A computer-implemented method for estimating a likelihood of an object element in a given position in a subsurface volume of interest, the method being implemented in a computer system that includes a physical computer processor, non-transient electronic storage, and a graphical user interface, the method comprising: obtaining, from the non-transient electronic storage, target subsurface data from the subsurface volume of interest, wherein the target subsurface data was obtained by sending energy into the subsurface volume of interest using sources and receiving the signal reflected off of a subsurface feature at receivers; the method being characterized by further comprising: obtaining, from the non-transient electronic storage, an object element set corresponding to the subsurface volume of interest, wherein the object element set includes (i) object templates specifying object elements in the subsurface volume of interest and (ii) object filters derived from the object templates; generating, with the physical computer processor, correlation values as a function of position in the subsurface volume of interest by applying the object filters to the target subsurface data, wherein applying the object filters to the target subsurface data includes rotating and/or scaling one or more of the object filters on the target subsurface data, wherein a given correlation value specifies a correlation between a given object filter and corresponding target subsurface data; and generating, with the physical computer processor, object element likelihood values by applying the object templates to positions in the subsurface volume of interest corresponding to the correlation values, wherein the object templates are applied to the subsurface volume of interest based on a magnitude of the correlation values, and wherein a given object element likelihood value estimates a likelihood of a given object element at a given position in the subsurface volume of interest. obtaining, from the non-transient electronic storage, target subsurface data from the subsurface volume of interest, wherein the target subsurface data was obtained by sending energy into the subsurface volume of interest using sources and receiving the signal reflected off of a subsurface feature at receivers; the method being characterized by further comprising: obtaining, from the non-transient electronic storage, an object element set corresponding to the subsurface volume of interest, wherein the object element set includes (i) object templates specifying object elements in the subsurface volume of interest and (ii) object filters derived from the object templates; generating, with the physical computer processor, correlation values as a function of position in the subsurface volume of interest by applying the object filters to the target subsurface data, wherein applying the object filters to the target subsurface data includes rotating and/or scaling one or more of the object filters on the target subsurface data, wherein a given correlation value specifies a correlation between a given object filter and corresponding target subsurface data; and generating, with the physical computer processor, object element likelihood values by applying the object templates to positions in the subsurface volume of interest corresponding to the correlation values, wherein the object templates are applied to the subsurface volume of interest based on a magnitude of the correlation values, and wherein a given object element likelihood value estimates a likelihood of a given object element at a given position in the subsurface volume of interest. obtaining, from the non-transient electronic storage, an object element set corresponding to the subsurface volume of interest, wherein the object element set includes (i) object templates specifying object elements in the subsurface volume of interest and (ii) object filters derived from the object templates; generating, with the physical computer processor, correlation values as a function of position in the subsurface volume of interest by applying the object filters to the target subsurface data, wherein applying the object filters to the target subsurface data includes rotating and/or scaling one or more of the object filters on the target subsurface data, wherein a given correlation value specifies a correlation between a given object filter and corresponding target subsurface data; and generating, with the physical computer processor, object element likelihood values by applying the object templates to positions in the subsurface volume of interest corresponding to the correlation values, wherein the object templates are applied to the subsurface volume of interest based on a magnitude of the correlation values, and wherein a given object element likelihood value estimates a likelihood of a given object element at a given position in the subsurface volume of interest.