechora Basin west of the northern Ural Mountains in Russia, and the Texan Permian Basin, Oklahoma and Illinois Basins show notable higher TTSc values as “true” intracontinental basins.

4.5.3 Grids derived from sediment thickness

A second set of extension factor and total tectonic subsidence grids, similar to the ones derived from SBCT, characterising the tectonic evolution of a given sedimentary basin can be derived using the sediment thickness models.

Isostatic correction (IsC)

For computing the isostatic response of the crust due to sediment loading/unloading I follow Sykes’ approach (Sykes, 1996) to compute the average density of a sediment column of a given thickness based on a density-depth relationship derived from Ocean Drilling Program (ODP) data.

(4.5)

where t_s is the observed sediment thickness at any given point in the basin. The differences in isostatic correction between using this approach and using a uniform sediment density can exceed 2000 m for thick sedimentary sequences (10–12 km; Sykes, 1996).

Total tectonic subsidence (Sediments) – TTSs

The TTSs describes the tectonic subsidence which can be calculated from the observed sediment thickness in a given basin. To compute the tectonic subsidence inferred from the sedimentary infilling, the unloading correction based on the formula of Sykes (1996; comp. Section 4.5.3 and Fig. 4.20) is subtracted from the observed basement depth. Theoretically this should restore the basement to the surface, if no tectonic forces acted additionally on the basin. The grids for TTSs are computed using:

(4.6)

where t4 is the base of the hard sediments according to the CRUST2 model and IsC is the isostatic correction derived using Eqn. 4.5.

The global map of TTSs shows clear differences with respect to the TTSc derived from using SBCT values (comp. Fig. 4.19). High TTSc values are observed in most of the intracontinental basins globally. Most of the basins show a circular pattern, indicating more tectonic subsidence in the basin center. One example for this is the large Taoudeni Basin in Northwest Africa (Fig. 4.21). In general, most basins show significant TTS_s values of around 1.5–3 km. Exceptionally low values are observed in the cratonic basins of northern Australia and in Central Eastern Africa (Fig. 4.22).

Sediment thickness β

With the help of equation 4.5 an extension factor based on sediment thickness in a given basin can be determined. The extension factor βs for a given sediment thickness can be calculated by modifying the Le Pichon and Sibuet (1981) equation which relates subsidence and extension factor and has already been used to derive the amount of subsidence based on TTSc (Eqn. 4.4).

(4.7)

where TTSs is the total tectonic subsidence derived from observed sediment thickness. This way, a βs grid can be derived which is based on the observed sediment thickness.

Again, as the βs is based on TTSs similar patterns in spatial distribution of minima and maxima are observed. Fig. 4.18 shows relatively high extension factors for the selected intracontinental basins on global scale compared to the βc values. Most of the large basins, such as the West Siberian Basin the basins on the Arabian Peninsula, cratonic South American Basins and some of the Chinese basins show βs values βs > 1.8.

4.5.4 Mantle lid thickness and predicted elevation

The elevation of the Earth’s topography is a measure of the lithospheric buoyancy, assuming isostatic balance at the base of the lithosphere and a fluid asthenosphere of uniform density. The lithospheric buoyancy only depends on its density (ρ_L) and thickness (L) so that all parts on Earth with the same elevation are required to have the same lithospheric buoyancy (Lachenbruch and Morgan, 1990). Any change in surface elevation should be reflected by a change in lithospheric buoyancy (Lachenbruch and Morgan, 1990; Turcotte and Schubert, 2001; and references therein). The thermal lithospheric thickness (Artemieva, 2006) and crustal thickness from the CRUST2 model are used to compute mantle lid thickness and the predicted elevation based on a lithospheric buoyancy model Lachenbruch and Morgan (1990); Zoback and Mooney (2003). Following those authors, the elevation above sea level of a lithosphere of a mean density ρ_L floating on an asthenosphere with a constant density ρ_a can be computed:

(4.8)

where ε is the elevation above sea level, and H₀ is the buoyant height of the sea level relative to a hypothetical free asthenosphere surface (Lachenbruch and Morgan, 1990). The total lithospheric buoyancy can be broken down into crustal and mantle lid component, H_c and H_m, respectively, where the total lithospheric buoyancy is given by:

(4.9)

If ε represents the elevation above sea level:

(4.10)

with H₀ being the buoyant height of the sea level with reference to the free asthenosphere surface. Zoback and Mooney (2003) use a mean value of H₀ = 2.78 ± 0.35 km derived from other studies as the best estimate. The surface elevation is:

