Simultaneous joint migration inversion with calendar-time constraints as a processing tool for semi-continuous surveys

Nowadays, to obtain a better understanding of dynamic time-lapse changes, frequent seismic monitoring is necessary, although it will generate a considerable cost increase. Therefore, low-cost frequent monitoring, e.g., sparse and/or nonrepeated surveys, is desired. The simultaneous inversion-based method allows the baseline and monitor parameters to communicate and compensate with each other during inversion via constraints and helps to reduce the artifacts caused by sparse acquisition. These features make it largely independent of the used low-cost acquisition geometry and suitable for inexpensive frequent monitoring surveys. Therefore, we have used this simultaneous inversion-based method as an effective time-lapse processing tool for data sets acquired from inexpensive, semi-continuous time-lapse monitoring surveys, which are based on the so-called instantaneous 4D (i4D) technology. We choose a specific simultaneous inversion method called simultaneous joint migration inversion (S-JMI), which combines a simultaneous processing strategy with the JMI method. In i4D technology, inexpensive localized/sparse surveys, called i4D surveys, are deployed frequently between the conventional full-field surveys. This technology can be treated as a special case of changing geometries during monitoring. In this case, the simultaneous strategy allows the information of the full-field survey to compensate for the insufficient illumination of the localized/sparse i4D surveys during processing. Furthermore, we apply constraints on the reflectivity and velocity differences between the baseline and monitor vintages along the calendar-time axis called calendar-time constraints. These constraints take advantage of the feature that time-lapse effects develop (semi-)continuously along the calendar-time axis, when the monitoring surveys are deployed (semi-)continuously over calendar time. Based on a complex synthetic example, we determined that S-JMI is a promising tool to process the data sets from the semi-continuous monitoring surveys based on i4D technology. Finally, we found that the calendar-time constraints significantly improve the quality of time-lapse effects.