diff --git a/adloc/_ransac.py b/adloc/_ransac.py
index 3d125ef..d6d5550 100644
--- a/adloc/_ransac.py
+++ b/adloc/_ransac.py
@@ -422,6 +422,7 @@ def fit(self, X, y, sample_weight=None, **kwargs):
         # number of data samples
         n_samples = X.shape[0]
         sample_idxs = np.arange(n_samples)
+        prob = X[:, 2] / np.sum(X[:, 2])  # X: idx_sta, type, score, amp
 
         self.n_trials_ = 0
         max_trials = self.max_trials
@@ -432,7 +433,8 @@ def fit(self, X, y, sample_weight=None, **kwargs):
                 break
 
             # choose random sample set
-            subset_idxs = sample_without_replacement(n_samples, min_samples, random_state=random_state)
+            # subset_idxs = sample_without_replacement(n_samples, min_samples, random_state=random_state)
+            subset_idxs = np.random.choice(sample_idxs, min_samples, replace=False, p=prob)
             X_subset = X[subset_idxs]
             y_subset = y[subset_idxs]
 
diff --git a/adloc/sacloc2d.py b/adloc/sacloc2d.py
index 2c32d8c..49b58d9 100644
--- a/adloc/sacloc2d.py
+++ b/adloc/sacloc2d.py
@@ -124,8 +124,8 @@ def fit(self, X, y=None, event_index=0):
             yt = y[:, 0]
 
         opt = scipy.optimize.minimize(
-            # self.huber_loss_grad,
-            self.l2_loss_grad,
+            self.huber_loss_grad,
+            # self.l2_loss_grad,
             x0=self.events[event_index],
             method="L-BFGS-B",
             jac=True,
@@ -180,7 +180,7 @@ def predict(self, X, event_index=0):
 
     def score(self, X, y=None, event_index=0):
         """
-        X: data_frame with columns ["timestamp", "x_km", "y_km", "z_km", "type"]
+        X: idx_sta, type, score, amp
         """
         if len(y) <= 1:
             return -np.inf
@@ -193,13 +193,20 @@ def score(self, X, y=None, event_index=0):
             ya = y[:, 1]
             R2_t = 1 - np.sum((yt - tt) ** 2) / (np.sum((y[:, 0] - np.mean(y[:, 0])) ** 2) + 1e-6)
             R2_a = 1 - np.sum((ya - amp) ** 2) / (np.sum((y[:, 1] - np.mean(y[:, 1])) ** 2) + 1e-6)
+            # weight = X[:, 2]
+            # mu_yt = np.average(yt, weights=weight)
+            # mu_ya = np.average(ya, weights=weight)
+            # R2_t = 1 - np.sum(weight * (yt - tt) ** 2) / (np.sum(weight * (yt - mu_yt) ** 2) + 1e-6)
+            # R2_a = 1 - np.sum(weight * (ya - amp) ** 2) / (np.sum(weight * (ya - mu_ya) ** 2) + 1e-6)
             R2 = (R2_t + R2_a) / 2
         else:
             tt = output
             yt = y
             R2 = 1 - np.sum((yt - tt) ** 2) / (np.sum((y - np.mean(y)) ** 2) + 1e-6)
+            # weight = X[:, 2]
+            # mu_y = np.average(yt, weights=weight)
+            # R2 = 1 - np.sum(weight * (yt - tt) ** 2) / (np.sum(weight * (yt - mu_y) ** 2) + 1e-6)
 
-        # print(f"{R2=}")
         return R2
 
 
diff --git a/adloc/utils.py b/adloc/utils.py
index 0ec0647..40c9f69 100644
--- a/adloc/utils.py
+++ b/adloc/utils.py
@@ -14,18 +14,33 @@ def invert(picks, stations, config, estimator, event_index, event_init):
     """
 
     def is_model_valid(estimator, X, y):
-        check = True
-        check = check and (estimator.events[0][2] > 1.0)  # depth > 1.0 km
-        check = check and (estimator.score(X, y) > config["min_score"])
-        return check
+        """
+        X: idx_sta, type, score, amp
+        """
+        # n0 = np.sum(X[:, 1] == 0)  # P
+        # n1 = np.sum(X[:, 1] == 1)  # S
+        n0 = np.sum(X[X[:, 1] == 0, 2])
+        n1 = np.sum(X[X[:, 1] == 1, 2])
+        if not (n0 >= config["min_p_picks"] and n1 >= config["min_s_picks"]):
+            return False
+
+        if estimator.events[0][2] < 1.0:  # depth > 1.0 km
+            return False
+
+        if estimator.score(X, y) < config["min_score"]:
+            return False
+
+        return True
 
     def is_data_valid(X, y):
         """
-        X: idx_sta, type, score
+        X: idx_sta, type, score, amp
         y: t_s
         """
-        n0 = np.sum(X[:, 1] == 0)  # P
-        n1 = np.sum(X[:, 1] == 1)  # S
+        # n0 = np.sum(X[:, 1] == 0)  # P
+        # n1 = np.sum(X[:, 1] == 1)  # S
+        n0 = np.sum(X[X[:, 1] == 0, 2])
+        n1 = np.sum(X[X[:, 1] == 1, 2])
         return n0 >= config["min_p_picks"] and n1 >= config["min_s_picks"]  # At least min P and S picks
 
     MIN_PICKS = config["min_picks"]
@@ -192,7 +207,7 @@ def invert_location(picks, stations, config, estimator, events_init=None, iter=0
     if "ncpu" in config:
         NCPU = config["ncpu"]
     else:
-        NCPU = min(32, mp.cpu_count() - 1)
+        NCPU = min(64, mp.cpu_count() * 2 - 1)
 
     jobs = []
     events_inverted = []