(4.11)

Mantle lid thickness

The mantle lid thickness controls the elevation of the crust by exerting a negative buoyancy component to the total lithospheric buoyancy due to cooler temperatures of lithospheric mantle material of the density ρ_a (Lachenbruch and Morgan, 1990; Zoback and Mooney, 2003). The mantle lid thickness can be computed by simply subtracting the crustal thickness grid from CRUST2 from the TC1 thermal lithospheric thickness grid (compare Figs. 4.4 and 4.9) so that:

(4.12)

where L_m is the thickness of the mantle lid (lithospheric mantle), L is the total lithospheric thickness, and L_c is the crustal thickness. The computed mantle lid thickness agrees well with published results from Zoback and Mooney (2003) and shows thick mantle lids for most of the cratonic regions (e.g. Siberian, Amazon and West Australian cratons) and thin lids in areas with active tectonics, like the East African rift zone, or the Alpine orogen (Fig. 4.24a). The basins used for this study show a characteristic mantle lid thickness of around 75–100 km. Basins with an exceptionally thin mantle lid are the SE Asian Sundaland basins located close to the plate margin, the West Siberian basin, the Precaspian basin, and the Central European basins with lid thicknesses well below 75 km. Thick mantle lids are found below the eastern Siberian Basins, basins in the region north of the Caspian and the Taoudeni Basin in Northwest Africa with lid thicknesses of around 140–160 km (Fig. 4.24b).

Predicted elevation

The predicted surface elevation can be calculated by using the mantle lid thickness data from Eqn. 4.12 and the lithospheric buoyancy model of Lachenbruch and Morgan (1990). Using the empirical crustal density relationship of Zoback and Mooney (2003; Fig. 4) the average crustal density ρ_c is defined by:

(4.13)

Here, t_c is the crustal thickness, which is based on the CRUST2 model (Fig. 4.4). Following Lachenbruch and Morgan (1990), the average crustal density grid can then be used to calculate the crustal buoyancy H_c, assuming a uniform asthenospheric density ρ_a = 3300kg∕m³. :

(4.14)

To compute the mantle lid buoyancy contribution, several assumptions have to be made when global crustal and lithospheric data is used. The base lithosphere temperature is given by the TC1 thermal lithospheric thickness model as Θ_a = 1300° C. In order to derive the average mantle lid temperature, the temperature at 50 km depth from the TC1 model is combined with the Moho depth estimate from the CRUST2 model, and the TC1 lithospheric temperature gradient assuming a linear geotherm. The base crust temperature Θ_c and the uniform asthenospheric temperature Θ_a define a simple average lithospheric mantle temperature which is used with a thermal expansion coefficient α = 3.5 × 10^-5 ° C^-1 and the observed lithospheric mantle lid thickness L_m to compute the mantle lid buoyancy contribution H_m

(4.15)

The negative mantle lid buoyancy H_m and crustal buoyancy H_c are added to a total lithospheric buoyancy H and following Eqn. 4.11, the predicted surface elevation ε is derived by subtracting H₀ (2.78 km). Calculated surface elevations for the crustal/lithospheric parameters used in this study show relatively high elevations form most of the continental areas and basins (Fig. 4.25).

The elevation plots (Figs. 4.26–4.26) for the global basin data set show generally a large discrepancy between the predicted and the actual observed topography for all regions. Most of the predicted elevations cluster around 1000 m and it appears that some inconsistencies between the CRUST2 and the TC1 lithospheric thickness and thermal properties introduce this significant misfit. The computational results here do not agree with the more detailed analysis carried out by (Mooney and Vidale, 2003; Zoback and Mooney, 2003). It is suggested that in terms of investigating isostatic properties for individual basins, the global approach presented here is not sufficiently accurate. As Zoback and Mooney (2003); Lachenbruch and Morgan (1990) demonstrate, uncertainties in heat flow and heterogeneous structure of the lithosphere can introduce large errors and need to be accounted for in a more detailed way than which is possible in this automated approach.

4.5.5 Crustal thickness and topography

Following Lachenbruch and Morgan (1990); Mooney and Vidale (2003); Zoback and Mooney (2003) I have use the ETOPO2 topography and the CRUST2 models to compare crustal thickness and observed elevation of the global basin data set. Figure 4.27–4.27 show the regional thickness versus median topography of the individual basin polygons. There is only a very weak correlation between the crustal thickness and elevation in most of the basins used in this study. Hence isostasy must be achieved either through variations in lateral crustal or mantle lid densities (chemical depletion or lower temperatures; Zoback and Mooney, 2003; Mooney and Vidale, 2003) or through larger scale dynamic processes in the mantle. Especially the African (Fig. 4.27a), Arabian (Fig. 4.27b), North American and Arctic (Figs. 4.27g and 4.27h), and Siberian (Fig. 4.27j) basins indicate that the relationship between crustal thickness and elevation breaks down with even lower elevations for thicker crust.

