diff --git a/pyemu/utils/helpers.py b/pyemu/utils/helpers.py
index caf8dc83..ad759a56 100644
--- a/pyemu/utils/helpers.py
+++ b/pyemu/utils/helpers.py
@@ -3805,7 +3805,7 @@ def get_current_prop(_cur_thresh):
 
 
 def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_kernel=None,nverf=0,
-                 plot_fits=False):
+                 plot_fits=False,apply_standard_scalar=False):
     """helper function to setup a gaussian-process-regression emulator for outputs of interest.  This
     is primarily targeted at low-dimensional settings like those encountered in PESTPP-MOU
 
@@ -3829,6 +3829,9 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_
 
     from sklearn.gaussian_process import GaussianProcessRegressor
     from sklearn.gaussian_process.kernels import RBF, Matern, ConstantKernel
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.pipeline import Pipeline
+
     import pickle
     import inspect
     pst = pyemu.Pst(pst_fname)
@@ -3916,12 +3919,13 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_
 
 
     if gp_kernel is None:
-        gp_kernel = ConstantKernel(constant_value=1.0,constant_value_bounds=(1e-8,1e8)) *\
-                             RBF(length_scale=1000.0, length_scale_bounds=(1e-8, 1e8))
-        #gp_kernel = Matern(length_scale=100.0, length_scale_bounds=(1e-4, 1e4), nu=4)
+        #gp_kernel = ConstantKernel(constant_value=1.0,constant_value_bounds=(1e-8,1e8)) *\
+        #                     RBF(length_scale=1000.0, length_scale_bounds=(1e-8, 1e8))
+        gp_kernel = Matern(length_scale=100.0, length_scale_bounds=(1e-4, 1e4), nu=0.5)
 
     for hp in gp_kernel.hyperparameters:
         print(hp)
+
     cut = df.shape[0] - nverf
     X_train = df.loc[:, input_names].values.copy()[:cut, :]
     X_verf = df.loc[:, input_names].values.copy()[cut:, :]
@@ -3937,11 +3941,16 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_
         y_train = df.loc[:, output_name].values.copy()[:cut]
         print("training GPR for {0} with {1} data points".format(output_name,y_train.shape[0]))
         gaussian_process = GaussianProcessRegressor(kernel=gp_kernel, n_restarts_optimizer=20)
-        gaussian_process.fit(X_train, y_train)
-        print(output_name,"optimized kernel:",gaussian_process.kernel_)
+        if not apply_standard_scalar:
+            print("WARNING: not applying StandardScalar transformation - user beware!")
+            pipeline = Pipeline([("gpr",gaussian_process)])
+        else:
+            pipeline = Pipeline([("std_scalar", StandardScaler()), ("gpr", gaussian_process)])
+        pipeline.fit(X_train, y_train)
+        print(output_name,"optimized kernel:",pipeline["gpr"].kernel_)
         if plot_fits:
             print("...plotting fits for",output_name)
-            predmean,predstd = gaussian_process.predict(df.loc[:, input_names].values.copy(), return_std=True)
+            predmean,predstd = pipeline.predict(df.loc[:, input_names].values.copy(), return_std=True)
             df.loc[:,"predmean"] = predmean
             df.loc[:,"predstd"] = predstd
             isverf = np.zeros_like(predmean)
@@ -3971,13 +3980,13 @@ def prep_for_gpr(pst_fname,input_fnames,output_fnames,gpr_t_d="gpr_template",gp_
         if os.path.exists(os.path.join(gpr_t_d,model_fname)):
             print("WARNING: model_fname '{0}' exists, overwriting...".format(model_fname))
         with open(os.path.join(gpr_t_d,model_fname),'wb') as f:
-            pickle.dump(gaussian_process,f)
+            pickle.dump(pipeline,f)
 
         model_fnames.append(model_fname)
         if nverf > 0:
-            pred_mean,pred_std = gaussian_process.predict(X_verf,return_std=True)
+            pred_mean,pred_std = pipeline.predict(X_verf,return_std=True)
             vdf = pd.DataFrame({"y_verf":y_verf,"y_pred":pred_mean,"y_pred_std":pred_std})
-            verf_fname = os.path.join(gpr_t_d,os.path.split(pst_fname)[1]+"."+output_name+".verf.csv")
+            verf_fname = os.path.join(gpr_t_d,"{0}_gpr_verf.csv".format(output_name))
             vdf.to_csv(verf_fname)
             print("saved ",output_fname,"verfication csv to",verf_fname)
             mabs = np.abs(vdf.y_verf - vdf.y_pred).mean()