The plus–minus method, also known as CRM (conventional reciprocal method), is a geophysical method to analyze seismic refraction data developed by J. G. Hagedoorn. It can be used to calculate the depth and velocity variations of an undulating layer boundary for slope angles less than ~10°.[1]
In the plus–minus method, the near surface is modeled as a layer above a halfspace where both the layer and the halfspace are allowed to have varying velocities. The method is based on the analysis of the so-called 'plus time'
t+
t-
t+=tAX+tBX-tAB
t-=tAX-tBX-tAB
where
tAB
tAX
tBX
Assuming that the layer boundary is planar between A'' and B'' and that the dip is small (<10°), the plus time
t+
t-
t-=t++
2x | |
v2 |
where
x
v2
Therefore, the slope of the minus time
\trianglet-/\trianglex
v2
v2(x)=2
\trianglex | |
\trianglet- |
The interval
\trianglex
\trianglex
t+
z(x)=
t+v1(x)v2(x) | ||||||||||||||
|
This requires an estimation of the velocity of the upper layer
v1(x)
Furthermore, the results of the plus–minus method can be used to calculate the shot-receiver static shift
\triangle\tau(x)
\triangle\tau(x)=-
z(x) | |
v1(x) |
+
EX-ES+z(x) | |
v2(x) |
where
EX
ES
The plus–minus method was developed for shallow seismic surveys where a thin, low velocity weathering layer covers the more solid basement. The thickness of the weathering layer is, among others, important for static corrections in reflection seismic processing or for engineering purposes. An important advantage of the method is that it does not require manual interpretation of the intercept time or the crossover point. This makes it is also easy to implement in computer programs. However, it is only applicable if the layer boundary is planar in parts and the dips are small. These assumptions often lead to smoothing of the actual topography of the layer boundary. Nowadays, the plus–minus method has mostly been replaced by more advanced inversion methods that have less restrictions. However, the plus–minus method is still used for real-time processing in the field because of its simplicity and low computational costs.[3]