4.6 Global results

Based on the computations of the total tectonic subsidence and extensional factors both, for observed sub-basement crustal and sediment thickness, differential (“anomalous”) tectonic subsidence and extension factor maps can be computed. These maps provide a valuable tool to assess the overall crustal structure and geodynamics mechanisms involved in the evolution of the basins since their formation. I present two maps showing the differential extension factor and anomalous tectonic subsidence, generated by subtracting crustal grids from their sediment equivalents. Additionally a residual lithospheric extension factor map, based on the thermal lithospheric thickness has been generated and will be discussed. It is important to note that the crustal and sediment thickness grids are time-independent estimates of extension and subsidence, in contrast to the residual lithospheric extension factor.

4.6.1 Differential beta

The differential β map is obtained by subtracting βc grids from βs grids. The differential β is a measure of the misfit between extension factors derived from sub-basement crustal thickness and the extension factors derived from the sediment thickness for the global set of basins analysed in this study. Values close to 0 indicate an equal amount of stretching estimated both, from SBCT and sediment thickness. Areas with negative Δβ are the North Sea/Anglo-Paris Basin region, the eastern Chinese Basins, some of the Southeast Asian Sundaland Basins and the majority of the Patagonian Basins. High positive Δβ values are observed in the West Siberian Basin, most of the African Basins, the intracontinental North American Basins and throughout the Russian Platform. The maximum differential beta values are reached in the Precaspian basin which has a sediment thickness of up to 20 km according to the CRUST2 model. In general, basins in marginal settings are mostly characterised by negative differential beta values, whereas “true” intracontinental basins mostly show slightly higher differential beta values due to more sediment infill than estimated from SBCT stretching. As Le Pichon et al. (1981) already account for thermal basin subsidence in their equation, the positive differential beta must be attributed to other tectonic effects, like creation of additional sediment accommodation space through dynamic topography, phase transitions or thermal perturbations in the lower crust due to magmatic intrusions.

4.6.2 Anomalous tectonic subsidence

Similar to the differential stretching factor, the anomalous tectonic subsidence is calculated by subtracting the total tectonic subsidence derived from crustal extension from the total tectonic subsidence estimated from the sediment thickness in a given basin. The difference denotes, like for the differential beta, the degree of anomalousness of a given basin. The global median value for the selected basins in this study is 1.37 km.

Apart from the eastern Chinese Basins, the Basin and Range Province, and the Patagonian offshore region, the overwhelming majority of the basins shows large anomalous tectonic subsidence values, in favour of larger tectonic subsidence estimates from the sediment thickness, than from crustal stretching. Partly, this can be attributed to an underestimation of sediment densities using the isostatic correction equation of Sykes (1996). In basins with higher sediment densities, for example due to significant carbonate deposition, a part of the large anomalous tectonic subsidence values is probably due to an underestimation of the average densities. This could be the case on the Arabian Peninsula or the Taoudeni Basin in Northwest Africa (comp. Fig. 4.21). In order to quantify the error due to generalising the sediment densities, more detailed local assessments and studies would be necessary which were beyond the scope and time constraints of this study.

The anomalous tectonic subsidence maps do not discriminate between mechanisms other than crustal stretching which can create sediment accommodation space, such as flexural loading. In basins close to convergent plate boundaries, like the northern part of the Arabian basins, or the Tarim Basin, flexural effects can be the dominating cause for anomalously high tectonic subsidence values.

