Abstract Electrical resistivity tomography (ERT) is a popular geophysical tool used for a variety of prospecting. To derive a true resistivity model from the observed apparent (ERT) data, the smoothness-constrained least squares inversion method is still frequently employed. However, the smooth inversion usually obtained unclear interfaces of resistivity changes, which exacerbates the final interpretation of the inverted model. To overcome the drawback related to the smoothness-constrained inversion, I proposed using the Euler deconvolution (ED) method as a layer interface detector for interpreting ERT data. By employing the ED approach, the boundaries of various resistivity zones could be automatically identified rather than relying on manual detection. To achieve this, the efficiency of the ED method in interpreting ERT data was evaluated using both synthetic models and actual field cases. In this paper, five models were used to simulate different scenarios of horizontally stratified and undulating layers using RES2DMOD software. The response of these models was calculated using the Wenner and dipole–dipole array. Then the synthetically apparent data were inverted using Res2dinv software. The results obtained from the inversion process were interpreted using the ED method. The overall findings demonstrate that, for both the simulated and actual data, the calculated Euler depth solution closely matches the layer interface of the inverted resistivity sections. A structure index of 0 produced the tightest cluster solutions. This study highlights that in order to improve the interpretation of the inversion results, the ED approach can be utilized as an additional processing tool for ERT data interpretation.
Abstract The paper comprises an (s,S) production inventory facility in which multiple vacations and varying service rates are considered for the multiple servers. The arrival of customers constitutes a Markovian Arrival Process (MAP) and service completion times follow the phase type distributions with representations ( α, S ) and ( β, T ) respectively. Manufacturing needs to begin at the moment when the stock position falls to s. Service is offered at a lower rate if the stock level ranges from 0 to s and the service time distribution has the representations (α, η 1 S) and (β, η 2 T ), 0 < η 1, η 2 < 1 respectively. If the stock level reaches the maximum S, production is ceased to operate. 1-limited service policy, Bernoulli service schedule, and Exhaustive service disciplines are considered for the servers. A suitable cost function is outlined as per performance assessment. The impact of negative correlated inter arrival times on cost function is illustrated. Also, a relative study of expected cost function on different service modes is presented.