This paper discussed a novel application called merge-optimization method that combines resistivity and seismic refraction data to provide a detailed knowledge of the studied site. This method is interesting because it is able to show strong accuracy of two geophysical imaging methods based on many of data points collected from the conducted geophysical surveys of disparate data sets based strictly on geophysical models as an aid for model integration for two-dimensional environments. The geophysical methods used are high resolution methods. The resistivity imaging used in this survey is able to resolve the subsurface condition of the studied site with low RMS error (less than 2.0%) and 0.5 metre electrodes interval. For seismic refraction method, high resolution of seismic is used for correlation with resistivity results. Geophones spacing is 1.0 metre and the total number of shot-points is 15, which provides very dense data point. The algorithms of merge-optimization have been applied to two data sets collected at the studied site. The resulting images have been proven to be successful because they satisfy the data and are geometrically similar. The regression coefficient found for conductivity-resistivity correlation is 95.2%.

The characterization of the subsurface requires a detailed knowledge of several properties of the composing rocks and fluids. Whereas some of these properties can be measured directly (seismic and borehole methods), other properties have to be estimated by indirect measurement methods such as resistivity, TEM, and magnetic. However, it is not uncommon that the geophysical data yield models of limited accuracy which may not contribute significantly to our understanding of the subsurface condition or may show incompatibilities. Thus, a new technique needs to be produced not only for better interpretation by geophysics but also for nongeophysical background people such as engineers and architects. The distribution of uncorrelated physical properties seems to be controlled by common subsurface attributes, when taken into account, able to improve and resolve the accuracy of the geophysical imaging results. An outstanding feature of the subsurface that is common to the geophysical data is the geometrical distribution of the physical properties which can be measured by the physical property changes. This condition of commonality can be incorporated in the process of estimation to obtain meaningful and more reliable subsurface imaging results.

In this paper, seismic refraction data using the SeisOpt@2D software were developed by Pullammanappallil and Louie [

This paper adopts a merge problem formulation with resistivity-velocity cross gradients function in order to provide the required effective link between the resistivity model and the seismic velocity model. The cross product of the gradient is defined as

The cross gradients criterion requires the problem to satisfy the condition

In iteration 2D optimization approach, the subsurface model is discretised into rectangular cells of variable sizes optimized according to the natural sensitivity of each particular set of resistivity and seismic velocity measurements. We define the discrete version of (

Definition of the resistivity-velocity cross gradients function and its derivatives on a rectangular grid domain. For 2D grid extending in the

Here, the first order of Taylor series expression is used, and (

In this paper, we used data collected during recent electrical tomography and seismic refraction surveys [

Geometry of the infield tests for both geophysical methods.

The seismic refraction velocity and electrical resistivity imaging derived by merge-optimization of infield tests data sets (Figures

March infield’s test results for optimal 2D merge resistivity and velocity models (a) resistivity model, (b) velocity model, and depth section with arcs.

April infield’s test results for optimal 2D merge resistivity and velocity models (a) resistivity model, (b) velocity model, and depth section with arcs.

The resistivity images show that the subsurface of the studied sit consists of two main zones. Resistivity values lower than 900 Ω.m are indicative of residual (clayey sand soils) while values higher than 1,100 Ω.m are indicative of a weathered layer. The presence of moist zones and dry zones can be associated with loose zones and compacted soil, respectively. The seismic refraction images showed that the subsurface consists of three layers. Velocity values of 370–500 m/s are associated with loose soil mixed with boulders (high resistivity value near surface). The second layer has velocity values of 600–800 m/s associated to hard layer (unsaturated) and the third layer has velocity values greater than 1,000 m/s which are associated with a saturated layer. Comparing the results of the two geophysical methods, we can summarize that the resistivity method has the limitation of a lower depth of investigation with respect to seismic refraction. On the other hand, seismic refraction is unable to resolve well the subsurface features and has much less resolution compare to electrical resistivity. However, both data sets showed their validity and reliability when correlated together to determine the subsurface features of studied site. In this paper, 838 of data points correlated. This can be showed by relationship resistivity-velocity (Figure

The results show an L-shaped (A-B) trend that may be a consequence of a water table or natural divide between consolidated and unconsolidated materials.

To ascertain the resistivity-velocity relationship for the reconstructed models, we have plotted in Figure

In Figure

(a) Interpretative model showing empirical correlation between conductivity (S/m) and resistivity (Ω.m) is found as

The incorporation of the cross-gradients criterion in 2D optimization leads to a geologically meaningful solution by improving the near-surface structural conformity between the velocity and resistivity imaging, without forcing or assuming the form of the relationship between two geophysical methods which have their own advantage and limitation. The cross-gradients criterion also allows detecting subsurface features to which only one of the geophysical techniques is sensitive, leading to a better structural characterization. The application of these geophysical techniques combination of 2D optimization with cross-gradients to the data collected from seismic refraction and electrical resistivity field surveys has led to an improved characterization of the near-surface material and features. This study suggests that unconsolidated (possibly unsaturated and saturated) materials may be subclassified on the basis of the resistivity-velocity relationship revealed from the application of merge-optimization method. The cross-gradients approach adopted in this paper can also be used for 3D problems and for any combination of independent geophysical methods.

A. A. Bery would like to thank Rosli Saad, Mydin Jamal, and Nordiana Mohd Muztaza for their assistance in giving advice and data acquisition. The author also would like to thank and give appreciation to Mr. Jeff Steven and Mdm. Eva Diana for their support and advice. Lastly the author would like to thank and to express profound appreciation to anonymous reviewers for insightful comments that helped improved the quality of this paper.