4.6.3 Residual lithospheric extension

The residual lithospheric extension value is computed using a similar approach as for the computation of βc. Here, the thermal lithospheric thickness along the basin margin is extracted from the TC1 model (Artemieva, 2006) and put in relation with the observed lithospheric thicknesses in the interior of the basin, so that:

(4.16)

where L_rim is the median thermal lithospheric thickness extracted from the basin rim and L is the observed lithospheric thickness in the basin interior. As the lithosphere cools and thickens after a rifting event, the RLE values are dependent on the time of the last thermo-tectonic event in a given basin. For young basins, like those overlapping passive margins, or close to zones of active extension, higher RLE values are observed than for older, mature basins. The southern Argentinian basins and the Newfoundland marginal basins are examples. For older intracontinental basins, RLE values of close to 0 would be expected. However, the basin polygon location plays a large role for the computation of the RLE values. If a basin overlaps the margin of a craton with large lithospheric thickness and an adjacent area with thinner lithosphere, the median rim lithospheric thickness automatically increases. This is the case for the Australian Canning basin, which onlaps onto the Pilbara Craton with thick lithosphere, whereas the other parts of the basin margin are located on thinner lithosphere (comp. Fig. 4.9), but also for basins bordering the southern margin of the East European craton. Processes like lithospheric delamination can also cause localised, high RLE values (Meissner and Mooney, 1998).

Results from the residual lithospheric extension (RLE) show locally confined regions or relatively high RLE values. One of the most prominent areas with the highest RLE values is the West Siberian Basin. Maximum RLE values are reached there basically throughout the central part of the basin. This is partly due to relatively thick lithosphere along the basin margins with the Siberian Craton to the east and the Ural Mountains in the west, but also due to a thinner lithosphere of around 100 km in the basin centre (comp. Fig. 4.9). A similar setting is observed for the Precaspian Basin, with very high RLE values in the basin centre. Although not as spatially extensive as the WSB the Precaspian Basin shows a very localised thinning of the lithosphere and thus high RLE values of more than 1.8. Other notable basins with relatively high RLE values are the southern part of the Central European Basin System (CEBS), below the southern North Sea, Northwest German and Münsterland basins. In Australia, the eastern part of the continent, below the extensive Murray-Darling basin, stretching up into Queensland show high RLE values. In this case, it is likely the proximity to a former, relatively young, Late Mesozoic/Cenozoic plate boundary and present passive margin which is the cause for the higher RLE values. Basins overlapping onto the continental margins and onto younger, newly reworked/accreted lithosphere show naturally significantly higher RLE values.

4.7 Discussion

I have demonstrated in this chapter that global crustal and lithospheric structure data sets are a valuable tool to assess the evolution of intracontinental basins and their mismatch with conservative extension models on a global scale. Differential extension grids, anomalous tectonic subsidence, residual lithospheric extension maps and topography vs. crustal thickness plots derived from global crustal structure data can be used to classify intracontinental basins. The data provides a time-independent insight into the underlying geodynamic mechanisms associated with intracontinental basin evolution. The crustal structure information derived from global and large-scale regional crustal models allows to explore a new parameter spaces which can be utilised for future geodynamic modeling packages.

The spatial patterns of the differential extensional factors varying within a given basin and the anomalous tectonic subsidence allows to make assumptions about the causes of larger sediment thickness than what would be expected from standard basin modeling or anomalous subsidence observed in those basins.

For instance sharp, localised anomalies of anomalous TTS and differential extension factors within a basin are likely to indicate localised processes in the crust or underlying lithosphere which are responsible for the observed additional subsidence. The Precaspian, Permian (West Texas), Taoudeni (Africa) or Paranà (South America) basins all show very localised high differential extension and anomalous subsidence values in or near the basin centre. This might be related to phase transformations in deeper parts of the sedimentary basins due to increasing overburden as proposed by Artyushkov (1992); Kaus et al. (2005); Petrini et al. (2001). In particular Artyushkov (1992) mentioned that most of these basins form without large amounts of crustal extension, a theory which is confirmed by the findings of this study which suggests evidence of positive differential extension values for most of the basins, indicating less crustal extension than that inferred from the observed sediment thickness.

To summarise, the observed patterns of anomalous subsidence and differential extension factors can be grouped into three main classes:

1. Localised, defined highs of both values are likely to indicated local, basin-scale crustal or lithospheric processes as a cause for anomalous subsidence in a basins
2. diffuse, broad patterns of both values are most likely caused by regional-scale processes such as dynamic topography. The effects of dynamic topography on basin subsidence will be discussed in more detail in the following chapter.
3. asymmetric distribution of differential extension factors or anomalous TTS patterns likely indicate flexural causes for excess sediment in a given basin due to loading at one side. This can partly be observed in the Arabian Basins as well as in the sub-Andean basins of South America and some Chinese basins.

The lack of detailed crustal structure information is sometime obscuring important information, as the initial 2°resolution of the CRUST2 is not capable of resolving important features on basin scale. However, the approach and the workflow presented here demonstrates, that the global analysis of large sedimentary basins using large data sets is possible and offers a new view on the driving mechanisms of basin evolution.