Lithology-derived structure classification from the joint interpretation of magnetotelluric and seismic models

Abstract

S U M M A R YMagnetotelluric and seismic methods provide complementary information about the resistivity and velocity structure of the subsurface on similar scales and resolutions. No global relation, however, exists between these parameters, and correlations are often valid for only a limited target area. Independently derived inverse models from these methods can be combined using a classification approach to map geologic structure. The method employed is based solely on the statistical correlation of physical properties in a joint parameter space and is independent of theoretical or empirical relations linking electrical and seismic parameters. Regions of high correlation (classes) between resistivity and velocity can in turn be mapped back and reexamined in depth section. The spatial distribution of these classes, and the boundaries between them, provide structural information not evident in the individual models. This method is applied to a 10 km long profile crossing the Dead Sea Transform in Jordan. Several prominent classes are identified with specific lithologies in accordance with local geology. An abrupt change in lithology across the fault, together with vertical uplift of the basement suggest the fault is sub-vertical within the upper crust.The interpretation of geophysical models derived by inversion is a highly subjective part of any geologic study. Our incomplete knowledge of the subsurface, the spatially-varying resolution of the models, and the non-uniqueness of the geophysical inverse problem make it difficult to objectively interpret physical property models in terms of geologic structure. The problem is exacerbated by the many-tomany, or at best, many-to-one, relationship between geologic units and their physical properties. It is thus commonplace to use multiple methods to determine multiple physical properties over an area of interest in order to discriminate between the range of possible geologic/lithologic structures. The analysis of such complementary data, however, is rarely taken beyond a qualitative comparison. Attempts at a quantitative comparison are, for the most part, centered upon constitutive or empirical relations between physical properties, which tend to be limited in scale and applicability.Seismic and magnetotelluric (MT) methods are often favored for crustal studies as they provide images of acoustic velocity (V p , V s ) and electrical resistivity (ρ), respectively, on similar scales and with comparable spatial resolution (Jones 1987). By looking in tandem at velocity and resistivity we retain the strengths of each method, while lessening the susceptibility of our interpretation to their individual weaknesses. Seismic refraction, for example, has difficulty imaging vertical velocity contrasts. Along a similar vein, MT has difficulty resolving structure beneath strong conductors due to the large amount of energy dissipated within them. A properly formulated joint interpretation must take these variations in resolution into account, but unfortunately there exists no fundame...

Initial results from wide-angle seismic refraction lines in the southern Cape

“…Put differently, the velocity model we are trying to produce is dependent on our first guess at it. To minimize the importance of the accuracy of this first guess, an iterative approach varying cell sizes has been developed (also discussed in Ryberg et al, 2007). In this approach, the initial cell size is made vary large, for example the cross-section is divided into six cells along its length, and four along depth, and a fairly straightforward initial model is provided.…”

Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data

“…Examples of well-known geostatistical methods that may have value in cooperative inversion include basic methods such as kriging or co-kriging and clustering techniques such as k-means, Fuzzy logic, principal component analysis (PCA) and neural networking (Bedrosian et al 2007;De Benedetto et al 2012;Di Giuseppe et al 2014;Dubrule 2003;Kieu and Kepic 2015;Klose 2006;Roden et al 2015;Ward et al 2014).…”

“…Bedrosian et al (2007) use kriging to interpolate inverted MT-derived electrical resistivity values onto a finer mesh corresponding to that used for seismic velocity, to perform lithological classification. Bourges et al (2012) demonstrate the benefits of co-kriging on the two dataset's porosity and acoustic impedance.…”

Joint Interpretation of Magnetotelluric and Seismic Models for Exploration of the Gross Schoenebeck Geothermal Site

“…The methodology used in the present paper was described by Bedrosian et al (2007) and is based on a probabilistic approach developed by Bosch (1999), in a sense that diverse geophysical parameters are represented as a probability density function (pdf) in the joint parameter space. The coincident velocity and resistivity models are first interpolated onto a common grid.…”

“…However, with a statistical analysis of the distributions of both resistivity and velocity, we can find certain areas of the models space where a particular relation between the physical parameters holds locally, thus allowing us to characterize this region as a particular lithology. In the present work, we use a statistical analysis, as described by Bedrosian et al (2007) in order to correlate two independently obtained models of the Groß Schönebeck geothermal test site in the Northeast German Basin.…”